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The job demand for compliance managers is like never before. With increasing scrutiny on companies’ security posture, managers must meet a large array of regulations, standards, and contractual requirements. In addition, the technological revolution means companies must deal with massive amounts of data, raising concerns about data security and privacy.  Finally, a more worrying challenge is the threat from cybercriminals on the prowl to exploit vulnerabilities in the startup ecosystem. All these factors elicit frameworks and guidelines companies must comply with to ensure data security. In addition, they'll also need to look out for user privacy and safeguard industries from risks and sharp practices. In response, companies should consider adopting flexible and robust compliance management systems. These systems allow them to support the compliance manager and respond adequately to changing regulatory requirements. What is a Compliance Management System A compliance management system (CMS) is a software platform that helps compliance professionals gain the visibility, efficiency, and consistency they need to stay on top of all of their security assurance and compliance work. The system comes with powerful automation capabilities that allow compliance professionals to quickly and easily collect and test evidence from their key IT systems. Included integrations and deep collaboration capabilities enable an entire organization to advance its compliance function in unison. Further, the software serves as a system of record that retains all historical data from previous audits and generates insightful analytics showcasing the state of an organization’s compliance effort. A compliance management system (CMS) integrates industry frameworks/standards (e.g. ISO 27001, PCI DSS, SOC2, etc.), functions, tools, and internal controls to help you meet your compliance targets. So instead of relying on spreadsheets, file storage, and emails to process, communicate, implement, and evaluate your compliance processes, a CMS allows you to do all that without the usual stress, repetitions, and boringness. Rather than expend scarce resources on tedious, complicated, and ultimately inefficient manual tasks, you probably are better served with a CMS. It makes the jobs more convenient and more productive for your company. Unlike in the past when industries developed regulations in response to events once in a blue moon, tides of industry standards now change very rapidly. Companies have to either adjust their sails or get swept away. You must decide whether to stick with spending hours on spreadsheets or minutes on automated systems. An automated CMS minimizes the risk of compliance failure and the costs that come with it. A CMS benchmarks your compliance tasks against relevant, recent, and applicable frameworks to optimize your data intake, routing, and management. This way, your company is better prepared to meet internal and external compliance requirements, improve service and product quality, and improve customer satisfaction. The system provides a channel for effective communication among and between those responsible for your compliance task. For example, you can train employees on industry requirements with a CMS. Beyond that, the CMS lets your employees know their responsibilities for managing controls – so everyone can do their part to keep your organization protected.  Unifying these factors helps you monitor their activities and take corrective actions where necessary. Why You Need a Compliance Management System Firms must adhere to governmental laws and industry requirements that apply to their business operations. These requirements can range from how you conduct your business to the measures you put in place to secure your systems. Given the sporadic nature of most industries, the regulatory requirement can change very quickly. So you need to adopt a flexible system for compliance management. Such a system helps your business keep up with the changes so your company can keep up with industry demands. However, compliance is a rigorous, time-consuming process. Navigating the many regulatory frameworks to entrench a compliance culture in your business is not a walk in the park. It takes time, money, and human resources for businesses to keep up, and many firms don't just have enough to spare. Whatever the case, firms must comply. A no-action letter can protect you at the early stages of your business. However, sooner or later, you must get in line with government requirements. Otherwise, regulators will fine you or deny you licenses for breaching industry standards or failing to meet compliance targets. Therefore, you must optimize your efforts to meet industry standards as diligently as possible. Compliance is Everyone's Responsibility ." "There is no act for which nobody is responsible The timeless words of Otto von Bismark ring true when one considers the need for accountability in compliance management. Indeed, while every stakeholder in a company is responsible for internal and external compliance, not everyone will answer when external auditors and regulators come knocking. That is the board of directors' job. Therefore, the board of directors needs to keep the system working as far as regulatory requirements and industry standards are concerned. A first step is to stay abreast of applicable requirements and establish relevant compliance programs for all parties engaged in the firm. Guided by established programs, management can maintain oversight by communicating compliance expectations and demonstrating willingness to meet those expectations. Your organization's leadership needs to set the tone of commitment from top to bottom and show that it takes compliance very seriously. Leadership at this stage is vital. Employees embrace rules and guidelines when management embodies those values. Therefore, you should not only mobilize resources for compliance management, but you also need to coordinate the use of such resources according to compliance objectives. The Role of Compliance Audit in a Compliance System A compliance audit involves an independent review of your compliance program. It provides the opportunity to assess if your information security activities are measuring up to the objectives/requirements that your organization intends to meet. It allows you to gather evidence and identify compliance risks and weaknesses in-house, so you can correct them before external auditors come around. The aim of an audit is not just to meet legal requirements. More importantly, an audit allows you to validate whether your organization is truly doing what you said you will do to protect information and satisfy customers’ security needs. This step is essential in preventing the reputational damage that could result from a compliance failure. Tools For Compliance Professionals As industries evolve and the list of compliance tasks expands, adopting industry requirements with your corporate standards and implementing them across your company is too much of a job for a compliance officer. In no time, you will realize that having a compliance officer alone does not guarantee compliance success for your business. A CMS can, however, increase your chances significantly. A CMS bundles your compliance workflow into a single system for easy execution. So, for example, instead of filing individual and disjointed reports, your compliance team can use software to create their reports easily and quickly. An outcome of this unified system is that you will have a real-time record of your company's compliance posture with applicable guidelines and requirements. In addition, a centralized compliance system makes for a convenient way for you to review and analyze your risk and compliance status and take corrective action in earnest. Regardless of your organization's complexity, CMS tools are flexible to permit the integration and operation of your compliance system. Such flexibility and scalability are exceedingly feasible with software like . In addition, this CMS tool allows you to manage your compliance program requirements, controls, and proof in one place. Hyperproof Hyperproof is software designed with compliance professionals and security-conscious organizations in mind. The software supports frameworks like SOC 2, ISO 27001, NIST 800-53, and PCI-DSS that contain guidelines and requirements on legal and industry expectations of your company. Besides supporting the frameworks, a CMS provides an easy-to-use UI and workflows for compliance managers to oversee these security compliance frameworks and regulatory compliance programs most effectively and efficiently. Other software like Third-Party Manager empowers security and compliance professionals with the tools and data to manage compliance systems and ensure audit readiness. It supports an expanded range of frameworks like Hyperproof. However, unlike Hyperproof, Third-Party Manager does not allow companies to create bespoke internal control programs for their firms. Either way, you can be sure to meet both internal and external compliance targets with both software. Not all CMS execute integrated compliance management; some are task-specific. Skillcast and RegTech are great for compliance training. Donesafe helps companies to comply with HSE requirements. For legal compliance, firms can use Libryo, a software that covers laws applicable to businesses in multiple jurisdictions. Finally, if you are in an industry that deals with large data, Netwrix has all the features you need to maintain data security compliance. Final Thought The startup landscape is evolving daily; so is the compliance and security landscape. As new threats and regulations emerge, security and compliance managers must be at the top of their game to prevent risks and pass audits. In addition, they need to maintain the capacity to ensure the health and safety of their organizations in an ever-changing environment. Meeting these demands is a rigorous but necessary challenge for compliance managers. A CMS  can make this challenge simpler. Some software like RegTech, Donesafe, and Netwrix enable you to achieve specific compliance targets, while others such as Hyperproof provide an integrated platform for general compliance management. Each of the software has its strengths and weaknesses. Whatever the CMS software you choose, make sure it's up to the challenge.

How Compliance Management System Tools Are Changing the Compliance Landscape

The Python programming language is assisting the developers in creating standalone PC games, mobiles, and other similar enterprise applications. Python has in excess of 1, 37,000 libraries which help in many ways. In this data-centric world, most consumers demand relevant information during their buying process. The companies also need data scientists for achieving deep insights by processing the big data. This info will guide the data scientists while making critical decisions regarding streamlining business operations and several other related tasks that need valuable information for accomplishment efficiently. Therefore, with the rise in demand for data scientists, beginners and pros are looking to reach resources for learning this art of analysis and representation of data. There are some certifications programs available online which can be helpful for training. You can find blogs, videos, and other resources online as well. Let’s have a look at some of the Python Data science libraries that are helpful for you. 1 . NumPy: is among the first choice for data scientists and developers who know their technologies dealing with data-related things. This is a Python package and is available for performing scientific computations.  By using NumPy, you may leverage the n-dimensional array objects, C, C++, FORTRAN programs based on integration tools, functions for difficult mathematical operations such as Fourier transformations, linear algebra, and random numbers. Therefore you may effectively integrate the DB by selecting a variety of operations for performing. NumPy NumPy gets installed under TensorFlow and other such machine learning platforms, thereby internally providing strength to their operations. As this is an array interface, it will allow multiple options for reshaping large data sets. NumPy may be used for treating images, sound wave representations, and other binary operations. In case you have just arrived in the field of data science and machine learning, you must acquire a good understanding of NumPy for processing the real-world data sets. 2. Theano: Another useful Python library is , which assists data scientists to create big multi-dimensional arrays which are related to computing operations. This is similar to TensorFlow; however, the only difference being it is not very efficient. It involves getting used to parallel and distributed computing-related tasks. By using this, you may optimize, evaluate, or express the data-enabled mathematical operations. Theano Due to its GPU based infrastructure, the library has the capability of processing the operations in quicker ways than compared to the CPU. The library stands fit for stability and speed optimization and delivering you the expected outcome. For quicker evaluation, the C code generator used is dynamic and is extremely popular among data scientists. They can do unit testing here for identifying the flaws in the model. Read 7 Best Data Visualization Tools 3. Keras: One of the most powerful Python libraries is that permits higher-level neural network APIs for integration. The APIs will execute over the top of TensorFlow, CNTK, and Theano. Keras was developed for decreasing the challenges faced in difficult researches permitting them to compute quicker. For someone using the deep learning libraries for their work, Keras will be their best option. Keras permits quicker prototyping and supports recurrent and convoluted networks independently. It also allows various blends and execution over CPU and GPU. Keras Keras give you a user-friendly environment, thereby decreasing the efforts required for cognitive loads by using simple APIs and so providing necessary results. Because of the modular nature of Keras, you may use a range of modules from optimizers, neural layers, and activation functions, etc. for preparing newer models. Keras is an open source library and is written in Python. It is a particularly good option for the data scientists who are having trouble in adding newer models as they may easily add newer modules as functions and classes. 4. PyTorch: It is one of the largest machine learning libraries available for data scientists and researchers. The library aids them with dynamic computational graph designs; quick tensor computation accelerated via GPU and other complicated tasks. In the case of neural network algorithms, the PyTorch APIs will play an effective role. This crossbreed front-end platform is simple to use and allows transitioning into a graphical mode for optimization. In order to get precise results in the asynchronous collective operations and for the establishment of peer to peer communication, the library gives native support to its users. By using ONNX (Open Neural Network Exchange), you may export models for leveraging the visualizers, run times, platforms, and many other resources. The greatest part of PyTorch is that it enables a cloud-based environment for simple scaling of resources utilized for deployment testing. PyTorch is developed on a similar concept to another machine learning library called Torch. During the last few years, Python has gradually become more popular with the data scientists because of the trending data-centric demands. 5. SciPy: This is a Python data science library used by researchers, data scientists, and developers alike. However, do not confuse the SciPy stack with the library. SciPy gives you optimizations, integration, statistics, and linear algebra packages for the computations. The SciPy is based on the NumPy concept for dealing with difficult mathematical problems. SciPy gives numerical routines that can be used for integration and optimization. SciPy will inherit a range of sub-modules to select from. In the event that you have recently started your career in data science, SciPy will be quite helpful for guiding you through the whole numerical computation. We have seen thus far how the Python programming can assist the data scientists in analyzing and crunching big and unstructured data sets. There are other libraries such as Scikit-Learn, TensorFlow, and Eli5 available for assistance through this journey. 6 : . Pandas The Python Data Analysis Library is called PANDAS. It is an open source library in Python for availing the analysis tools and high-performance data structures. PANDAS is developed on the NumPy package, and its main data structure is DataFrame. By using DataFrame, you can manage and store data from the tables by doing manipulation of rows and columns. Methods such as square bracket notation decrease the personal effort involved in data analysis tasks such as square bracket notation. In this case, you will have the tools for accessing the data in the memory data structures and perform read and write tasks even though they are in multiple formats like SQL, CSV, Excel, or HDFS, etc. 7. PyBrain: This is a powerful modular machine learning library which is available in Python. The long form of PyBrain goes like Python Based Reinforcement Learning ArtifiArtificial Intelligence and Neural Network Library. For the entry-level data scientists, this offers flexible algorithms and modules for advanced research. It has a range of algorithms available for evolution, supervised and unsupervised learning, and neural network. For the real-life tasks PyBrain has emerged as a great tool, and it is developed across a neural network in the kernel. 8. SciKit-Learn: This is a simple tool used for data analysis and data mining-related tasks. It is licensed under BSD and is an open-source tool. It can be reused or accessed by anyone in different contexts. The SciKit is developed over NumPy, Matplottlib, and SciPy. The tool is utilized for regression, classification, and clustering or managing spam, image recognition, stock pricing, drug response, and customer segmentation, etc. SciKit-Learn allows for dimensionality reduction, pre-processing, and model selection. 9. Matplotlib: This library of Python is used for 2D plotting and is quite popular among data scientists for designing different figures in multiple formats across the respective platforms. It can be easily used in the Python code, Jupyter notebook, or IPython shells application servers. By using the Matplotlib, you will be able to make histograms, bar charts, plots, and scatter plots, etc. 10. TensorFlow: TensorFlow is an open source library designed by Google for computing the data low graphs by using empowered ML algorithms. The library was designated for fulfill fulfilling the high demands for training for neural network work. TensorFlow is not only limited to scientific computations conducted by a Google rater. It is used extensively for the popular real-world applications. Because of the flexible and high-performance architecture, you can easily deploy it for all GPUs, CPUs, or TPUs and you can perform the PC server clustering for the edge devices. 1 1. Seaborn: It was designed for visualizing complex statistical models. Seaborn comes with the potential of delivering accurate graphs like heat maps. Seaborn is developed on the Matplotlib concept, and as a result, it is highly dependent on it. Even the minor data distributions can be seen by using this library, which is the reason why the library has become popular with the developers and data scientists. 12. Bokeh: It is one of the more visualization purpose libraries used for the design of interactive plots. Similar to the library described above, this one is also developed on Matplotlib. Because of the support of used data-driven components such as D3.js this library can present interactive designs in your web browser. 13. Plotly: Now, let’s see the description of Plotly, which happens to be one of the most popular web-based frameworks used by the data scientists. The toolbox offers the design of visualization models by using a range of API varieties supported by the multiple programming languages which include Python. InterInteractive graphics can be easily used along with numerous robust accessories via the main site plot.ly. For utilizing Plotly in the working model, you will have to set up the available API keys correctly. The graphics are processed on the server side, and once they are successfully executed, they will start appearing on the browser screen. 14. NLTK: The long form of NLTK is Natural Language ToolKit. As indicated by its name, the library is useful in accomplishing natural language processing tasks. In the beginning, it was created for promoting teaching models along with other NLP enabled research like the cognitive theory used in AI and linguistic models. It has been a successful resource in its area and drives real-world innovations of artificial intelligence. By using NLTK you can perform operations such as stemming, text tagging, regression, corpus tree creation, semantic reasoning, named entities recognition, tokenization, classifications and a range of other difficult AI related tasks. Now challenging work will need large building blocks such as semantic analysis, summarization, and automation. But this work has become easier and can be easily accomplished by using NLTK. : 15. Gensim It is a Python-based open-source library that permits topic modeling and space vector computation by using an implemented range of tools. compatible with the big test and makes for efficient operation and in-memory processing. It utilizes SciPy and NumPy modules to provide easy and efficient handling of the environment. Gensim utilizes unstructured digital text and processes it by using in-built algorithms such as word2vec, Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Processes (HDP), and Latent Semantic Analysis (LSA). 16. Scrapy: Scrapy is also known as spider bots. Scrapy is a Data science library responsible for crawling the programs and retrieving structured data out of web applications. Scrapy is a Python-written open-source library. This happens to be a complete framework having the potential to collect data via APIs and acts as a crawler. You can write codes by using Scrapy, re-utilize universal programs, and develop scalable crawlers for the applications. it is created across a spider class that contains instructions for the crawler. 17. Statsmodels: Statsmodels is another Python library, and it is responsible for giving exploration modules by using multiple methods for performing assertions and statistical analysis. It uses robust linear models, time series, analysis models, regression techniques, and discrete choice models, thereby making it prominent among similar data science libraries. It comes with a plotting function for the statistical analysis for achieving high-performance outcomes during the processing of the large statistical data sets. 18. Kivy: This is another open-source Python library providing a natural user interface that may be accessed easily over Linux, Windows, or Android. The open-source library is licensed under MIT, and it is quite helpful in the building of mobile apps along with multi-touch applications. In the beginning, the library was developed for the Kivy iOS and came with features such as a graphics library. Extensive support is provided to the hardware with a keyboard, mouse, and a range of widgets. You can also use Kivy for creating custom widgets by applying it as an intermediate language. 19. PyQt: Another Python binding toolkit for being used as a cross-platform GUI is PyQt. PyQt is being implemented as the Python plugin. It is a free application licensed under the General Public License (GNU). It comes with around 440 classes and in excess of 6000 functions in order to make the user experience simpler. PyQt has classes to access SQL databases, active X controller classes, an XML parser, SVG support, and several other useful resources for reducing user challenges. 20. OpenCV: This library is designed for driving the growth of real-time computation application development. The library is created by Intel, and the open-source platform is licensed with BSD. It is free for use by anyone. OpenCV comes with 2D and 3D feature toolkits, mobile robotics, gesture recognition, SFM, Naive Bayes classifier, gradient boosting trees, AR boosting, motion tracking, segmentation, face recognition and object identification algorithms. Although OpenCV is written by using C++, it will provide binding with Python, Octave, and Java. Previously published at https://skillpaythebills.com/top-20-data-science-libraries/

Python Libraries For Data Science

Why should I learn AWS? Cloud computing is taking the tech world by storm and so is the need to learn cloud computing. The only thing certain in IT today is that it’s never too late to cash in on the cloud. The demand curve for cloud skills will only grow at a rapid pace as cloud becomes critical to IT and businesses at large. According to Forbes, the spending on public cloud services is anticipated to grow at a compound annual growth rate of 19.4% from $70B in 2015 to more than $141B in 2019. AWS generated a revenue of $5.44 billion in first quarter of 2018, 49% increase year-on-year. With Fortune 500 companies and institutions like NASA, General Electrics, Netflix, IMDb, AirBnB, to name a few extensively using AWS cloud computing services- AWS is the leader in the cloud computing market growing at a breakneck pace. Still not convinced if AWS is for you? This blog will give you the top 7 reasons to learn AWS and help you understand why should you pursue AWS careers. Reasons to Learn AWS 1) AWS -Fastest Growing Public Cloud in the World AWS was officially launched in 2006 and there were around 180,000 developers on the platform by 2007. By 2015, AWS revenue was $6Billion USD per annum and since then revenues have doubled and are growing at an exponential rate .AWS enjoys the advantage of 7 year head start before facing like-minded competition, and since then the team has never slowed down. AWS is continuously boosting its growth every quarter. AWS expanded by 42% in third quarter of 2017 which increased to 45% in fourth quarter and then  49% in first quarter of 2018. The fastest growing AWS products used by customers include data crunching tool Amazon EMR (Elastic MapReduce) growing at a compound annual growth rate of 24% , network monitoring offering Amazon CloudWatch growing at a compound annual growth rate of 23%, and serverless product AWS Lambda growing at a compound annual growth rate of 18%. Considering AWS’ huge lead, if the current trends continue, we are likely to witness remarkable acceleration in AWS growth in future. Want to pass the AWS Solutions Architect – Associate Exam? Want to become Amazon Web Services Certified? Check Out this ! AWS Training 2) Increased Enterprise Cloud Migration to AWS It is not only small organizations migrating to the cloud, even commercial enterprises are migrating to the cloud at a rapid pace. According to a report from McKinsey’s Silicon Valley group, 77% companies were dependent on traditional IT infrastructure till 2015 but with large enterprise migrating to cloud-based infrastructure, the number is expected to drop down to 43% in 2018. Organizations migrating their services and applications to the cloud are confronted with multiple challenges and roadblocks. The cloud platforms like AWS, Azure, Google offer several automated features but migration is not one of them. This implies that organizations have to depend on customised, professional services from providers. There is an increasing demand for cloud professionals who can oversee migration projects in a secure and organised manner. This requires professionals to have in-depth knowledge of any chosen cloud platform like AWS. Becoming a certified AWS solutions architect will endow you with the entire process of shifting from an existing on-premise application to AWS cloud. 3) AWS Careers Pay Top Money AWS Certified Solutions Architects -Associate professionals earn an average salary of $121,292, over $13,000 higher than the average salary of top 15 IT certified professionals. The salaries for AWS skills in premium areas like Austin, San Francisco, Washington, or Boston are 25% higher than usual. According to PayScale, non-architect related AWS cloud jobs garner comparatively lower pay checks over architect jobs. The best-paid positions that can help professionals make the most money from their cloud jobs is – AWS Enterprise Cloud Architect –$138,051 AWS Senior Cloud Solutions Architect -$132,092 Top Paying AWS Skills According to Indeed.com AWS SkillSalary DynamoDB$141,813 Elastic MapReduce (EMR)$136,250 CloudFormation$132,308 Elastic Cache$125,625 CloudWatch$121,980 Lambda$121,481 Kinesis$121,429 Key Management Service$117,297 Elastic Beanstalk$114,219 Redshift$113,9504) 4. AWS Skills on the list of most in-demand skills since 2015 “The need for cloud and data specialists is unlikely to abate anytime soon, especially as the software platforms powering businesses become more powerful (and, in many ways, more complex). For tech professionals, learning any new cloud-related skills is a reliable way to advance their careers. On the employer side of the equation, companies should prepare to continue paying top dollar for specialists in increasingly ubiquitous technologies.”- said Dice President Bob Melk Knowing which way the wind blows and then keeping up with the change in the same direction is the need of the hour. And as of today the wind seems to be blowing into AWS cloud. As Google trends indicate, organizations are heading for a serious skills shortage around AWS. Professionals yearning for a lucrative career should run in this direction. With more than 350,000 professionals required to fill cloud job roles there’s clearly a huge opportunity for people who can prove their skills. AWS skills are sitting high on the list of most in-demand skills for employers. Use the search term “Cloud”  for a simple full-time jobs search today on Indeed.com and you will see that there are more than 70,000 results. Of these Amazon Web Services (AWS) skills are the most in-demand skills with 25,361 open jobs. 60% of the cloud computing job postings ask for skills related to Amazon Web Services. According to a Indeed report, there has been a significant growth of 37% in the Australian job market for AWS professionals in the past year. The statistics reveal that the number of open AWS jobs is 6 to 12 times more than the number of job seekers. There are not sufficient people who know how work with AWS cloud services and jobs are going unfilled with salaries sky-rocketing for skilled and certified AWS professionals. It  is clearly evident that cloud computing skills and , specifically AWS cloud skills are and will continue to be in great demand over the years to come. AWS’s dominance as a public cloud platform is a major catalyst driving demand for AWS skills and certifications to higher levels in the coming years. 5. Flexibility & Scalability Now that I have already mentioned scalability let’s discuss that first. Right from initiation Amazon’s focus has been on scalability. This flexibility meant businesses could easily scale up or down to meet the business needs. The same philosphy has been incorporated by AWS which greatly helps the users.This extremely flexible system is now the hallmark of AWS, and is one of the main reasons to choose AWS. Hence on demand scalability and flexibility find their place in the list of top 7 reasons to learn AWS and use it. 6. The AWS Certification is a goal that is achievable Since AWS certification has been around in some form since 2013, many resources exist to help prospective exam takers to learn AWS. The best way to learn AWS  is often by taking some form of . AWS Architect training comes in the form of live instructor-led training, virtual and digital training in addition to a plethora of books and forums that exist on the subject. AWS Solutions Architect training Oftentimes, the best AWS training is training that offers a combination of theory with a great deal of hands-on exposure to the core services on the platform. The wealth of information that exists as well as the overwhelming amount of sample questions and best practices available makes achieving your certification a real possibility. 7. AWS community This opens up greater networking opportunities for certified candidates. AWS certified candidates gain access to the AWS certified LinkedIn community, receive invites to regional events and free practice exams to prepare for other certifications. Amazon also provides you with official AWS certified logo usage and digital badges so you can display your credentials to the world. In closing, AWS has become one of the fastest growing technology products in information technology. Companies are moving forward with AWS at astounding rates. Getting your AWS certification now will help you to gain the necessary knowledge and learn the tools needed to work within this new landscape of technology. This brings us to the end of this blog on Top 7 reasons to learn AWS. To learn more about AWS you can refer our blog. We have also come up with a curriculum which covers exactly what you would need to crack the Solution Architect Exam! You can have a look at the course details for Amazon AWS Tutorial . AWS Solution Architect training Resources to learn Popular programming AWS Better Jobs Finally, everyone in this world is learning one or the other for what purpose? Yes, obviously to get a wonderful and meaningful job to lead a peaceful life. Are you not ready to get hold of an innovative and powerful job in this amazing competitive world? If any of you waiting to enter the world of cloud industry, better go ahead with cloud computing course which can tremendously benefit you to reach the interview phase. We are at the end of this blog! There are many courses and training session on AWS, either online or offline. Be sure that there is no end for learning and age is not a matter for enhancing the technical skills. AWS plays a major role in this competitive technology world and none of us are underestimated to keep away from learning such concepts. Now you are aware of the top 7 reasons to learn AWS and now what are you waiting for? Just keep rocking with all your valuable skills to enter the AWS world.

7 Reasons Why You Should Keep Learning AWS

Determining ‘right’ and ‘wrong’ for subjective crowd input using a global expertise bank and expertise-weighted reviews In my last article, I wrote about — using human intelligence and experience to answer questions that and AI cannot answer. the need for Social Search Google For a social search platform like (or any system that relies on crowd intelligence) to work and to offer high quality responses**,** there needs to exist — for categorizing answers as better or worse, right or wrong. Without a mechanism to measure ‘correctness’ of an answer, there would be no way to rank or filter the answer space. Furthermore, in a crowdsourcing platform that offers rewards or penalties to encourage responders to participate, there’d be no way to determine which responders to reward, which responders to penalize, and by how much. Proffer a reliable system for evaluating crowd-provided answers (subjecting an answer to the scrutiny of other experts in the same field) is the traditional approach for assessing crowd inputs. It’s a powerful idea and widely practiced in academia and online communities, but suffers from political manipulation, a lack of granularity/specificity in the measurement of expertise, an ability for reviewers to falsely accumulate expertise, and an unnatural constraint whereby votes from skilled and unskilled reviewers have equal impact on the outcome. Peer review In building Proffer, we sought to create a protocol for decentralized peer review that avoids the pitfalls of traditional peer review and runs on the , adapting to cover future crowdsourcing and social search use cases on a global scale. blockchain Don’t want to read? Go through the slides for a protocol overview and example. This article is structured as follows: Problems with peer review as implemented today Solution and Key Features: a novel peer review platform built on the concept of , where reviews are weighted by the reviewers’ expertise, where ‘correctness’ of answers is based on cumulative skill of its supporters minus that of its detractors, and where expertise for all reviewers for all topics is stored and updated on the ( ). self-optimizing expert networks blockchain global expertise bank Walkthrough of crowdsourced peer review on Proffer, a Physics Q&A use case. Bootstrapping a global expertise bank (what happens at t=0) Problems with Traditional Peer-Review It puts significant power in the hands of a few without offering the necessary checks and balances, or the necessary incentives to encourage unbiased, thoughtful reviews. Just ask the academic community — e.g. in , Chris Lee describes academic peer review as “utter randomness,” citing the undue influence that reviewers’ “mood, medication, and memory” play on the outcome. He proposes a decentralized peer review process for academic publications that opens up each review to a crowd of 100+ panelists and significantly improves upon the quality and efficiency of traditional peer review. Peer Review is politically influenced: this Ars Technica article For example, anyone can endorse anyone for any skill on LinkedIn. Reddit users can accumulate ‘karma’ from one subreddit (e.g. ) to qualify them to post on a completely unrelated subreddit (e.g. ). It’s hard for our computational systems to identify true expertise, and such systems can be fooled. Expertise should be measured based on actions and outcomes, not on self- or crowd-reported inputs with a single data point. Expertise is arbitrary, inaccurate, and easily manufactured. /r/dogs /r/ethereum across apps, i.e. Reddit “karma” for blockchain topics doesn’t carry any weight on Stack Exchange or Quora blockchain topics. A ‘top writer’ for Artificial Intelligence on a Medium blog will see no benefits of that reputation when posting on an AI thread on Reddit. Users are forced to re-create their reputation on each new platform or app they join, and must maintain this reputation separately cross the various platforms, leading to a missed opportunity for effects and usage driven by shared reputation across apps. Expertise is fragmented network Crowdsourced Peer Review: Key Features : stores every user’s in every topic on a common decentralized ledger. It is ‘global’ as opposed to ‘fragmented’ because all dApps read/write expertise to this bank rather than maintaining their own application-specific skill stores. A global expertise bank benefits from and creates shared network effects across multiple applications — a user can switch from one math tutoring dApp to another while retaining his/her expertise in topic ‘Math’. Global expertise bank skill A mechanism to objectively determine correctness of a subjective answer by the cumulative expertise of those who answered or upvoted it minus those who downvoted it. This allows more skillful responders to have a greater influence on the correctness of a response than less skillful responders. Skill-weighted peer reviews: A network of experts where expertise can be gained or lost by any individual, pushing the network closer to ground-truth over time. i.e. false expertise gets filtered out as incorrect responders lose skill, and true experts rise to the top as they provide correct responses repeatedly. Self-optimizing expert network: Crowdsourced peer review on Proffer: a Physics Q&A example Step 1: Receive a question from Seeker, broadcast to qualified responders Seeker asks a question that he/she wants crowd input on, e.g. “Why is the sky blue?” Seeker optionally backs the question with money ( ) that will be distributed among correct responders. This seeds an incentive pool called . A new token backing pool is created question that is asked on the platform. SeekerStake SeekerStake Token Backing Pool for each Step 2: Start peer review process. Receive answers and votes from responders Responders a.k.a peer reviewers see the question and by other responders. Counterintuitively, responders must also contribute a stake ( ) to the as an expression of their confidence in their response; this stake is returned to them unless their answer is deemed incorrect (explained below) by the peer review protocol. The requirement of a dis-incentivizes spam and puts responders’ skin in the game. can either respond with a new answer, or upvote/downvote answers previously submitted ResponderStake Token Backing Pool ResponderStake Step 3: Compute the net ‘skill’ supporting each submitted answer The protocol keeps track of the “net skill” backing each answer that is added to the answer space. The for an answer is equal to the skill of the responder who first proposed it (+) skill of all responders who upvoted it (-) the skill of all responders who downvoted it. is a skill-weighted measure of the correctness of an answer at any given point in time. SkillBacking plus minus SkillBacking “Skill” here refers to the very specific / granular skill of the user in the topic being addressed in the question, which in this case can be ‘Physics’ or ‘Optical Phenomenon’. The skill is read from the and written back to it each time it’s updated. Global Expertise Bank Shown above, five different responders (Joe / Sam / Jenny / Andrea / Brett) contribute five new answers to the question “Why is the sky blue?”, paying into the for each answer. Answer Stake Token Backing Pool Shown below, 15 different responders, named P1 thru P15, choose to upvote or downvote the five answers already in the Answer Space, paying into the for each vote. Note that each vote updates the net skill backing of each answer: an upvote adds the upvoter’s skill and downvote reduces net skill by the downvoter’s skill. Vote Stake Token Backing Pool Step 4: Use net ‘Skill Backing’ to determine which answers are Correct/Incorrect and accordingly compute payouts for each responder After P1 thru P15 have submitted their votes, the answer space has five answers, each with a net skill backing. At this point, we can either use a heuristic such as “net skill backing is > zero” to determine which answers are correct, or we can present all options to the Seeker who initially asked the question and allow him / her to choose what is the correct answer. The best way to determine ‘correctness’ will depend on use case, so our protocol provides two configurable parameters rather than prescribing the judge of correctness/incorrectness: Let’s assume for this example that = ‘seeker’ and = ‘peers’. This means that we can determine incorrect answers based on net skill backing. Answer 3, which received a net skill backing of -70, and Answer 5 with net skill backing of -15 are therefore . Judge of Correctness Judge of Incorrectness Incorrect Answer 2 is deemed because we make the assumption that the Seeker will select Answer 2 as the best answer since it has the highest net skill backing. Answers that are neither Correct nor Incorrect are labeled . Correct undecided We now use the following guidelines to compute two types of payouts — financial and skill — for each responder: Guidelines for financial and skill payouts for each responder Skill is an open/indefinite quantity. It can be given freely and taken freely. Performing skill payouts is therefore as easy as increasing or decreasing the responder’s skill on the Global Reputation Bank for the topic at hand. Skill updates after Answers 3 and 5 determined to be and Answer 2 to be . Incorrect Correct Money / tokens on the other hand are a closed / zero-sum quantity. They are re-distributed, not created, from incorrect responders to correct responders. The protocol first returns the money that responders with Correct responses and undecided responses had put into the at the start of the process. Token Backing Pool The remainder in the pool after these stakes have been returned consists of the original SeekerStake, and the AnswerStake(s) and VoteStake(s) of incorrect responders. Finally, this amount can be distributed across correct responders as their reward for answering or voting correctly. Possible outcomes in crowdsourced peer review. Incorrect responders lose what they had put in. Correct responders recover what they had put in AND get an additional reward. Bootstrapping the Global Expertise Bank: getting it right at t=0 In steady state, the self-optimizing expert network described above updates the expertise of each responder over time based on his/her current expertise, his/her response, the crowd’s current expertise, and the crowd’s responses. Expertise of person ‘p’ at time ‘t+1’ is a function of that same person’s expertise and response at time ‘t’, and the crowd’s expertise and response at time ‘t’ As long as expertise in the system is a reliable metric at time , we can rest assured that it will be reliable at time , getting closer to ground truth / ‘actual’ expertise of all users with time. t t+1 Our peer review protocol and the global expertise bank are capable of working out of the box, with all responders starting off with Skill = zero across all topics. The law of large numbers will ensure that with enough iterations, responders who started off with no skill will have won or lost skill based on the crowd’s voting for or against them, equilibrating somewhere close to their actual skill relative to their peers. However, to improve the rate at which this skill equilibrium is reached, and to provide the highest quality answers right from , particularly for industry topics that require specialized knowledge or certification, , both online and offline, formal and informal. t=0 we propose bootstrapping the expert network by manually selecting and on-boarding experts from pre-existing networks For example, in the field of healthcare, one could onboard physician networks and initialize skill for each physician in their respective practice (‘Cardiology’, ‘General Medicine’, etc.) based on a combination of past experience and in-person interviews. The same can be done with practicing lawyers for topics concerning the Law, with school teachers for topics in k-12 education, with land developers and construction teams for topics concerning real estate, etc. Seeding an expert network through a manual process would be neither trivial nor quick, but in a world where the nature of work is already tending towards ad-hoc gigs that allow workers to monetize their free time, an expert network on the blockchain with frictionless reward payments for experts who answer correctly could be an exciting platform to be a part of. This would be especially true for Proffer’s global expertise bank, as experts would be able to leverage reputation accumulated answering questions on Proffer while using other dApps built on the same global expertise bank. If you’re curious to learn more about Proffer, the social search protocol we’re building on top of crowdsourced peer review and self-optimizing expert networks, check out our tech spec here , and the 5 apps we’ve published to showcase five different use cases for Proffer on our website here .

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