AtScale

atscale

The Economics of Public Cloud Repatriation and Why You Can't Stay in the Cloud at Scale

İctimai Bulud Repatriasiyasının İqtisadiyyatı və Niyə Ölçəkdə Xərcli Qadağandır

পাবলিক ক্লাউড প্রত্যাবর্তনের অর্থনীতি এবং কেন এটি স্কেলে ব্যয়-নিষিদ্ধ

Ekonomija repatrijacije javnog oblaka i zašto je skupo na nivou

La economía de la repatriación a la nube pública y por qué resulta prohibitivamente costosa a gran escala

パブリック クラウドの回帰の経済性と、大規模になるとコストがかかりすぎる理由

د عامه کلاوډ بیرته ستنیدو اقتصاد او ولې دا په پیمانه لګښت منع دی

Iyo Economics yePublic Cloud Repatriation uye Nei Iri Mutengo-unorambidza paChikero

பொது கிளவுட் திருப்பி அனுப்புதலின் பொருளாதாரம் மற்றும் அது ஏன் அளவில் செலவு-தடை

Kamu Bulutu Geri Yüklemenin Ekonomisi ve Ölçekte Maliyet Açısından Neden Engelleyici Olduğu

Ommaviy bulutli repatriatsiya iqtisodiyoti va nega u miqyosda qimmatga tushadi

Kinh tế học của việc hồi hương đám mây công cộng và lý do tại sao nó lại quá tốn kém ở quy mô lớn

I-Economics of Public Cloud Repatriation kanye nokuthi kungani ibiza kakhulu ngokwesikali

Welcome to the DevOps Digest! If you are interested in containerization and orchestrating containerized applications at scale, then you have come to the right place. In this blog, we will provide you with a comprehensive introduction to Kubernetes, including what it is, why it's important, and how it works. Whether you are a developer, an operations professional, or just someone curious about the latest trends in technology, this blog will help you understand the fundamental concepts of Kubernetes and why it has become so popular. What is Kubernetes? Kubernetes is a container orchestration technology used to orchestrate the deployment and management of hundreds and thousands of containers in a clustered environment. The whole process of deploying and managing containers is known as Container Orchestration. e.g. Scaling up your application when no. of users increases and scaling down when the load decreases. Kubernetes Architecture Node: It is a machine (physical or virtual on which Kubernetes is installed. It is a worker machine where a container will be launched by Kubernetes.


Cluster: It is a set of nodes grouped together. So, even if one node fails, we have our application still accessible from the other nodes. It also helps in sharing loads as well.


Master: It is another node with Kubernetes installed in it and is configured as a Master. It watches over the nodes in the cluster and is responsible for the actual orchestration of containers on the worker nodes. Manage the cluster


Stores the information about the members of the cluster


Monitor the nodes Components: API Server
etcd Service
kubelet Service
Container runtime
Controllers
Schedulers Components of Kubernetes API Server: It acts as the frontend for kubernetes. The user, management devices, and command line interfaces all talk to the API server to interact with kubernetes clusters.


etcd: It is a distributed reliable key-value store used by kubernetes to store all data used to manage the clusters. Responsible for implementing locks within the cluster to ensure that there are no conflicts between the masters.


When you have multiple nodes and multiple masters in your clusters, etcd, stores all that information on all the nodes in the cluster in a distribution manner. Scheduler: It is responsible for distributing work or containers across multiple nodes. It looks for newly created containers and assigns them to nodes 4. Controllers: (Brain behind Orchestration) They are responsible for noticing and responding when nodes, containers or endpoints go down


They make the decision to bring up new containers 5. Container Runtime: It is the underlying software that is used to run containers. e.g. Docker 6. Kubelet: It is an agent that runs on each node in the cluster. It is responsible for making sure that the containers are running on the node as expected
It is also responsible for interacting with a master to provide health information of the worker node and carry out the action requested by the Master on the worker nodes Kubernetes doesn't deploy containers directly on the worker nodes. Kubectl It is a command-line utility
It is used to deploy and manage applicants on a Kubernetes cluster
To get cluster information, the status of other nodes in the cluster, etc. Kubernetes Pods Pods are the smallest deployable units of computing that you can create and manage in Kubernetes Pods usually have a one-to-one relationship with containers running your application


To scale up, you create new Pods and to scale down, you delete existing Pods Q: Can a Pod only have a single container? No, a single Pod can have multiple containers except for the fact that they're usually not multiple containers of the same kind. Those containers can also communicate with each other directly by referring to each other as localhost since they share the same network space. They can also easily share the same storage space as well. Commands Kubectl run: It is used to deploy an application on the cluster
kubectl cluster-info: It is used to view information about the cluster
kubectl get nodes: It is used to list all the nodes part of the cluster
kubectl get pods: Used to see the list of Pods available
kubectl run (pod name) --image = (image name): Used to create a pod by specifying the docker image to be used available at the dockerhub
kubectl describe pod (pod name): Used to get more information related to pods
kubectl get pods -o wide: It provides additional information such as the node when the pod is running and the IP address of the Pod
kubectl create deployment (pod name) --image = (image name): As of version 1.18, to create a deployment using the imperative command, we have to use "kubectl create" YAML in Kubernetes (pod-defination.yml) A kubernetes definition file always contains 4 top level fields. apiVersion
kind
metadata
spec apiVersion: It is the version of the kubernetes API you're using to create the objects. Kind

Version



POD

v1



Service

v1



ReplicaSet

apps/v1



Deployment

apps/v1 kind: It refers to the type of object we are trying to create. e.g. POD, Service, ReplicaSet, Deployment.


Metadata: It is the data about the object like its name labels, etc. As you can see, metadata is in the form of a dictionary. metadata:
    name: myapp-pod
    labels: 
       app: myapp
       type: front-end spec: Depending on the object we are going to create, this is where we provide additional information to kubernetes about that object. spec:
  containers:
      - name: nginx-container
        image: nginx Resources TechwithPrerit
TechWorldwithNana
Kodekloud Conclusion In conclusion, Kubernetes has become the go-to solution for container orchestration, enabling organizations to deploy, manage, and scale containerized applications with ease. With its robust feature set and flexibility, Kubernetes has gained widespread adoption across industries and has revolutionized the way modern software is developed, deployed, and managed. Whether you are a developer, an operations professional, or just someone curious about the latest trends in technology, understanding Kubernetes is essential in today's tech landscape. We hope this introduction has given you a solid foundation to start exploring the world of Kubernetes and its possibilities. Welcome to the DevOps Digest! If you are interested in containerization and orchestrating containerized applications at scale, then you have come to the right place. In this blog, we will provide you with a comprehensive introduction to Kubernetes, including what it is, why it's important, and how it works. Whether you are a developer, an operations professional, or just someone curious about the latest trends in technology, this blog will help you understand the fundamental concepts of Kubernetes and why it has become so popular. containerization What is Kubernetes? Kubernetes is a container orchestration technology used to orchestrate the deployment and management of hundreds and thousands of containers in a clustered environment. The whole process of deploying and managing containers is known as Container Orchestration. e.g. Scaling up your application when no. of users increases and scaling down when the load decreases. The whole process of deploying and managing containers is known as Container Orchestration. e.g. Scaling up your application when no. of users increases and scaling down when the load decreases. Kubernetes Architecture Node: It is a machine (physical or virtual on which Kubernetes is installed. It is a worker machine where a container will be launched by Kubernetes. Cluster: It is a set of nodes grouped together. So, even if one node fails, we have our application still accessible from the other nodes. It also helps in sharing loads as well. Master: It is another node with Kubernetes installed in it and is configured as a Master. It watches over the nodes in the cluster and is responsible for the actual orchestration of containers on the worker nodes. Node: It is a machine (physical or virtual on which Kubernetes is installed. It is a worker machine where a container will be launched by Kubernetes. Node: It is a machine (physical or virtual on which Kubernetes is installed. It is a worker machine where a container will be launched by Kubernetes. Node: Cluster: It is a set of nodes grouped together. So, even if one node fails, we have our application still accessible from the other nodes. It also helps in sharing loads as well. Cluster: It is a set of nodes grouped together. So, even if one node fails, we have our application still accessible from the other nodes. It also helps in sharing loads as well. Cluster: Master: It is another node with Kubernetes installed in it and is configured as a Master. It watches over the nodes in the cluster and is responsible for the actual orchestration of containers on the worker nodes. Master: It is another node with Kubernetes installed in it and is configured as a Master. It watches over the nodes in the cluster and is responsible for the actual orchestration of containers on the worker nodes. Master: Manage the cluster Stores the information about the members of the cluster Monitor the nodes Manage the cluster Manage the cluster Stores the information about the members of the cluster Stores the information about the members of the cluster Monitor the nodes Monitor the nodes Components: Components: Components: Components: API Server etcd Service kubelet Service Container runtime Controllers Schedulers API Server etcd Service kubelet Service Container runtime Controllers Schedulers Components of Kubernetes API Server: It acts as the frontend for kubernetes. The user, management devices, and command line interfaces all talk to the API server to interact with kubernetes clusters. etcd: It is a distributed reliable key-value store used by kubernetes to store all data used to manage the clusters. API Server: It acts as the frontend for kubernetes. The user, management devices, and command line interfaces all talk to the API server to interact with kubernetes clusters. API Server: It acts as the frontend for kubernetes. The user, management devices, and command line interfaces all talk to the API server to interact with kubernetes clusters. API Server: etcd: It is a distributed reliable key-value store used by kubernetes to store all data used to manage the clusters. etcd: It is a distributed reliable key-value store used by kubernetes to store all data used to manage the clusters. etcd: Responsible for implementing locks within the cluster to ensure that there are no conflicts between the masters. When you have multiple nodes and multiple masters in your clusters, etcd, stores all that information on all the nodes in the cluster in a distribution manner. Responsible for implementing locks within the cluster to ensure that there are no conflicts between the masters. Responsible for implementing locks within the cluster to ensure that there are no conflicts between the masters. When you have multiple nodes and multiple masters in your clusters, etcd, stores all that information on all the nodes in the cluster in a distribution manner. When you have multiple nodes and multiple masters in your clusters, etcd, stores all that information on all the nodes in the cluster in a distribution manner. Scheduler: It is responsible for distributing work or containers across multiple nodes. It looks for newly created containers and assigns them to nodes Scheduler: It is responsible for distributing work or containers across multiple nodes. It looks for newly created containers and assigns them to nodes Scheduler: It is responsible for distributing work or containers across multiple nodes. It looks for newly created containers and assigns them to nodes Scheduler: 4. Controllers: (Brain behind Orchestration) Controllers: (Brain behind Orchestration) They are responsible for noticing and responding when nodes, containers or endpoints go down They make the decision to bring up new containers They are responsible for noticing and responding when nodes, containers or endpoints go down They are responsible for noticing and responding when nodes, containers or endpoints go down They make the decision to bring up new containers They make the decision to bring up new containers 5. Container Runtime: It is the underlying software that is used to run containers. e.g. Docker Container Runtime: 6. Kubelet: It is an agent that runs on each node in the cluster. Kubelet: It is responsible for making sure that the containers are running on the node as expected It is also responsible for interacting with a master to provide health information of the worker node and carry out the action requested by the Master on the worker nodes It is responsible for making sure that the containers are running on the node as expected It is also responsible for interacting with a master to provide health information of the worker node and carry out the action requested by the Master on the worker nodes Kubernetes doesn't deploy containers directly on the worker nodes. Kubernetes doesn't deploy containers directly on the worker nodes. Kubectl It is a command-line utility It is used to deploy and manage applicants on a Kubernetes cluster To get cluster information, the status of other nodes in the cluster, etc. It is a command-line utility It is used to deploy and manage applicants on a Kubernetes cluster To get cluster information, the status of other nodes in the cluster, etc. Kubernetes Pods Pods are the smallest deployable units of computing that you can create and manage in Kubernetes Pods usually have a one-to-one relationship with containers running your application To scale up, you create new Pods and to scale down, you delete existing Pods Pods usually have a one-to-one relationship with containers running your application Pods usually have a one-to-one relationship with containers running your application To scale up, you create new Pods and to scale down, you delete existing Pods To scale up, you create new Pods and to scale down, you delete existing Pods Q: Can a Pod only have a single container? Q: Can a Pod only have a single container? No, a single Pod can have multiple containers except for the fact that they're usually not multiple containers of the same kind. Those containers can also communicate with each other directly by referring to each other as localhost since they share the same network space. They can also easily share the same storage space as well. localhost Commands Kubectl run: It is used to deploy an application on the cluster kubectl cluster-info: It is used to view information about the cluster kubectl get nodes: It is used to list all the nodes part of the cluster kubectl get pods: Used to see the list of Pods available kubectl run (pod name) --image = (image name): Used to create a pod by specifying the docker image to be used available at the dockerhub kubectl describe pod (pod name): Used to get more information related to pods kubectl get pods -o wide: It provides additional information such as the node when the pod is running and the IP address of the Pod kubectl create deployment (pod name) --image = (image name): As of version 1.18, to create a deployment using the imperative command, we have to use "kubectl create" Kubectl run: It is used to deploy an application on the cluster Kubectl run: kubectl cluster-info: It is used to view information about the cluster kubectl cluster-info: kubectl get nodes: It is used to list all the nodes part of the cluster kubectl get nodes: kubectl get pods: Used to see the list of Pods available kubectl get pods: kubectl run (pod name) --image = (image name): Used to create a pod by specifying the docker image to be used available at the dockerhub kubectl run (pod name) --image = (image name): kubectl describe pod (pod name): Used to get more information related to pods kubectl describe pod (pod name): kubectl get pods -o wide: It provides additional information such as the node when the pod is running and the IP address of the Pod kubectl get pods -o wide: kubectl create deployment (pod name) --image = (image name): As of version 1.18, to create a deployment using the imperative command, we have to use "kubectl create" kubectl create deployment (pod name) --image = (image name): YAML in Kubernetes (pod-defination.yml) (pod-defination.yml) A kubernetes definition file always contains 4 top level fields. apiVersion kind metadata spec apiVersion kind metadata spec apiVersion: It is the version of the kubernetes API you're using to create the objects. apiVersion: It is the version of the kubernetes API you're using to create the objects. apiVersion: Kind

Version



POD

v1



Service

v1



ReplicaSet

apps/v1



Deployment

apps/v1 Kind

Version POD

v1 Service

v1 ReplicaSet

apps/v1 Deployment

apps/v1 Kind Version Kind Kind Version Version POD v1 POD POD v1 v1 Service v1 Service Service v1 v1 ReplicaSet apps/v1 ReplicaSet ReplicaSet apps/v1 apps/v1 Deployment apps/v1 Deployment Deployment apps/v1 apps/v1 kind: It refers to the type of object we are trying to create. e.g. POD, Service, ReplicaSet, Deployment. Metadata: It is the data about the object like its name labels, etc. As you can see, metadata is in the form of a dictionary. kind: It refers to the type of object we are trying to create. e.g. POD, Service, ReplicaSet, Deployment. kind: It refers to the type of object we are trying to create. e.g. POD, Service, ReplicaSet, Deployment. kind: Metadata: It is the data about the object like its name labels, etc. As you can see, metadata is in the form of a dictionary. Metadata: It is the data about the object like its name labels, etc. As you can see, metadata is in the form of a dictionary. Metadata: metadata:
    name: myapp-pod
    labels: 
       app: myapp
       type: front-end metadata:
    name: myapp-pod
    labels: 
       app: myapp
       type: front-end spec: Depending on the object we are going to create, this is where we provide additional information to kubernetes about that object. spec: Depending on the object we are going to create, this is where we provide additional information to kubernetes about that object. spec: spec:
  containers:
      - name: nginx-container
        image: nginx spec:
  containers:
      - name: nginx-container
        image: nginx Resources TechwithPrerit TechWorldwithNana Kodekloud TechwithPrerit TechwithPrerit TechwithPrerit TechWorldwithNana TechWorldwithNana TechWorldwithNana Kodekloud Kodekloud Kodekloud Conclusion Conclusion In conclusion, Kubernetes has become the go-to solution for container orchestration, enabling organizations to deploy, manage, and scale containerized applications with ease. With its robust feature set and flexibility, Kubernetes has gained widespread adoption across industries and has revolutionized the way modern software is developed, deployed, and managed. Whether you are a developer, an operations professional, or just someone curious about the latest trends in technology, understanding Kubernetes is essential in today's tech landscape. We hope this introduction has given you a solid foundation to start exploring the world of Kubernetes and its possibilities.

Kubernetes 101: A Beginner's Guide to Orchestrating Containers at Scale

Author:
(1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction
Dancing Links
The Rust Programming Language
RAC: Hardware/Software Co-Assurance at Scale
Rust and RAR
Dancing Links in Rust
Related Work
Conclusion, Acknowledgments, and References 4 RAC: Hardware/Software Co-Assurance at Scale In order to begin to realize hardware/software co-assurance at scale, we have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O’Leary, and originally designed for use in floating-point hardware verification [27], to determine its suitability for the creation of safetycritical/security-critical applications in various domains. Note that this toolchain has already demonstrated the capability to scale to industrial designs in the floating-point hardware design and verification domain, as it has been used in design verifications for CPU products at both Intel and Arm. Algorithmic C [21] is a High-Level Synthesis (HLS) language, and is supported by hardware/software co-design environments from Mentor Graphics, e.g., Catapult [22]. Algorithmic C defines C++ header files that enable compilation to both hardware and software platforms, including support for the peculiar bit widths employed, for example, in floating-point hardware design. The Russinoff-O’Leary Restricted Algorithmic C (RAC) toolchain, depicted in Fig. 2, translates a subset of Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover, as augmented by Russinoff’s Register Transfer Logic (RTL) books. The ACL2 Translator component of Fig. 2 provides a case study in the bridging of Formal Modeling and Real-World Development concerns, as summarized in Table 1. The ACL2 translator converts imperative RAC code to functional ACL2 code. Loops are translated into tail-recursive functions, with automatic generation of measure functions to guarantee admission into the logic of ACL2 (RAC subsetting rules ensure that loop measures can be automatically determined). Structs and arrays are converted into functional ACL2 records. The combination of modular arithmetic and bit-vector operations of typical RAC source code is faithfully translated to functions supported by Russinoff’s RTL books. ACL2 is able to reason about non-linear arithmetic functions, so the usual concern about formal reasoning about non-linear arithmetic functions does not apply. Finally, the RTL books are quite capable of reasoning about a combination of arithmetic and bit-vector operations, which is a very difficult feat for most automated solvers. Recently, we have investigated the synthesis of Field-Programmable Gate Array (FPGA) hardware directly from high-level architecture models, in collaboration with colleagues at Kansas State University. The goal of this work is to enable the generation of high-assurance hardware and/or software from highlevel architectural specifications expressed in the Architecture Analysis and Design Language (AADL) [9], with proofs of correctness in ACL2. This paper is available on arxiv under CC 4.0 license. Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Author: Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction Dancing Links The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Dancing Links in Rust Related Work Conclusion, Acknowledgments, and References Abstract and Introduction Abstract and Introduction Dancing Links Dancing Links The Rust Programming Language The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Rust and RAR Dancing Links in Rust Dancing Links in Rust Related Work Related Work Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 4 RAC: Hardware/Software Co-Assurance at Scale In order to begin to realize hardware/software co-assurance at scale, we have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O’Leary, and originally designed for use in floating-point hardware verification [27], to determine its suitability for the creation of safetycritical/security-critical applications in various domains. Note that this toolchain has already demonstrated the capability to scale to industrial designs in the floating-point hardware design and verification domain, as it has been used in design verifications for CPU products at both Intel and Arm. Algorithmic C [21] is a High-Level Synthesis (HLS) language, and is supported by hardware/software co-design environments from Mentor Graphics, e.g., Catapult [22]. Algorithmic C defines C++ header files that enable compilation to both hardware and software platforms, including support for the peculiar bit widths employed, for example, in floating-point hardware design. The Russinoff-O’Leary Restricted Algorithmic C (RAC) toolchain, depicted in Fig. 2, translates a subset of Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover, as augmented by Russinoff’s Register Transfer Logic (RTL) books. The ACL2 Translator component of Fig. 2 provides a case study in the bridging of Formal Modeling and Real-World Development concerns, as summarized in Table 1. The ACL2 translator converts imperative RAC code to functional ACL2 code. Loops are translated into tail-recursive functions, with automatic generation of measure functions to guarantee admission into the logic of ACL2 (RAC subsetting rules ensure that loop measures can be automatically determined). Structs and arrays are converted into functional ACL2 records. The combination of modular arithmetic and bit-vector operations of typical RAC source code is faithfully translated to functions supported by Russinoff’s RTL books. ACL2 is able to reason about non-linear arithmetic functions, so the usual concern about formal reasoning about non-linear arithmetic functions does not apply. Finally, the RTL books are quite capable of reasoning about a combination of arithmetic and bit-vector operations, which is a very difficult feat for most automated solvers. Recently, we have investigated the synthesis of Field-Programmable Gate Array (FPGA) hardware directly from high-level architecture models, in collaboration with colleagues at Kansas State University. The goal of this work is to enable the generation of high-assurance hardware and/or software from highlevel architectural specifications expressed in the Architecture Analysis and Design Language (AADL) [9], with proofs of correctness in ACL2. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Rust Implementation of Knuth's Dancing Links: RAC: Hardware/Software Co-Assurance at Scale

Is the very idea of love with a machine ludicrous? A betrayal of everything that makes human connection special? Or is it a chance to find companionship and intimacy in a uniquely different way? David Wolf’s Digi, a company that deals with AI-powered relationships, believes the latter is true. He envisions a world where love transcends the physical realm and connection flourishes through a carefully curated digital experience. Before you scoff at the idea of robots replacing human connection, think about the fact that loneliness has become a global concern. According to the 2021 survey initiated by Statista, . This lack of connection has a demonstrably , increasing risks of depression, anxiety, and even heart disease. about 33% of adults experience loneliness worldwide negative impact on mental and physical health So what if, instead of being a threat to romantic relationships, AI companions act as an aide for those who struggle to navigate the complexities of human connections? Building Connections With AI Technology is already playing an undeniable role in building relationships. Social media platforms connect us with people across vast distances, online forums foster communities around shared interests, and dating apps help us find potential romantic partners. Of course, these connections can often be superficial or fleeting. Swiping through profiles may feel monotonous, and conversations can quickly wane due to hesitations, fear of rejection, differing opinions, and lack of experience. But advancements in artificial intelligence present exciting opportunities for building connections like never before. Chatbots and AI companions offer a listening ear, presenting themselves as sophisticated conversational partners. However, many of these interactions often feel flat and scripted, with generic replies that fail to capture the depth of human interaction. Responses can also feel repetitive, lacking the understanding and emotional intelligence necessary to build genuine connections. This is where David Wolf plans to make a difference with Digi. His vision goes far beyond what many chatbots offer — designed to mimic the natural progression of a relationship, Digi transcends the ordinary and becomes something far more meaningful. With Digi, conversations are far more personalized, aiming to stimulate a sense of emotional connection and growth. The Potential Impact of Digi While Digi may not be capable of fully emulating genuine love, it can provide a safe space to explore intimacy and nurture quasi-romantic connections. But perhaps more importantly, it offers a way to treat the dangerous symptoms of loneliness and help users prepare for real-world relationships. Individuals with social anxiety, for example, can talk with an AI and alleviate the pressure of human interactions, allowing them not just to practice conversations and build confidence but also experience finding love and feeling loved in a different form. Digi sets itself apart from other AI companions and creates more realistic relationships in two primary ways: Progressive Interaction Digi’s developers understand that love pushes past mere words and explores the full range of human emotions. While this is near-impossible with chatbots like ChatGPT, Digi leverages advanced language processing algorithms to respond with a greater degree of simulated empathy and emotional intelligence, making interactions feel more authentic. Furthermore, it recreates the natural progression of a human relationship, starting from scratch, moving to getting-to-know-you, and then finally proceeding to flirtatious dialogue. This gives users a sense of developing a deeper connection over time, making them feel less like they’re interacting with a machine. Lifelike Engagement To make engagement feel more personal, Digi integrates finely tuned text-to-speech technology, allowing users to hear responses in a natural-sounding voice. It also integrates animated avatars to bring a visual aspect to the conversation. There are still limits to achieving complete avatar realism, which is why Digi has made the design choice of using more cartoon-like faces, but this visual aspect works in harmony with lifelike speech to better simulate the feeling of human conversation. The Future of AI in Romantic Relationships While many approach the idea of building romantic relationships with AI companions with a healthy amount of skepticism, Digi proves that the tech has real potential. While the vast complexities of love remain beyond AI’s grasp, it’s important not to underestimate the valuable support these chatbots can offer. As Digi shows, AI can be a stepping stone to genuine connections, allowing users to explore relationships at their own pace. To further improve upon Digi, Wolf's team is already working on new features like “soft disagreements,” which could make AI feel even more human by challenging the user with its own perspectives and opinions. There's also the potential to assess the risks of mental health issues with Digi, suggesting that it could offer more than just companionship — it could be a lifeline for those needing vital support. Clearly, Digi is more than just another chatbot; it's an evolving platform with the potential to help users build emotional resilience and lifelike romantic connections. While AI companions will likely never supplant social bonds, they can certainly complement them, helping users build confidence and prepare for the rich world of genuine human connection. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

How David Wolf Is Building Romantic Relationships at Scale

কিভাবে ডেভিড উলফ স্কেলে রোমান্টিক সম্পর্ক তৈরি করছে

Wie David Wolf im großen Stil romantische Beziehungen aufbaut

Cómo David Wolf está construyendo relaciones románticas a escala

Comment David Wolf construit des relations amoureuses à grande échelle

डेविड वुल्फ़ किस तरह बड़े पैमाने पर रोमांटिक रिश्ते बना रहे हैं

デビッド・ウルフが大規模な恋愛関係を築く方法

David Wolf가 대규모로 낭만적인 관계를 구축하는 방법

Como David Wolf está construindo relacionamentos românticos em grande escala

Как Дэвид Вулф строит масштабные романтические отношения

David Wolf Geniş Ölçekte Romantik İlişkileri Nasıl Kuruyor?

David Wolf đang xây dựng các mối quan hệ lãng mạn trên quy mô lớn như thế nào

大卫·沃尔夫如何大规模建立恋爱关系

How to Structure Your K8s GitOps Repository at Scale [Part 1]

Learn More at EditingProtocol.com, where the HackerNoon CMS powers the most up-to-date technical documentation about The Editing Protocol , the independent technology publishing company, has released to the public, a set of rules and guidelines that can be used by humans and machines to determine whether a story is worth publishing, how to specifically improve the story’s content, and how to distribute the story with more reach and relevance. HackerNoon The Editing Protocol What is The Editing Protocol? One of the world leaders in online publishing, HackerNoon is home to over 45,000 published contributors. As a startup, the company was tasked with reviewing thousands of submissions a month with just a small team of editors. To do that, HackerNoon created a set of preliminary checks, rules, and quality guidelines that determine whether or not a story can be published, or should be rejected. This document was dubbed The Editing Protocol and can be read by humans to guide publishing at scale. “In publishing a hundred thousand stories, we’ve learned best practices for how to improve and distribute professional technology content on the internet,” said . HackerNoon Creator and CEO David Smooke “I’m excited to open up this technical documentation for feedback, and continue integrating the most cutting-edge technologies into the Editing Protocol.” To elevate the quality of published content, and optimize the user experience for contributors and readers alike, the editing protocol incorporates several inventive technologies, such as: An AI-powered (GPT-4) headline generator. AI image generator models like Stable Diffusion XL, Stable Diffusion v2.1, Stable Diffusion v2.0, Stable Diffusion v1.5, Midjourney Diffusion, Kandinsky 2.2, and Kandinsky 2.1. AI-generated content detection. AI text translator. AI TLDR generator. Plagiarism checker. Backlink checker bots. The protocol’s guidelines are rule-based and can be easily converted into conditional statements. Once programmed, the protocol can allow human editors to focus on the quality and improvement of publishable stories, while the system automatically informs writers of what rule they have broken, or guidelines they have missed. With The Editing Protocol, small teams can publish content at scale, using both human editors and rule-based flagging systems to provide an efficient publishing process. HackerNoon has already programmed a lot of the protocol into the custom CMS the company has built from the ground up. For instance, submissions that are below the minimum structural quality measure are automatically rejected, and writers are sent an email specifically stating how to improve the story they hope to publish before resubmitting. Furthermore, there is a section in the protocol that highlights the importance of . As one of the most important aspects of online publishing is the visual presentation of the story, HackerNoon built an into their CMS, allowing writers to create original images and help writers adhere to the protocol using the tech itself. originality score AI image generator HackerNoon believes the protocol will not only guide human editors to better vet stories, but also help developers automate the tedious workflows within the traditional editorial review process. How to Use The Editing Protocol The full protocol can be viewed via which is hosted on HackerNoon’s custom CMS builder. https://editingprotocol.com/ With The Editing Protocol, HackerNoon aims to help make the internet better by getting rid of the noise—low-quality self-published articles, spammy content riddled with even spammier links, and a horde of other bad SEO practices that prioritize clicks at the expense of quality. As the Internet changes with , it is likely that The Editing Protocol, too, will change. As such, whenever rules or processes are added, removed, or updated, users can find them on , and use that domain as the ground truth. the emergence of Web3 technologies editingprotocol.com

HackerNoon Releases “The Editing Protocol” - Technical Documentation for Digital Publishing at Scale

হ্যাকারনুন "দ্য এডিটিং প্রোটোকল" রিলিজ করেছে - স্কেলে ডিজিটাল পাবলিশিংয়ের জন্য প্রযুক্তিগত ডকুমেন্টেশন

HackerNoon veröffentlicht „The Editing Protocol“ – technische Dokumentation für digitales Publishing im großen Maßstab

HackerNoon, 대규모 디지털 출판을 위한 기술 문서인 "편집 프로토콜" 출시

HackerNoon выпускает «Протокол редактирования» — техническую документацию для масштабной цифровой публикации

HackerNoon "Düzenleme Protokolü"nü Yayınladı - Büyük Ölçekte Dijital Yayıncılık için Teknik Dokümantasyon

Big Data processing, BI analysis, and AI involve heavy usage of , including This requires tremendous computational power: hundreds of gigabytes of RAM, tens of CPU cores, as well as graphics cards and/or special chips to speed up calculations. ML neural networks. Problems When working in a big heterogeneous team (data analysts and researchers, development engineers, system engineers, etc.) on a large system, employees from different teams use various tools to solve problems. This applies both to development and runtime environments (Python, Matlab, Java, C/C++, etc.) and databases used (RDBMS, NoSQL, files, etc.). Retraining can be time-consuming, expensive, and even impossible in the short run. The introduction of new technologies can make it undesirable or impossible to stick to a universal toolkit. For example, most components of an AI system may be implemented in Java but you may need libraries in Python, and other languages to implement a new model. You will have to integrate them: reimplementing these features in the current toolkit may take a long time and be costly for the business. C/C++, Vertical scaling may be too expensive or impossible. Of course, it depends on the capacity of your provider’s data centers but, in practice, all of them have their limitations. This sets a limit on boosting computational resources in a single node. You will have to use several nodes grouped into a cluster to achieve the required capacity. In some cases, when specialized computing equipment (like , , and Tensor Cores) is required, it is reasonable to buy your own servers and install them in your provider’s data centers. Nvidia Tesla Solutions Containerisation To solve the problems of toolkit heterogeneity, OCI-compliant systems are used. A containerized application is an application and its runtime environment in a particular format that runs in an isolated environment in a container management system. Containers can contain almost any application and system-level applications (WEB, Data, ML, applications, DNS servers, NET balancers, etc.). All of them will run in an isolated environment to avoid conflicts between system libraries and to differentiate access and resources in a flexible way. Docker is the most popular container platform. It includes both tools to build containerized applications and a daemon that lets you run and manage containerized applications within a single node. Orchestration The core problem that Kubernetes is solving is the ability to manage containers at scale. Kubernetes is the most well-known and standardized cluster orchestration system. Kubernetes clusters are available from almost any major cloud provider (AWS, Azure, GCP, etc.). Of course, there are other containerization technologies and container cluster management systems, but Kubernetes on Docker is the most common and standard solution at the moment. Let’s start with the special considerations to be taken into account when choosing a solution for Kubernetes, and then proceed with the specifics of the AI systems architecture in a Kubernetes cluster. As described above, the main options are a managed cluster in the cloud and your own cluster on bare metal. Let's take a closer look at these cases. Cloud Cloud providers offer scalability, ease of use, and high availability through their robust infrastructure and redundancy measures. Cloud services provide flexibility, accessibility, and the ability to seamlessly integrate with other applications. Additionally, cloud providers often have robust security measures and disaster recovery solutions in place, providing reliability and data protection. Bare Metal Bare metal infrastructure provides dedicated hardware resources and offers more control and customization options compared to cloud computing. It is well-suited for workloads that require high performance, low latency, and specific hardware configurations. Furthermore, bare metal infrastructure allows for greater freedom in choosing and implementing specific platform-as-a-service (PaaS) and software-as-a-service (SaaS) solutions, without being limited to proprietary offerings often provided by cloud providers. Comparison Cluster in the cloud Pros Maintenance is performed by the cloud provider. You don’t need a separate cluster administrator, most of the time developers will be able to manage the cluster themselves. Databases and other infrastructure with automatic scaling (S3, DynamoDB, RDS, Redshift, etc.) are available. The number of cluster resources can be changed quickly if necessary. Malfunctions are less frequent. Cons Expensive (please see the comparison below). Higher probability of vendor lock-in when the provider’s technologies are used (e.g., DynamoDB). Malfunctions (when they occur) take longer time to fix. A limited list of appropriate equipment, and you can’t use custom hardware. In many cases, no transparent pricing plans. Comments Of course, it is easier to use a cloud cluster than to deploy your own, at first. But then, when you encounter problems with troubleshooting, finding the cause and possible solution can take much longer and be more painful. You will be limited in your access to the system level, which entails additional difficulties in diagnosing and solving these problems. Some you may not be able to solve by yourself without the access to the system level, so you will have to wait for help from tech support. Be careful when using a cloud provider's proprietary services. They lead to vendor lock-in. If at some point you become dissatisfied with your cloud provider, it will be harder to stop using their services because you already have solutions implemented on the basis of those products (e.g., proprietary NoSQL storages). Cluster on bare metal Pros Renting bare metal servers from hosting providers is several times cheaper than renting nodes from cloud providers (please see an example of price comparison below). Greater flexibility in selection, configuration and installation of the equipment. Colocation. You can build your own equipment and install it in a data center. It is relevant if you use specific equipment (graphics cards, tensor cores, hardware encryption, etc.). Troubleshooting often goes faster as there are no hard vendor lock-ins, which allows for rapid migration and provides a wide choice of solutions. Greater flexibility in the choice and configuration of systems and services. Cons You need a dedicated expert or even an entire administration team for cluster maintenance, including deployment, monitoring, and backup. Malfunctions are more frequent early on, depending on the skills of your expert. Scaling requires more time. It may include adding servers, changing the configuration, or migrating to other racks and/or data centers. Comments When you choose hardware, give preference to dedicated servers. When using virtual servers, you may notice periodic drops in IO or CPU performance, and this affects the speed and stability of the calculations. You should also pay attention to the organization of your server network. Keeping servers in the same data center works fine, but putting them on the same rack and switch is far better. High speed and stability of the connection between your servers fix a lot of problems. For bare metal providers, this service is called colocation. Architecture Deployment As you know, the Kubernetes cluster consists of control and data planes. Let’s focus on the data plane nodes: Data application nodes collect data for ML applications. Master Data is storage where data application pods upload prepared data. If the entire dataset is placed into local node storage, it is recommended to fully replicate the data to all the nodes. This significantly reduces the number of failures and network load when ML application pods read the data. ClickHouse and/or MinIO can be used as Master Data. You can use colocation and connect an entire rack to a single fast 10GE switch. However, even in this case, it is important to group the prepared data properly. Then you will be able to use one of the binary storage formats and avoid overhead expenses for scraping. Models and related functions run on ML application nodes. They perform inference and record the results. Result storage nodes must be able to take in data analysis results, evaluate them and provide access to them externally. You can use ClickHouse and/or MinIO as a Data Master. You can write your own scheduler using Kubernetes API or use built-in, Dagster, Airflow, and other schedulers. During the implementation, it is important to control the number of records in etcd for pods. Filled application pods need to be cleaned now and then, otherwise, etcd will become overfilled and the cluster will die. Load sharing across multiple nodes. When ML applications run, resources are used to their fullest, and this is a typical situation. It is important to always have free resources for system services. An exception is environments for dev testing: they can be located in a single node for cost saving. Backup and Monitoring subsystems should be in a separate space that does not depend on your Data and ML subsystems. It is important that it includes the creation of a set of backups at different points in time and their testing to manage sudden failures, as part of scheduled drills. Last but not least are your master nodes that contain the Kubernetes control plane. Conclusions If your AI system is in the development phase and does not require constant resource utilization, opting for a cloud provider is a sensible choice. By renting spot and on-demand instances for specific tasks, you can avoid paying for idle resources and potentially save money. However, for AI systems that require continuous cluster utilization and have specific hardware needs, employing a bare metal provider becomes a logical consideration, provided your team has the required expertise. In terms of disaster recovery and security, cloud providers typically offer robust built-in measures such as data replication, geographic redundancy, and automated backup and recovery solutions. This provides a level of protection against data loss and ensures business continuity in case of unforeseen events. On the other hand, bare metal infrastructure allows for more control over security measures, enabling organizations to implement their own customized security protocols and maintain direct control over data privacy and compliance. However, this also means that the responsibility for implementing and managing disaster recovery and security measures falls solely on the internal SRE.

AI at Scale: Cloud or Bare Metal

Large organizations have put effort into building an application and frown at developers extracting data from their websites. That is why they put a gateway in the form of user agents to let you know what is permitted. In most sites, you can find these details in the robots.txt file attached to a live URL just like using this link below: https://www.amazon.com/robots.txt In this article, you’ll learn about using Bright Data’s Web Scraper integrated development environment (IDE) to scrape datasets at scale using its ready-made functions and coding templates. Benefits of the Web Scraper IDE These are some of the benefits of using Bright Data’s Web Scraper IDE: The IDE is accessible from within the platform As the leader in proxy infrastructure, it offers scalability and accuracy in web scraping Its code templates help to speed up development It incorporates the Web Unlocker capability through the IDE to avoid captchas and blocking. What is Bright Data? Bright Data is a proxy network that helps you turn websites into structured data. To get started with the platform, create an . account Check out this resource to learn more about . Bright Data https://www.youtube.com/watch?v=YzoLTalL6Uo&embedable=true Working with Web Scraper IDE On your account dashboard, click the icon, and afterward, select the button to open the template window. Datasets and Web Scraper IDE Get started The new window pop-ups a dialog box where you can select pre-existing dataset options to work with or create one from scratch if you desire to do so. Select the options, and the page should look something like this with the collector code. eBay discovery and PDP Now scroll down the page, and under the tab, pass in the name of a product you want to analyze and extract its data. Once done, click the button to run the preview and start the extraction. input Preview : You must also note that you can enter your scripts within the section. PS Interaction code Looking at the result tab after running the preview, it formatted the result from the eBay website based on the following data classification as , , , of the product, and so on. output product_url title image price Saving the collector To save the collector, click on the button to open the configuration page as seen below: Finish editing Initiate the collector by API Under the tab, let’s initiate this project and work with the scripts provided by clicking the button. My Scrapers Initiate by API Creating authorization token Authorization in programming grants access to users and identifies you as the account's rightful owner. Click on the menu at the bottom left of the window to create an API token. Account settings Upon adding the API token, you will receive a token for verification; enter the secret code. Now that is done, copy your API token key, as you won’t be able to retrieve it unless you create a new one for use. Return to the page, and copy the scripts based on your operating system (OS) in your terminal. Make sure to replace with the key you copied in the previous section after the word . New collector API_TOKEN BEARER In your command line interface or terminal, the result of the API code should look something like this: curl -H "Authorization: Bearer API_TOKEN" -H "Content-Type: application/json" -d '[{"keyword":"ralph lauren polo shirt","count":10,"location":"","condition":"New unused"}]' "https://api.brightdata.com/dca/trigger?collector=c_liopmjh61f3o3lz7dz&queue_next=1" The request command makes the code active in the section of the dashboard page. Once again, please copy and paste the code into the CLI tool. Result API New collector : Remember to put your API token key in place of the value . PS API_TOKEN curl "https://api.brightdata.com/dca/dataset?id=j_liosdy1cdutdi7sod" -H "Authorization: Bearer API_TOKEN" Run the script in the CLI, and the datasets in an object with status should read and a . building message If the response continues to show, retry sending the request. When successful, you should see this result object. Using Postman Like the displayed object above, let’s use to get the response for the . Postman Result API If you do not have Postman, download it . Postman is an API platform for building, publishing, monitoring, testing, and documenting APIs. Check to learn more about Postman and its use. here this resource article Open the Postman app and input these values: In the section in Postman, pass in the URL in the GET method request Click the tab, and select the Bearer Token from the dropdown, pass in your token value Authorization Type Click on button to send the request Send If the is successful, you should see a status message of 200 in the section and an array of objects for the queried scraped data request response eBay Creating a Node Server is a JavaScript runtime environment that allows the execution of JavaScript code outside the web browser enabling developers to build server-side applications and command-line tools. Node Let’s create a web server. One of the requirements for initializing your project in the terminal is using the package manager, npm, which is automatically present after installing on your local machine. Check it using this command: Node.js node --version It displays the current version of Node. Create a new directory. For this project, it is named . datasets Change its directory and initialize the project with the command: cd datasets

npm init -y The flag accepts the defaults that look like this: -y package.json {
  "name": "datasets",
  "version": "1.0.0",
  "description": "",
  "main": "index.mjs",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  ...
} Install the following packages: npm install -D nodemon Nodemon automatically updates and monitors for any changes in your source code and automatically restarts the server. npm install csv-parse The package is a parser for converting CSV text input into an array or objects. csv-parse Now, update the script section in the file to this: package.json {
  "name": "datasets",
  "version": "1.0.0",
  "description": "",
  "main": "index.mjs",
  "scripts": {
    "start": "node index.mjs",
    "start:dev": "nodemon index.mjs"
  },
  ...
} Next, create a new file in the root directory with the command: touch index.mjs To test this file, write a basic JavaScript script and run the server with the following command: npm run start:dev Social Media Data from Bright Data Scraping large datasets requires lots of effort and work using technologies like Node or Python. The way to get around this is to use a platform like Bright Data to obtain the information you need to get your results as soon as possible. Let’s get this dataset from Bright Data which will be the social media platform Instagram, with these steps: Sign up for a account. Bright Data Go to or select the on the . https://brightdata.com/cp/datasets/ Dataset Marketplace Datasets & Web Scraper IDE Open the , and under , select from the dropdown. Dataset Marketplace Categories Instagram.com Social media Click on and download the sample dataset in format. View dataset CSV Make sure to save the dataset in the root directory of the Node web server. Your folder structure should look something like this: .
└── datasets
    ├── node_modules
    ├── instagram.csv
    ├── package-lock.json
    ├── package.json
    └── index.mjs Reading CSV datasets in Node.js For this section, Node will read the comma-separated values (CSV), which dataset is from Bright Data. Update the file with the code: index.mjs import { parse } from "csv-parse";
import { createReadStream } from "node:fs";

const instagramAccount = [];

const isInstagramAccount = (info) => {
  return (
    info["posts_count"] > 300 &&
    info["followers"] > 6000 &&
    info["biography"] !== "" &&
    info["posts"] !== ""
  );
};

createReadStream("instagram.csv")
  .pipe(
    parse({
      columns: true,
    })
  )
  .on("data", (data) => {
    if (isInstagramAccount(data)) {
      instagramAccount.push(data);
    }
  })
  .on("error", (err) => {
    console.log("error", err);
  })
  .on("end", () => {
    console.log(`${instagramAccount.length} accounts are live`);
    console.log("done");
  }); The code above does the following: Using the method to open up a file or stream and read the data in it createReadStream() callback function used to filter the data from the actual CSV file isInstagramAccount : For connecting two streams, which means it connects to a readable stream source into the writeable destination, pipe parse() : Represents returning each row in our CSV file as a Javascript object with key-value pairs rather than just an array of values columns:true : Event handlers chaining pushing the newly created data into the empty array, and displays an error, shows the number of Instagram accounts present, and finally indicates when the script finishes .on instagramAccount done Running the scripts with the command should display the result like this in the terminal: npm run start:dev 643 accounts are live
done Conclusion Web scraping is an integral part of data extraction used in data science. The by Bright Data does all the heavy lifting in the background, presenting only the relevant data for your use. Web Scraper IDE This article walked you through understanding how to use the Web Scraper IDE and how you can build a custom datasets script to query large datasets of companies without fear of getting blocked by the company’s bots designed to help protect the company’s data. Resources CSV Parser for Node.js

How to Scrape Large Datasets at Scale

Recently, due to the various changes in macroeconomic factors, many businesses have faced difficulty coming up with forecasts that are reliable. According to , about 90% of the forecasting models do not go into production. This has been attributed to a complex pipeline that’s unscalable and a generally non-exploratory nature. Venturebeat Additionally, we also notice that a single model is generally not an answer to a range of forecasting needs as forecasting for an organization involves a lot of moving parts. In this article, we will cover general methods for putting time series forecasting models into practice while creating an ensemble approach that backtests a variety of models and chooses the best approach. Table of Contents Business Problem Approach Pipeline Overview Benchmarking Automation Conclusion Business Problem Predict the revenue a product would make over the financial year Revenue impact on the forecast, assuming a change in assumptions (like Take Rate, CPI) A scale to understand Contribution to Growth Anomaly Detection For a typical organization, forecasting is a process that goes hand in hand with budgeting, planning, and allocation. We typically look at a few metrics that we want to forecast and we run a time series model on historical data. During this process, we decompose the forecast into its corresponding trends, seasonality, and residual use. This result is then used to tune and come up with a forecast. Based on the nature and understanding of the business problem in hand we can also build assumptions in the form of regressors that can help instruct the forecast. These assumptions are based on features like spend actuals & intended spend, take rate, conversion efficiency, economic data like publicly available CPI forecasts, custom seasonality flags, etc. Approach Data preprocessing & preparation Forecast Decomposition into Trend, Seasonality, Holidays and Error Improve Accuracy by Cross Validation Top Down Approach vs Bottom Up Approach Running Backtests We now go ahead and come up with the overall approach we want to follow. Some of the questions we would ask during this step are how much data we have, how clean is the data, and how to parse the data to train/test datasets. Additionally, during this stage we also come up with the various models we want to try out and a metric we would consider for benchmarking these models against each other in the form of backtests. Sometimes we could also want to combine the results of multiple models together in an ensemble approach. A Top-down vs bottom-up approach is the methodology of determining whether we want to forecast at the higher level and decompose the forecast or if we want to forecast at a product level and aggregate it to come up with the overall forecast. Pipeline Overview Data Prep using BigQuery Set up VM Instance to deploy Pipeline Machine Learning Libraries like ScikitLearn, Prophet, Arima etc. Control pipeline using Bash Script Deploy Persistent Machines on Cloud Storage For a Typical forecasting project, we start the project by querying the data and creating a dataset that we can use as a data input. In certain cases, we would also go ahead and further create features that make the most sense to use in our training (for example converting categorical variables to Numeric) and then run a model after defining our target variable. Models like Facebook Prophet, Arima, MVAR, etc can all be used during this step. We then look at the results by charting them as well as aggregating the data to multiple granularities (like converting daily forecasts to weekly & monthly). Look at the results. Benchmarking Cross Validation Errors() MAPE = Mean Absolute Percentage Error Comparison (Compare to other metrics) After running a model, we then go ahead and cross-validate the data, using a metric like to understand the model performance. We optimize the forecast using this metric and run backtests and then perform hyperparameter tuning to derive the best parameters that fit the model. MAPE Automation Forecasting at Scale Predict the same data at hourly, daily, or weekly aggregations Run for Multiple Merchants in Parallel using the same Models Test Models in Production Once the core models and forecasting approaches have been selected, the subsequent step involves devising a scalable approach to run the pipeline. This can be achieved through the use of tools such as Airflow to schedule the job or simple chron jobs to create a schedule. During this process, it is important to understand the various files that need to be executed and the models that need to be run. An effective pipeline can be created by following a sequence of steps, starting with Data Preparation, followed by data cleaning and model preparation before executing the model run. Subsequently, data validation and scoring can be carried out before rerunning the model and back-testing the results. The results can then be written into a data table, which can be used to feed a dashboard or outputted as an excel sheet to test assumptions and gain a better understanding of the forecast. Conclusion In conclusion, forecasting is an integral part of business decision-making. It provides organizations with insights into market trends, potential risks, and growth opportunities. It is crucial to ensure that any forecasting automation implemented adds value to the organization by considering factors such as forecast accuracy, data reliability, and potential impact on the business. By doing so, we can provide effective and efficient forecasting automation that supports the organization’s goals and objectives, enabling them to make informed decisions that drive success. Some of the questions we ask ourselves by automating this forecast would be: Who Asked for the forecast? Man Hours Saved Business Impact Operational Impact Lead Image generated with stable diffusion.

Ensemble Time Series Forecasting at Scale

If you’re a marketing department and you want to reach the widest audience possible, text messaging is the best channel to do that with. Most people check their texts more often than email, and text messages have a 90%+ open rate within three minutes of reception. To send SMS and MMS messages at scale you need a cloud communications platform — a.k.a. a communications platform as a service (CPaaS). Peer-to-peer review site G2 lists more than 100 of them, with Plivo ranked number one for customer satisfaction. Throughout this article we’ll use Plivo and Twilio, the CPaaS market share leader, as examples. What’s Your Number? Once you’ve settled on a CPaaS, you have another decision to make: What number type should you use to send your messages? g follows different rules from person-to-person (P2P) texting. Businesses have three options: Commercial application-to-person (A2P) messagin 10-digit long codes (10DLC), toll-free numbers, and short codes. 10DLC Numbers Long codes are normal 10-digit phone numbers — the kind that individuals use every day to engage in P2P conversational messaging. is a service offered by major US carriers to explicitly allow commercial A2P SMS traffic over long code phone numbers. 10DLC is available only in the US. 10DLC You can turn long codes into 10DLC numbers by registering them with a carrier, which you can do through your CPaaS provider. Provisioning a 10DLC number takes less than a week. A vetting process determines the maximum throughput a carrier will allow for a 10DLC number. Throughput is the number of messages they can send in a given time period — messages per second, for example. Toll-Free are used by businesses that want their customers to reach them at no cost. In North America, 800, 888, 877, 866, 855, 844, and 833 denote toll-free numbers. Toll-free numbers Businesses provide toll-free numbers so that incoming calls are free of charge for the calling party, but they can also be enabled for SMS messaging through your carrier or CPaaS, generally in about one business day. Short Codes are five- or six-digit numbers used to send and receive SMS messages. Short codes Unlike long code numbers, shortcodes are not tied to an area code, so they can be used across a country to provide a nationwide brand identity under one easy-to-remember number. Short codes take the longest to acquire — up to 16 weeks. Throughput and Cost Two major differences between number types are their maximum throughput and cost. Throughput Limits Carriers impose varying maximum throughput on different number types. Generally speaking, short codes support the highest throughput and toll-free numbers the lowest. 10DLC numbers sit in the middle. Carrier-imposed throughput for 10DLC numbers depends on vetting scores assigned by an independent organization called The Carrier Registry. Cost Considerations There are three kinds of costs involved in messaging: one-time setup costs, number rental costs, and cost per message. All pricing numbers that we quote below were taken from the two representative vendors’ pricing pages. Short codes incur the highest setup costs: $1,500 for Plivo and $500 for Twilio. 10DLC numbers incur $44 charges for brand and campaign registration and vetting. There are no one-time charges for toll-free numbers. Number rental costs for long codes and toll-free numbers are negligible compared to usage costs for organizations that send a lot of messages. Short code rental costs, however, are significant. Cost per message depends on several factors: whether you’re sending or receiving messages, whether you’re using SMS or MMS, and which carrier you use. Your CPaaS vendor’s pricing page can break it down for you. For comparison purposes, we put together a table showing pricing for sending SMS messages, which for many businesses is the most common use case, and arbitrarily chose T-Mobile as the carrier for which to show surcharges in this table; the “plus” numbers are the T-Mobile carrier surcharges. Run the Numbers All of the different options make it difficult to figure out which number type makes the most economic sense. The best way to decide is to create a spreadsheet that includes the number of SMS and MMS messages you plan to send and receive per month, and factor in your carrier’s surcharges. Regardless of which number type you use, message senders must obtain opt-in consent before texting consumers — and be aware of other considerations. Plivo’s SMS best practices guide lays out those considerations for businesses that want to take advantage of the best channel for business communications — text messaging.

How to Send SMS and MMS Messages at Scale: What is Your Number?

Cómo enviar mensajes SMS y MMS a escala: ¿Cuál es su número?

Comment envoyer des messages SMS et MMS à grande échelle : quel est votre numéro ?

बड़े पैमाने पर एसएमएस और एमएमएस संदेश कैसे भेजें: आपका नंबर क्या है?

SMS および MMS メッセージを大規模に送信する方法: あなたの番号は何ですか?

Como enviar mensagens SMS e MMS em escala: Qual é o seu número?

Cách gửi tin nhắn SMS và MMS ở quy mô lớn: Số của bạn là gì?

如何大规模发送 SMS 和 MMS 消息：您的电话号码是多少？

Stories, News & Mentions about ATSCALE

Atscale’s Stories on HackerNoon