The PredictWise Insights Engine – our subscription-based dashboard - algorithmically curates relevant insights for every step of an advertising/marketing campaign, providing data-driven insights for topic-, messages-, and content-selection, identifying target populations, and enabling RoI tracking in real time

PredictWise

Every business has at least one piece of equipment that would seriously impact their service, production, or everyday operations if it suddenly broke down. As technology has advanced, predictive maintenance has emerged as a solution to this issue. How can business owners implement it effectively to get the most benefits? What Is Predictive Maintenance? Predictive maintenance is a maintenance strategy that monitors machines’ conditions to forecast likely events. It uses data analytics to anticipate and prevent equipment failures. Businesses use it to estimate when their machinery will break down to avoid unexpected downtime and delays. Since unplanned downtime carries an immense financial and reputational cost, many businesses consider predictive maintenance critical. According to one survey, believe it is a strategic priority. seven in 10 organizations Unlike its counterpart, preventive maintenance, predictive maintenance doesn’t happen at specific, pre-set intervals regardless of equipment condition. Instead, it occurs as needed and before signs of failure appear. Businesses can maintain an optimal maintenance schedule this way, keeping their machines in top shape. Countless factors can accelerate wear, causing equipment to fail sooner than expected. For instance, while projector bulbs , poor storage, misuse, or a lack of upkeep could make them burn out faster — potentially in the middle of an important meeting. In these scenarios, preventive maintenance isn’t as accurate or effective as it should be. last around 2,500 hours Why is predictive maintenance better than preventive? While a comparison is somewhat subjective, the first strategy is typically more effective. Since happen at random, it pays to maintain machinery on a continuous schedule instead of a pre-set one. 80% of equipment failures How Can AI Be Used in Predictive Maintenance? Traditional predictive maintenance relies on a manual, paper-based process where someone tracks metrics like the vibration rate, oil readings, and emissions to tell whether a machine shows signs of failure. Recently, artificial intelligence has emerged as an alternative. Predictive maintenance is one of the of AI in industrial sectors since it reduces operational costs and helps guide business decisions. It’s already become very prevalent even though advanced algorithms have only existed for a few years. most well-funded use cases With AI-driven predictive maintenance, an algorithm takes over and automates most manual processes like data collection and analysis. Now, businesses can receive insights into their machine’s condition in real-time and around the clock instead of on a weekly or monthly basis. Since AI can integrate directly with other technologies, its insights are based on real-time conditions instead of a subjective interpretation of metrics. While an algorithm’s training process can introduce bias, it’s still more accurate than people are — and immune to human error. Machine learning algorithms can evolve, meaning they adjust to how condition data differs as equipment gets new components or changes over time. This feature also means businesses don’t have to worry about their predictive maintenance technology becoming outdated one day. While AI-driven predictive maintenance performs best with real, current data, organizations can also use synthetic or simulation-generated information. This technology is highly versatile, so it can adapt to their business-specific needs. What Is the Value of Predictive Maintenance in Business? Businesses can experience a range of benefits from implementing predictive maintenance technology. Considering experts project its market value in 2030 — an increase of nearly $60 billion from 2020 — its potential is undeniable. will reach $64.3 billion Fewer Delays and Less Downtime Predictive maintenance helps businesses fix indicators of failure before they become an issue, resulting in fewer delays. Considering the cost of one hour of downtime from 2020 to 2022, this benefit is significant. increased by around 50% Lower Operating Costs Fewer critical equipment failures result in significant cost savings. Typically, reactive maintenance because businesses have to schedule technicians at the last minute, order replacement parts, and reassign employees. expenses are 50% higher Heightened Customer Satisfaction Customer satisfaction directly correlates to operational delays. After all, experiencing unexpected downtime due to equipment failure can’t deliver on time. Predictive maintenance helps them consistently meet people’s expectations. four in 10 businesses Improved Brand Reputation Businesses that use predictive maintenance substantially lower the risk of a defective part going to a customer, improving their brand reputation. In fact, they on average. Over time, this improvement could give them a competitive advantage. have 78.5% fewer product defects Increased Employee Productivity Predictive maintenance reduces the number of delays and instances of unplanned downtime businesses experience, ensuring their employees can work without interruption. As a result, they experience significant productivity gains. Lower Carrying Costs Businesses don’t have to keep as many replacement parts on hand when critical failures become less likely. This development resulting in as much as a 20% reduction in carrying costs. As an added bonus, it also saves valuable floor space. reduces inventory by up to 30%, When Should Businesses Use Predictive Maintenance? The ideal scenario for predictive maintenance implementation involves a business that repeatedly experiences critical equipment failures or spends a large percentage of its operational budget on machine upkeep. However, this strategy has broader applications. Any business that depends on one or more pieces of equipment to deliver services to customers, produce products or keep employees productive should consider implementing this predictive strategy. This way, they can minimize delays and avoid costly downtime. Once businesses have implemented predictive maintenance, it becomes an ongoing part of their operations — meaning their technology continuously checks for signs of equipment failure, and they respond as needed. Consequently, having technicians on hand is essential. After all, there’s no point in businesses having a technology that can predict an imminent equipment failure when they don’t put those insights to use as soon as possible. Tips for Implementing Predictive Maintenance Effectively Leveraging predictive maintenance can be challenging for those without prior experience. In fact, research shows think their current program needs improvement. Effective implementation begins with strategy. four in 10 businesses 1. Define a Business Use Case Businesses shouldn’t approach full-scale, immediate implementation unless they know exactly what they’re doing. Sometimes, incremental adoption is ideal. In this case, they should prioritize their assets to decide which needs this maintenance strategy the most. 2. Ensure Data Is Relevant While the temptation to use every scrap of data available can be strong, businesses should prioritize relevancy and accuracy above all else. For example, it’s not necessary to include a maintenance report if it’s from years ago when a machine had different components. 3. Audit the Algorithm The algorithm is maintaining the equipment, but who is maintaining it? Businesses should have an on-staff AI engineer or outsource upkeep to a third party to ensure their model remains accurate and effective after absorbing new information. 4. Align Skills With Needs Many businesses make the mistake of rushing implementation to reap the benefits of predictive maintenance as soon as possible. In these scenarios, their outcomes aren’t as good as they should be because their workforce’s skills don’t align with their new technical needs. The Bottom Line of Predictive Maintenance in Business Businesses leveraging predictive maintenance can benefit from reduced costs, increased customer satisfaction, and higher employee productivity. Notably, their implementation strategy must be sound and calculated to get the best return on investment.

How to Implement Predictive Maintenance in Your Business

AI has radically reshaped predictive analytics, making forecasting more precise than ever before. Businesses in all industries are taking advantage, capitalizing on AI’s wide-ranging capabilities to anticipate future trends and prepare for what’s coming around the corner. The accuracy of any forecast hinges on a number of factors: the sophistication of , the robustness of predictive models, and the quality and quantity of the data they’re trained on, to name a few. AI algorithms is a generative pre-trained model specifically designed for predicting time-series data. It’s a powerful asset that will allow you to forecast future outcomes with greater precision, giving you the foresight to capitalize on upcoming opportunities and dodge possible pitfalls. Nixtla’s TimeGPT Below, we’ll put TimeGPT under the microscope, analyze it from all angles, and show how you can integrate it with your database to maximize the full potential of predictive analytics. What is TimeGPT? TimeGPT is a pre-trained generative model for time-series data forecasting. Its core functionality is to predict future values in a time series based on historical data. The application processes data sequentially, using a windowing technique to analyze historical data points and recognize patterns. This enables it to predict future values by extrapolating these identified patterns. An overview of TimeGPT : Nixtla's TimeGPT applies pre-trained models to time-series data, using the data collected to predict new datasets without specific training, a departure from traditional models. Innovative Pre-training : TimeGPT excels in identifying complex patterns in time series data, surpassing many traditional models in trend, seasonality, and anomaly detection. Advanced Pattern Recognition : By using advanced neural networks, TimeGPT offers efficient forecasting, reducing the time and resources typically needed for time series analysis by eliminating dataset-specific retraining. Efficient Forecasting : Unlike static traditional models, TimeGPT has the potential for ongoing learning and adaptation, maintaining accuracy as new data emerges. Continuous Learning and Adaptation TimeGPT’s features and benefits As one of the leading predictive analytics models, TimeGPT offers advanced capabilities that provide a variety of unique features, making it a powerful asset for managing complex forecasting scenarios. These include: Anomaly Detection Leveraging predictive modeling techniques, TimeGPT excels in identifying unusual patterns or outliers in time series data, which are often indicative of significant but unexpected events. Feature: This feature is crucial for businesses and analysts in quickly detecting and responding to unusual occurrences, ensuring that forecasting models remain accurate and reliable, even in the presence of aberrations. Benefit: Exogenous Variables Handling TimeGPT can incorporate external factors or exogenous variables (like weather conditions for retail sales forecasting) into its analysis. Feature: This ability allows for more nuanced and accurate forecasts by considering the impact of external elements, which is particularly useful in scenarios where external factors significantly influence the data trends. Benefit: Fine-tuning While TimeGPT is a pre-trained model, it offers the capability for fine-tuning to adapt to specific datasets or unique forecasting needs. Feature: This feature enables users to tailor the model more closely to their specific requirements, enhancing the accuracy and relevance of the forecasts in specialized contexts. Benefit: Historical Forecasting TimeGPT can analyze past data to generate historical forecasts, allowing users to understand how the model would have predicted past events. Feature: This is particularly beneficial for validating the model’s accuracy and reliability, giving users confidence in its future predictions. Benefit: Managing complex forecasting scenarios TimeGPT’s features are especially beneficial when handling complex scenarios such as holidays, special dates, and irregular timestamps. These events can significantly impact trends in time series data. TimeGPT’s ability to detect anomalies and incorporate exogenous variables helps in accurately forecasting around these dates, considering their unique impact. Holidays and Special Dates: Time series data can sometimes be irregular or have missing points. TimeGPT’s sophisticated algorithms can handle such irregularities, ensuring consistent and accurate forecasting even with non-uniform data. Irregular Timestamps: Deploying TimeGPT models in MindsDB MindsDB is the platform for building custom AI from enterprise data. It works by connecting any source of data with any AI/ML model or framework and automating how real-time data flows between them. MindsDB bridges data sources with advanced AI/ML models like TimeGPT. This allows you to easily create, train, and manage these models, making it particularly beneficial to those who have minimal experience in machine learning. Integrating TimeGPT into MindsDB begins with creating a TimeGPT ML engine using the below statement and providing the TimeGPT API key. CREATE ML_ENGINE timegpt_engine
FROM timegpt
USING timegpt_api_key = ‘timegpt-api-key’; Next, users can employ the statement to create, train, and deploy the TimeGPT model. CREATE MODEL CREATE MODEL model_table
FROM data_source
  (SELECT * FROM data_table)
PREDICT column_to_be_predicted
ORDER BY date_column
GROUP BY column_name, column_name, ...
HORIZON 3 -- model forecasts the next 3 rows
USING ENGINE = 'timegpt_engine'; Now you can query the model to get forecasts: SELECT m.date_column, m.column_to_be_predicted
FROM data_table AS d
JOIN model_table AS m
WHERE d.date_column > LATEST; With MindsDB, you can automate forecasts and generate them constantly for any downstream usage. It is especially useful when you have dynamic data that gets updated regularly. Then, the model should be fine-tuned with the latest data to provide as accurate forecasts as possible. Here, we create a job that fine-tunes the model with the latest data and saves daily forecasts in a database table. CREATE JOB automate_forecasts (

   FINETUNE model_table
   FROM data_source
      (SELECT * FROM data_table);

   CREATE TABLE daily_forecasts_{{START_DATETIME}} (
      SELECT m.date_column, m.column_to_be_predicted
      FROM data_table AS d
      JOIN model_table AS m
      WHERE d.date_column > LATEST
   )
)
EVERY 1 day; To see a complete example, visit our . You can find more . docs page here forecasting use cases here How to use TimeGPT in predictive analytics Companies must be agile enough to react to minute-by-minute market changes. Using predictive analytics techniques, such as those enabled by TimeGPT, will help keep you a step ahead and even give you a competitive advantage. Businesses can utilize TimeGPT in several key areas, including: Financial Forecasting TimeGPT’s proficiency in processing and interpreting time-based data leads to more precise forecasts in the fluctuating realm of finance. Its ability to identify current trends and subtle nuances within financial datasets helps to create more informed investment strategies. Demand Prediction In industries like retail and manufacturing, TimeGPT aids in predicting product demand. This is crucial for inventory management, supply chain optimization, and strategic planning. By analyzing historical sales data and market trends, TimeGPT can forecast future demand patterns, helping businesses to prepare and adapt effectively. Anomaly Detection The model's approach to sequential learning proves advantageous in detecting anomalies, like spotting unusual patterns in network traffic for cybersecurity or identifying machine performance irregularities for predictive maintenance. This function is crucial in averting potential problems before they intensify. Predictive analytics use cases Here are just a few ways to use TimeGPT for accurate predictive analytics. TimeGPT performs well compared to traditional methods in stock price prediction, aiding investors and analysts. Stock Market Analysis: : It predicts healthcare trends, which is important for resource allocation and planning. Healthcare Trend Anticipation TimeGPT predicts future demands in logistics, improving efficiency and reducing costs. Supply Chain Optimization: It forecasts retail buying trends, aiding in inventory management and targeted marketing. Retail Customer Behavior Analysis: TimeGPT identifies potential equipment failures, minimizing downtime and maintenance costs. Manufacturing Predictive Maintenance: TimeGPT in the real world Global business is more complex and interconnected than ever. One false move in a single industry can have a ripple effect in several others, making it crucial for companies to leverage predictive analytics to foresee, prepare, and react to future events. With its advanced time-series analysis, deep learning capabilities, adaptive learning, and customizable scalable models, TimeGPT has proven to be an effective tool for more accurate predictive analysis. Integrating TimeGPT with MindsDB creates a powerful combination that enhances the precision and applicability of predictive insights directly within business databases. This gives you the agility to anticipate and react swiftly to a rapidly evolving global market. How would TimeGPT perform on your company’s data? Check out the . tutorial here Also published . here

How TimeGPT Transforms Predictive Analytics with AI

টাইমজিপিটি কীভাবে AI এর সাথে ভবিষ্যদ্বাণীমূলক বিশ্লেষণকে রূপান্তরিত করে

Wie TimeGPT Predictive Analytics mit KI transformiert

Cómo TimeGPT transforma el análisis predictivo con IA

Comment TimeGPT transforme l'analyse prédictive avec l'IA

TimeGPT कैसे AI के साथ पूर्वानुमानात्मक विश्लेषण को बदलता है

TimeGPT が AI を使用して予測分析をどのように変革するか

TimeGPT가 AI를 통해 예측 분석을 혁신하는 방법

Como o TimeGPT transforma a análise preditiva com IA

Как TimeGPT преобразует прогнозную аналитику с помощью ИИ

TimeGPT, Yapay Zeka ile Tahmine Dayalı Analitiği Nasıl Dönüştürüyor?

Cách TimeGPT biến đổi phân tích dự đoán bằng AI

TimeGPT 如何利用 AI 改变预测分析

Telecom's AI Revolution: How Predictive Analytics Optimizes CI/CD Pipelines

There is a new paper, , stating that, "Large language model (LLM)–based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated. ChatGPT achieves impressive accuracy in clinical decision-making, with increasing strength as it gains more clinical information at its disposal. In particular, ChatGPT demonstrates the greatest accuracy in tasks of final diagnosis as compared to initial diagnosis. Limitations include possible model hallucinations and the unclear composition of ChatGPT’s training data set." Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study Who is a medical doctor? Or, who is a human in society? A human is an acceptable member of society based on acts according to common agreements, learned by the person. A medical doctor is a professional who is formally trained with established knowledge in medicine. An important capability of the brain, it seems, is its ability to learn widely. Though, the property is prepared in genes, the sprawling connections of the brain that provide a broad learning ability and become expanded, makes humans dominant and exceed other organisms on the planet. Several organisms have similar biological make-ups as humans. They also possess several survival mechanisms. They do not have some of the properties for language, advanced intelligence and so forth. They are also able to learn, but their ability to learn does not match humans. This is obvious by the difficulty for some domesticated animals to recognize certain familiar stuff in another form sometimes, like a known person in a picture or video, or to get the concept of what devices are so forth. Learning is one of the ways the human brain broke from other organisms, even though humans still have to fulfill their biological needs. This learning, though limited, has changed society in places and over eras. There are relays in the brain that alter how what was learned is accessed for many, enabling how things are seen in another way from usual, pivoting towards advances. The learning superiority of the human brain now seems threatened, if not displaced already by LLMs. There are people who play up, and others who downplay, LLMs, they may be right or wrong. What LLMs should be measured against is what the brain does, in terms of the need for people in several purposes. LLMs predict the next word, which is a form of output of their learning of the likelihood of what that word should be. Sometimes they are accurate, other times they are not. They have, however, been able to crossover into key doings in human society, like in medicine, as the paper showed. There are areas where human intelligence is limited, like learning new languages for many adults, or some complex subjects or taking on a fresh field to becoming an expert. In these, LLMs excel, plus they are not given to variations of pleasure or pain, of human consciousness. How does the human brain learn? The two most prominent components in that process are the electrical and chemical impulses of nerve cells. These components, their features and interactions are proposed to be the human mind. Conceptually, as sets of electrical impulses interact with sets of chemical impulses, there are fills that occur in the loop of sets of chemical impulses that ensure that something is learned. There are other features of sets of impulses that may influence the reach of the fills, making learning easy or hard, depending. Since impulses are also involved in other processes, aside learning and only one interaction is prioritized on the mind in a moment, it is generally difficult to do so much, in general. For humans, modeling learning by sets of impulses, in a way towards reshaping education, in adulthood or rearing children could help to counter some of the strengths of AI, to displace lots of people.

Predictive Coding: What Is Special About the Human Brain? Not Much

Machine Learning for Predictive Maintenance: How It's Reinventing Asset Upkeep

Aprendizaje automático para el mantenimiento predictivo: cómo se está reinventando el mantenimiento de activos

Apprentissage automatique pour la maintenance prédictive : comment il réinvente l'entretien des actifs

पूर्वानुमानित रखरखाव के लिए मशीन लर्निंग: यह परिसंपत्ति रखरखाव को कैसे नया रूप दे रहा है

機械学習による予知保全: 資産維持の再発明

Aprendizado de máquina para manutenção preditiva: como está reinventando a manutenção de ativos

Học máy để bảo trì dự đoán: Cách tái tạo việc bảo trì tài sản

用于预测性维护的机器学习：如何重塑资产维护

Over the last decade, the growth and success of machine learning have been phenomenal, driven primarily by the availability of vast amounts of data and advanced computational power. This surge was brought about by the digitization of various sectors, leading to an explosion of digital data—everything from social media posts to online transactions and sensor data. Advances in machine learning techniques, particularly in deep learning, facilitated the development of more complex and versatile models. Consequently, machine learning applications have become ubiquitous, contributing to improved efficiencies and capabilities across numerous sectors, including healthcare, finance, and transportation. Working with these massive amounts of event-based data and deriving value from the events themselves and their context in time – other events happening nearby – is still difficult. Doing so in a real-time or streaming manner is harder still. You often must use complex, low-level APIs or work around the limitations of a higher-level query language designed to solve very different problems, like SQL. To tackle these challenges, we introduce a new abstraction for time-based data, called timelines. Timelines organize data by time and entity, offering an ideal structure for event-based data with an intuitive, graphical mental model. Timelines simplify reasoning about time by aligning your mental model with the problem domain, allowing you to focus on what to compute, rather than how to express it. In this post, we present timelines as a natural way to organize event-based data and extract value – directly, and as the inputs for machine learning and prompt engineering. We delve into the timeline concept, its interaction with external data stores (inputs and outputs), and the lifecycle of queries using timelines. This post is the first in a series about , an open-source event-processing engine designed around the timeline abstraction. We’ll follow up with an explanation of how Kaskada builds an expressive temporal query language on the timeline abstraction, how the timeline data model enables Kaskada to efficiently execute temporal queries, and, finally, how timelines enable Kaskada to execute incrementally over event streams. Kaskada Making sense of time Dealing with a large variety of events is difficult when treating each kind of event as a separate, unordered table of data – the way SQL views the world. It is difficult to understand what motivated Carla to make purchases, or what caused Aaron to engage with other users via messages. By organizing the data – by time and user – it becomes much easier to spot patterns. Aaron sends messages after winning. Carla makes purchases when frustrated by a series of losses. We also see that Brad may have stopped playing. By organizing the data – by time and user – it becomes much easier to spot patterns. Aaron sends messages after winning. Carla makes purchases when frustrated by a series of losses. We also see that Brad might have stopped playing. By organizing the events in a natural way – by time and user – we were able to identify patterns. This same organization allows us to express feature values computed from the events and use those to train and apply machine learning models or compute values to use within a prompt. The timeline abstraction Reasoning about time – for instance, cause-and-effect between events – requires more than just an unordered set of event data. For temporal queries, we need to include time as a first-class part of the abstraction. This allows reasoning about when an event happened and the ordering – and time – between events. Kaskada is built on the timeline abstraction: a multiset ordered by time and grouped by entity. Timelines have a natural visualization, shown below. Time is shown on the x-axis and the corresponding values on the y-axis. Consider purchase events from two people: Ben and Davor. These are shown as discrete points reflecting the time and amount of the purchase. We call these discrete timelines because they represent discrete points. The time axis of a timeline reflects the time of a computation’s result. For example, at any point in time we might ask “what is the sum of all purchases?” Aggregations over timelines are cumulative – as events are observed, the answer to the question changes. We call these continuous timelines because each value continues until the next change. Compared to SQL, timelines introduce two requirements: ordering by time and grouping by entity. While the SQL relation – an unordered multiset or bag – is useful for unordered data, the additional requirements of timelines make them ideal for reasoning about cause-and-effect. Timelines are to temporal data what relations are to static data. Adding these requirements mean that timelines are not a fit for every data processing task. Instead, they allow timelines to be a better fit for data processing tasks that work with events and time. In fact, most event streams (eg., Apache Kafka, Apache Pulsar, AWS Kinesis, etc.) provide ordering and partitioning by key. When thinking about events and time, you likely already picture something like a timeline. By matching the way you already think about time, timelines simplify reasoning about events and time. By building in the time and ordering requirements, the timeline abstraction allows temporal queries to intuitively express cause and effect. Using timelines for temporal queries Timelines are the abstraction used in Kaskada for building temporal queries, but data starts and ends outside of Kaskada. It is important to understand the flow of data from input, to timelines, and finally to output. Every query starts from one or more sources of input data. Each input – whether it is events arriving in a stream or stored in a table, or facts stored in a table – can be converted to a timeline without losing important context such as the time of each event. The query itself is expressed as a sequence of operations. Each operation creates a timeline from timelines. The result of the final operation is used as the result of the query. Thus, the query produces a timeline which may be either discrete or continuous. The result of a query is a timeline, which may be output to a sink. The rows written to the sink may be a history reflecting the changes within a timeline, or a snapshot reflecting the values at a specific point-in-time. Inputting timelines Before a query is performed, each input is mapped to a timeline. Every input – whether events from a stream or table or facts in a table – can be mapped to a timeline without losing the important temporal information, such as when events happened. Events become discrete timelines, with the value(s) from each event occurring at the time of the event. Facts become continuous timelines, reflecting the time during which each fact applied. By losslessly representing all kinds of temporal inputs, timelines allow queries to focus on the computation rather than the kind of input. Outputting timelines After executing a query, the resulting timeline must be output to an external system for consumption. The sink for each destination enables configuration of data writing, with specifics depending on the sink and the destination (see for more). this connector documentation There are several options for converting the timeline into rows of data, affecting the number of rows produced: Include the entire history of changes within a range or just a snapshot of the value at some point in time. Include only events (changes) occurring after a certain time in the output. Include only events (changes) up to a specific time in the output. A full history of changes helps visualize or identify patterns in user values over time. In contrast, a snapshot at a specific time is useful for online dashboards or classifying similar users. Including events after a certain time reduces output size when the destination already has data up to that time or when earlier points are irrelevant. This is particularly useful when re-running a query to materialize to a data store. Including events up to a specific time also limits output size and enables choosing a point-in-time snapshot. With incremental execution, selecting a time slightly earlier than the current time reduces late data processing. Both “changed since” and “up-to” options are especially useful with incremental execution, which we will discuss in an upcoming article. History The history – the set of all points in the timeline – is useful when you care about past points. For instance, this may be necessary to visualize or identify patterns in how the values for each user change over time. The history is particularly useful for outputting training examples to use for creating a model. Any timeline may be output as a history. For a discrete timeline, the history is the collection of events in the timeline. For a continuous timeline, the history contains the points at which a value changes – it is effectively a changelog. Snapshots A snapshot – the value for each entity at a specific point in time – is useful when you just care about the latest values. For instance, when updating a dashboard or populating a feature store for connecting to model serving. Any timeline may be output as a snapshot. For a discrete timeline, the snapshot includes rows for each event happening at that time. For a continuous timeline, the snapshot includes a row for each entity with that entity’s value at that time. Conclusion This blog post highlighted the importance of temporal features when creating ML models from event-based data. The time and temporal context of events is critical to seeing patterns in activity. This post introduced the timeline abstraction, which makes it possible to work with the events and the temporal context. Timelines organize data by time and entity, providing a more suitable structure for event-based data compared to multisets. The timeline abstraction is a natural progression in stream processing, allowing you to reason about time and cause-and-effect relationships more effectively. We also explored the flow of data in a temporal query, from input to output, and discussed the various options for outputting timelines to external systems. Rather than applying a tabular (static) query to a sequence of snapshots, Kaskada operates on the history (the change stream). This makes it natural to operate on the time between snapshots, rather than only on the data contained in the snapshot. Using timelines as the primary abstraction simplifies working with event-based data and allows for seamless transitions between streams and tables. You can experimenting with your own temporal queries today. and let us know what you think about the timeline abstraction. In an upcoming post, we’ll delve into the Kaskada query language and its capabilities in expressive temporal queries. get started Join the Slack community By Ben Chambers and Therapon Skoteiniotis, DataStax

Connecting Event-Based Data to Predictive AI in Real-Time

Conexión de datos basados en eventos a IA predictiva en tiempo real

Connecter les données basées sur les événements à l'IA prédictive en temps réel

रीयल-टाइम में ईवेंट-आधारित डेटा को प्रिडिक्टिव AI से कनेक्ट करना

イベントベースのデータをリアルタイムで予測 AI に接続する

Conectando dados baseados em eventos à IA preditiva em tempo real

Kết nối dữ liệu dựa trên sự kiện với AI dự đoán trong thời gian thực

将基于事件的数据实时连接到预测性 AI

In today’s episode of the Brains Byte Back podcast, we speak with , Vice President of Marketing at , a company that democratizes the use of AI and Machine Learning by making it simple for organizations to leverage the power of their data through fast, unique, and easy-to-use tools. Walter Paliska dotData In this episode, we discuss how the company first began, how it has grown, and the solutions it currently offers. We also explore predictive mining and how it helps businesses leverage historical data to make accurate predictions about future behavior or outcomes. data Paliska draws on the example of marketing, arguing that a company could use predictive data mining to predict the behavior of visitors on a website for personalized targeting. Additionally, Paliska shares how dotData was founded, after the company’s CEO and founder, Ryohei Fujimaki, noticed that his team of data scientists would spend a huge amount of time on the feature engineering process. Since this was the most time-consuming and manual part of the data science process, Fujimaki decided to utilize automation to solve this problem, thus dotData was born. Paliska also shares how the company helps clients save time and cost on data science projects, democratizing data science for non-data scientists, and improving the accuracy and speed of models. machine learning He argues that these are just a few of the ways dotData distinguishes itself from the competition. But Paliska advocates that one of the biggest differentiators is the core engine of dotData, which automates the feature engineering process. Feature engineering is a vital step in building effective machine-learning models, but it is also a complex process that can be very time heavy. Normally, data scientists would spend months building feature tables that would then be entered manually into the machine learning algorithms. However, dotData automates this process, identifying the connections between different tables and building feature tables automatically. According to Paliska, this approach allows dotData to stand out and is one of the key reasons why the company has achieved such a high level of success in the market. Watch the Podcast Video https://www.youtube.com/watch?v=46zOpjfiU0s&embedable=true You can listen on , , , , , , , , , and Spotify Anchor Apple Podcasts Breaker Google Podcasts Stitcher Overcast Listen Notes PodBean Radio Public. Alternatively, you can find a transcript below: My name is Walter Walter Paliska. I’m the VP of Marketing for dotData. I’ve been with dotData since May of 2019. So almost four years now. And dotData is a leading provider or data science automation solutions, we have largely broadly speaking to customer types that we tend to target, experienced assigned teams primarily in larger organizations that are looking to automate the feature engineering part of their work. And the other one are more companies that are just getting started in the world of predictive analytics and in the world of data science, and are looking for automation solutions to try and empower non-data scientists in the process of building machine learning models and data science processes integrated to do predictive analytics. Walter: Awesome, fantastic. Well, thank you so much for joining me today. It’s a pleasure to have you here. And I’m really curious to know like when and how to dotData first stop? Sam: Yeah, so great question. So actually, the dotData story goes back quite a ways. Ryohei Fujimaki the CEO and founder of dotData, is a former employee of NEC in Japan. So he was actually what’s known as an NEC fellow Research Fellow. Now, there haven’t been that many in the history of the company, and he sees quite an old company goes back well over 100 years. And I forget the exact number, I don’t want to lie to you. But I know that, you know, there’s been very few NEC research fellows in the history of the company, he was the youngest one ever in the history of the company. And he was part of their data science team, and pretty much trend data, their data science organization from a services perspective, right. So they would do project based work going to accounts. And the idea behind dotData actually originated. Through the course of his experience with NEC, one of the things that he consistently kept noticing was that his team of data, scientists would always spend an inordinate amount of time on specific parts of the data science process. And getting a little bit into the technical weeds here. But there’s one part of specialty known as feature engineering, which is really the most time consuming the most manual part of process. And he kept seeing that they would literally be spending months on the feature engineering process. And they would still be at a point where they hadn’t even played with any machine learning algorithms yet to figure out what the model should look like. And you know, that’s obviously an aha moment, at some point that tells you there’s something there. So there’s a need here, there’s automation could perhaps solve that problem. And that’s where the idea behind dotData originated in the company was founded as a spin off from NEC Corporation in 2018. In Japan, and so originally born in Japan, but fully headquarter nine, United States, we have all of our headquarters, if you have staff and pretty distributed company. We’re worldwide we have people in Europe, we have people in Japan, with people in the United States. And that brings us to where we are today. Walter: Awesome. That’s a fantastic success story. And I’m also very curious to know what is the story behind the name dotData because for our listeners, it’s spelled like dot d o t, but with a lowercase d, and then data with a capital D right after it. Where did that come from? Sam: Great question. So the original inspiration behind the name dotData really comes from, you know, one of the ideas that they were the group of people to found that a company was toying with was, you know, we’re talking like, probably when this idea was first being kicked around 2016 2017. So there were a lot of conversations in the world going on about data and the volume of data and how much data was being generated around the world on a daily basis. And it sort of dawned on them, you know, the previous iteration of the internet, so to speak, in the 1990s, and the 2000s. Were all about dotnet. Right? And the network, and they thought, well, the next iteration of the world is really going to be about data. So that data, so instead of.net dot data. So that was the original intent behind the dotData name. Walter: Okay, yeah, that makes sense with a lot more clarity. With that in mind. I also really do love the alliteration of it the D D, the dot data, it’s got a really nice sound to it when you say Sam: Yeah it is a memorable name, and it’s, it’s easy to mark it. Walter: Yeah, yeah, I completely get that. And I also want to know, like, in November last year, you folks there at dotData published an article called what is predictive data mining? Obviously, I would highly recommend listeners go check it out. But while you’re here, like could you give us a brief overview regarding what predictive data mining is? Sam: Sure, absolutely. So obviously, you know, from a perspective of the audience, those that are not familiar with it, you know, you may have heard of it in different terminology may have heard of referred to as predictive analytics, predictive data mining data mining by itself, technically, they’re not quite the same thing, if you really want to split hair, but for, you know, sort of a broad audience, predictive data mining and predictive analytics are really about leveraging historical data that you have in your organization. You know, for example, a good use case might be in marketing, you might want to predict behavior of visitors on your website for a shopping cart, right? And you have historical data about, you know, what are the actions that certain people take before they purchase a specific product. And you want to use that data, you want to mine that data, and use very specific techniques and algorithms things like and you know, decision tree analysis or rule induction, clustering, outlier detection and other types of data mining techniques, to identify the patterns, determine, you know, sort of visit the insights, part of predictive analytics, build those insights. These are also sometimes referred to as features in the world of machine learning, but figure out the insights that tell you okay, this is what tends to happen. Every time somebody buys a particular product in the example we just had, and then taking it one step further and saying, Okay, now I can use certain machine learning algorithms to try and predict a probability of somebody purchasing a product when they take certain specific actions. The reason that’s important you as a marketer for the example that I just gave is, if I can predict with a certain degree of accuracy, what’s going to happen when somebody takes certain specific actions, I can drive people towards those actions, I can now leverage that information to optimize my marketing campaigns. So that’s predictive data mining in a nutshell, it gets a lot more complicated very quickly, there’s a lot more to talk about. So I’ve given you literally the marketing guys 32nd version, a lot more information available on our website, and we’re happy to meet with anybody that obviously wants to learn more about this. We’re very keen on educating the market as much as possible about this. Walter: Yeah, I can imagine that can get pretty complicated pretty quickly. So I really do appreciate you giving us that brief overview. And I think you did a good job of summarising what appears to be a very complex topic. Now, I also want to know like, are there other companies operating this space? And if so, like, how do you folks at dotData, differentiate yourself from the competition? Sam: Great question. So the short answer is yes, of course, there are plenty of other companies that operate in this space. And having, you know, having said that, the one thing that is probably also a truism about the Machine Learning slash predictive analytics space, is that it is developing and is changing at an incredibly fast pace. So if you look at the positioning of companies, say, three, four years ago, when I first joined up dotData, versus how those same companies are positioning themselves today, how their products are built today, where they do today, radically different conversations. And that’s really been driven largely by how quickly the market is developing. Through all of that, however, the one huge differentiator for that data really comes down to the core engine of dotData and how dotData works. So one of the things that I haven’t, we haven’t talked about, and again, this gets a little bit technical depth of this conversation, but it’s important is that in the world of predictive analytics, right, when you go and use these machine learning algorithms to build your predictive models, these machine learning algorithms like flat tables, essentially, they’re not happy. So if you know if you don’t know how much you know about enterprise data, but especially in the world of enterprise data, if you think about something like salesforce.com, for example, as a user of salesforce.com, I just see a leads screen and it has lead information. And as my activities against my leads, it’s all in one location. But if I take the covers off of that, and look underneath in the guts, so to speak, of how this system operates, it’s basically what’s called a relational database. So all of those fields that I’m seeing many of them are really parts of different tables delivered in different parts of salesperson, they’re all connected together. Well, machine learning algorithms don’t like those things, machine learning algorithms, like flat tables, machine learning algorithms, like things that look like CSVs looks like spreadsheets. So a big part of machine learning is what’s called Feature engineering, which is essentially a process of taking these complex relational data tables, figuring out the patterns that make sense for your machine learning algorithm and building these flat tables essentially, that you then have to feed into machine learning algorithms. The biggest core differentiator for dotData is that we do that part automatically. That’s traditionally a very hands on process. If I go back to when you asked me the very first question about, you know how that data originated. That was the aha moment that our CEO had was watching In these data scientists spent literally months on building these feature tables that they would then have to manually put into the machine learning algorithms. And realizing there’s got to be a better way, we have to be able to build a system that will automatically find the connections between these tables automatically identify the patterns that are relevant and purposeful, and build these feature tables automatically. So that’s by far our biggest differentiator. And today, we’re really the only company in the market that provides that functionality. Walter: Okay, that makes sense. Yeah. And I always love the fact that whenever I interview people, it always seems like there is some kind of aha moment at the basis of all of these companies I speak with. And it’s really, it’s a really fun part of my job getting to that core drive, I guess, so that makes absolute sense. And I’m curious to know, what is next on the horizon for you folks at dotData? Sam: : So I think from, you know, sort of a couple of perspectives, right. One is from a, obviously, as a business growth is the biggest area of interest for us. And actually, one of the things that we think, especially given the economic uncertainty that’s happening right now, systems like that data actually become even more beneficial to organizations. You know, when money went investment, capital was plentiful, and organizations didn’t have to worry about headcount and didn’t have to worry about, you know, you could just hire to solve the problem, right? You needed to do things faster, you hire more data scientists, you needed to build products faster, you hired more data engineers, and so on. Well, with the economy doing what it’s doing right now, we’re actually seeing an uptick in demand, we’re actually seeing more companies saying, I don’t have the ability to expand my team, I don’t have the permission, so to speak financially, to go and hire 1015 20 More data scientists. So how do I make my existing team more productive. And that’s where dotData can give them a lot of help. So we see a lot of opportunity in the short term, as well as a long term from that perspective. And obviously, we have a whole lot of ideas and a whole lot of new things coming down the line from a product perspective, most of which I can’t really talk about just yet, but some very exciting things that are coming in the second half of this year, that will continue to expand on the capabilities of the product and also take us into some new areas we haven’t been in before. Walter Fantastic. Well, it sounds like you folks have a lot going on. And I wish you the best of luck with that. And if people are listening, and they’re interested in keeping up with you, personally, Walter or dotData, where can they go to do that? Sam: Great question. So that dotData is easiest one, just go dotdata.com. To connect with me personally, you can find me on the leadership page. If you go to our about page and then leadership, you’ll see my picture my bio, and you can click directly to my LinkedIn profile, or my LinkedIn profile is simple enough. It’s just https://www.linkedin.com/in/walterpaliska/ go to my LinkedIn profile and just reach out to me. Walter: : Excellent. Well, we’ll include links as well in the description of this episode, so listeners can go there. But otherwise, Walter, thank you so much for joining me today. Sam Thank you very much for the opportunity, and thank you to all your listeners. Walter: This article was originally published by Sam Brake Guia on The Sociable The lead image for this article was generated by HackerNoon's via the prompt "people on computers in an internet cafe at nighttime". AI Image Generator

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Welcome back to POWER LEADER with our next expert, Alex Song, Founder and CEO of . Coming from the world of earned media relations, I am intrigued by my marketing counterparts and how machine learning is impacting their roles. Proxima The direct-to-consumer (DTC) audience further segmented by those who are iOS users is ripe for disruption. Song and his team have “cracked the code” on the post-iOS 14.5 world pairing proprietary data with machine learning to impact predictive AI in advertising. Let’s jump in. SE:  What innovations are you bringing to your industry? What sets you apart from your competitors? AS: Digital advertising continues to be a massive industry, but Apple’s iOS privacy
updates and skyrocketing Facebook advertising costs are making it much more
difficult for companies to scale and thrive online. More than 60% of iOS users have
opted out of iOS app tracking, which eliminates the signal between advertisers and
consumers. That major shift makes for a punishing advertising environment, with the cost of customer acquisition increasing by 50% over the last two years.  Now companies must spend more for less impressive results. As a DTC brand founder who managed to exit in 2021, I experienced the challenges
that were brewing in the digital advertising ecosystem due to a plethora of cheap
capital and the rising costs of customer acquisition. I started Proxima, an AI
audience technology, in 2022 to help digital businesses navigate these challenges
by providing better targeting and performance across media platforms such as
Google, Meta and TikTok at scale. Using predictive AI, Proxima leverages anonymized, first-party data and more than
55 million unique shopper personas from a network of 13,000 brands to reach new
customers, lower customer acquisition costs and increase ROI on ad spend. SE:  What is a recent win or announcement your company just celebrated? Why does it matter? I’m very excited to share that on April 19, 2023, Proxima to enable increased profitability and performance at scale across major AS: launched its self-service platform advertising platforms. Our clients say best why it matters.  Kasper Garnell, co-founder and CMO of Joe and The Juice said, “Proxima is our unique advantage to prospecting effectively post iOS14.5. Proxima is super easy to use and an incredibly powerful tool for profitable customer acquisition.” Ooshma Garg, founder and CEO of Gobble, reports that “Proxima has been a game-changer for customer acquisition. Their AI consistently finds qualified, high- value subscribers and has reduced our CPA by over 30%, giving us the confidence to scale our ads.” SE:  What are your company’s industry major challenges right now? Privacy updates and sky-high advertising costs on Facebook and Instagram, once the launch pad for thousands of small DTC businesses, are making it increasingly difficult for digital companies to target new audiences and acquire new customers efficiently. AS: Additionally, economic conditions have wreaked havoc on many major tech companies which are bellwethers for the rest of the industry — and capital is in shorter supply and more expensive. This makes cost efficient customer acquisition and establishing profitable unit economics more critical than ever. SE:  What stories aren’t currently being told about your industry that your audience should know? Meta’s Advantage+, a program touted as optimizing full ad campaigns with machine learning, is increasingly tipped in favor of Meta’s big spenders.  In fact, Advantage+ is the beginning of a significant strategic shift for platforms like AS: Facebook and Instagram. Our data reveals that larger brands, with the ability to spend more on Advantage+ campaigns, are reaping disproportionate benefits compared to smaller brands on Meta. More specifically, while Advantage+ campaigns were more expensive across the board, they were 26% more expensive for smaller brands, but only 4.7% more expensive for large brands. We can help digital companies of all sizes overcome this challenge with improved
targeting and more efficient results. SE:  How do you envision your business changing in the next 3-5 years? It’s very hard to predict with any certainty because we must see where macroeconomic conditions take us.  My mantra is to prepare for the worst and hope for the best. During the pandemic there was high demand for digital solutions, which lulled digital marketers into believing they should expect high returns for the foreseeable future. AS: But then the economy changed and Apple’s new approach to app tracking happened. The digital advertising community as a whole also has to ride the wave with the performance and stability of current media platforms (Twitter implosion anyone?) and the emergence of new ones. Our vision as a business is to be the essential growth platform for all digital businesses. In the near term that means optimizing our audience solution and further leaning into AI, one of the most revolutionary technologies we’ve ever seen. In the mid to longer term, we must build a comprehensive product offering that supports the livelihood of our digital customers across all the most important facets of their business health. SE:  What changes do you think are necessary for your industry to stay competitive in current economic times? Keeping pace with AI, for one.  AI is changing the face of business and society — you can’t turn around without reading about OpenAI and Chat-GPT, for example. Predictive AI’s impact on digital advertising will continue to be massive for targeting new high-value customers and scaling advertising budgets profitably. AS: Advertisers need to partner with companies like Proxima that can offer them the right AI tools and techniques. Another thought: I strongly urge my clients to take calculated risks. You must budget for testing — in other words allot small investments to testing new media platforms.  You never know where the next social media arbitrage opportunity will be, so you should test small ad budgets on new entrants to understand their impact on customer acquisition.  You won’t stay relevant if you don’t explore new waters. SE:  What measures has your organization taken to ensure you're driving towards the most important goals? We are a mission driven organization that invests significantly in ensuring alignment of our most valuable assets - our people. Ideas are great but it’s the execution that enables businesses to reach truly remarkable milestones. Our people drive that execution which means prioritizing them above all else. AS: This shows up in the culture we’re building and values we operate within. Culture is not perks or parties, culture is the operating system that your team commits to when leadership isn’t present. We have many important values but two that I emphasize are Transparency and Grit.  We believe deeply in transparency and providing an environment where candid feedback is the norm and company performance is visible to all. We seek out individuals that take extreme ownership over their responsibilities and embrace the grind of startups with levels of grit that are unmatched.  We’re just getting started but it’s so important to ensure that we’re starting with the right people in the right seats.

POWER LEADER: Predictive AI in Advertising with Proxima's Alex Song

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