📊 How to avoid data chaos? Ways to ensure consistency of metrics in a warehouse
If you work with analytics, you have probably encountered a situation where the same metric is calculated differently in different departments. This leads to confusion, reduces trust in the data, and slows down decision-making. The new article discusses the key reasons for this problem and two effective solutions.
🤔 Why do metrics diverge?
The reason lies in the spontaneous growth of analytics:
🔹 One analyst writes a SQL query to calculate the metric.
🔹 Then other teams create their own versions based on this query, making minor changes.
🔹 Over time, discrepancies arise, and the analytics team spends more and more time sorting out inconsistencies.
To avoid this situation, it is worth implementing uniform metric management standards.
🛠 Two approaches to ensure consistency
✅Semantic Layer
This is an intermediate layer between data and analytics tools, where metrics are defined centrally. They are stored in static files (e.g. YAML) and used to automatically generate SQL queries.
💡 Pros:
✔️ Flexibility: adapts to different queries without pre-creating tables.
✔️ Transparency: uniform definitions are available to all teams.
✔️ Relevance: data is updated in real time.
⚠️ Cons:
❌ Requires investment in infrastructure and optimization.
❌ May increase the load on calculations (but this can be solved by caching).
📌 Example of a tool: Cube.js is one of the few mature open-source solutions.
✅Pre-Aggregated Tables
Here, tables with pre-calculated metrics and fixed dimensions are created in advance.
💡 Pros:
✔️ Simple implementation, convenient for small projects.
✔️ Saving computing resources.
✔️ Full control over calculations.
⚠️ Cons:
❌ Difficult to maintain as the number of users increases.
❌ Discrepancies are possible if metrics are defined in different tables.
🤔 Which method to choose?
The optimal approach is hybrid use:
🔹 Implement a semantic layer for scalability.
🔹 Use pre-aggregated tables for critical metrics where minimal computation cost is important.
🔎More details here
If you work with analytics, you have probably encountered a situation where the same metric is calculated differently in different departments. This leads to confusion, reduces trust in the data, and slows down decision-making. The new article discusses the key reasons for this problem and two effective solutions.
🤔 Why do metrics diverge?
The reason lies in the spontaneous growth of analytics:
🔹 One analyst writes a SQL query to calculate the metric.
🔹 Then other teams create their own versions based on this query, making minor changes.
🔹 Over time, discrepancies arise, and the analytics team spends more and more time sorting out inconsistencies.
To avoid this situation, it is worth implementing uniform metric management standards.
🛠 Two approaches to ensure consistency
✅Semantic Layer
This is an intermediate layer between data and analytics tools, where metrics are defined centrally. They are stored in static files (e.g. YAML) and used to automatically generate SQL queries.
💡 Pros:
✔️ Flexibility: adapts to different queries without pre-creating tables.
✔️ Transparency: uniform definitions are available to all teams.
✔️ Relevance: data is updated in real time.
⚠️ Cons:
❌ Requires investment in infrastructure and optimization.
❌ May increase the load on calculations (but this can be solved by caching).
📌 Example of a tool: Cube.js is one of the few mature open-source solutions.
✅Pre-Aggregated Tables
Here, tables with pre-calculated metrics and fixed dimensions are created in advance.
💡 Pros:
✔️ Simple implementation, convenient for small projects.
✔️ Saving computing resources.
✔️ Full control over calculations.
⚠️ Cons:
❌ Difficult to maintain as the number of users increases.
❌ Discrepancies are possible if metrics are defined in different tables.
🤔 Which method to choose?
The optimal approach is hybrid use:
🔹 Implement a semantic layer for scalability.
🔹 Use pre-aggregated tables for critical metrics where minimal computation cost is important.
🔎More details here
Start Data Engineering
How to ensure consistent metrics in your warehouse – Start Data Engineering
If you’ve worked on a data team, you’ve likely encountered situations where multiple teams define metrics in slightly different ways, leaving you to untangle why discrepancies exist. The root cause of these metric deviations often stems from rapid data utilization…
📊 FinMind — world-class open financial data for analysis and learning
FinMind is not just a collection of quotes, but an entire ecosystem of financial data available for free and open source. The project is aimed at researchers, students, investors and enthusiasts who need access to high-quality, up-to-date data without having to pay for expensive subnoscriptions, such as Bloomberg Terminal or Quandl.
🔍 What you can find in FinMind:
📈 Historical and intraday stock quotes (tick data, candlesticks, volumes)
📊 Financial metrics: PER, PBR, EPS, ROE, etc.
💵 Dividends, company reports, revenue
📉 Options and futures data
🏦 Central bank interest rates, inflation
🛢 Commodity markets and bonds
🧠 Features:
✅Data is regularly updated automatically
✅Convenient and easy-to-learn Python API
✅Documentation and training examples in English and Chinese
✅The ability to quickly build a backtest or conduct market research
💡FinMind is ideal for:
✅Training courses on time series analysis, econometrics, ML in finance
✅Prototyping strategies, without risk and cost
✅University research and hackathons
🤖 GitHub
FinMind is not just a collection of quotes, but an entire ecosystem of financial data available for free and open source. The project is aimed at researchers, students, investors and enthusiasts who need access to high-quality, up-to-date data without having to pay for expensive subnoscriptions, such as Bloomberg Terminal or Quandl.
🔍 What you can find in FinMind:
📈 Historical and intraday stock quotes (tick data, candlesticks, volumes)
📊 Financial metrics: PER, PBR, EPS, ROE, etc.
💵 Dividends, company reports, revenue
📉 Options and futures data
🏦 Central bank interest rates, inflation
🛢 Commodity markets and bonds
🧠 Features:
✅Data is regularly updated automatically
✅Convenient and easy-to-learn Python API
✅Documentation and training examples in English and Chinese
✅The ability to quickly build a backtest or conduct market research
💡FinMind is ideal for:
✅Training courses on time series analysis, econometrics, ML in finance
✅Prototyping strategies, without risk and cost
✅University research and hackathons
🤖 GitHub
GitHub
GitHub - FinMind/FinMind: Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/
Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/ - FinMind/FinMind
You receive data from an external API with an unstable structure. What would you do in preprocessing so that the entire pipeline does not crash?
Anonymous Poll
16%
I write a schema-validator with default values and logging
16%
I wrap parsing in try/except with sending alerts in case of anomalies
40%
I save “raw” data separately so that it can be re-parsed during revision
28%
I make mapping API → internal schema via an adapter or ETL layer
📚Winning with Data Science — a guide for business leaders in the digital age
Authors: Howard Steven Friedman and Akshay Swaminathan
🔍 What is the book about?
The book Winning with Data Science is not about writing Python code or building neural networks. It is about how businesses can get real value from data science projects, even if you are not a techie.
The authors explain how to be a competent “customer” of analytical solutions: ask the right questions, understand the stages of a data science project, participate in the formation of requirements and evaluation of results.
💡 Key ideas of the book:
✅Data Science ≠ magic. It is a tool for solving specific business problems, and not a reason to chase hype.
✅The role of business is key. The business customer must clearly understand the problem, constraints, and desired outcome.
✅It is important to ask the right questions. Not "which algorithm is better?", but "will this help achieve the goal on time and on budget?"
✅Technical skills are not required. But basic knowledge of data, model types, storage, qualities, and capabilities is desirable.
✅Ethics is not the least important. Models should not reinforce biases or discriminate.
🧠 Who will benefit from this:
✅Managers implementing analytics
✅Product managers and startup founders
✅Marketers and CFOs
✅Anyone who wants to effectively interact with data science teams
🤔General conclusion:
This book is a bridge between business and analysts. Without an overload of terms, but with a deep understanding of the processes.
Authors: Howard Steven Friedman and Akshay Swaminathan
🔍 What is the book about?
The book Winning with Data Science is not about writing Python code or building neural networks. It is about how businesses can get real value from data science projects, even if you are not a techie.
The authors explain how to be a competent “customer” of analytical solutions: ask the right questions, understand the stages of a data science project, participate in the formation of requirements and evaluation of results.
💡 Key ideas of the book:
✅Data Science ≠ magic. It is a tool for solving specific business problems, and not a reason to chase hype.
✅The role of business is key. The business customer must clearly understand the problem, constraints, and desired outcome.
✅It is important to ask the right questions. Not "which algorithm is better?", but "will this help achieve the goal on time and on budget?"
✅Technical skills are not required. But basic knowledge of data, model types, storage, qualities, and capabilities is desirable.
✅Ethics is not the least important. Models should not reinforce biases or discriminate.
🧠 Who will benefit from this:
✅Managers implementing analytics
✅Product managers and startup founders
✅Marketers and CFOs
✅Anyone who wants to effectively interact with data science teams
🤔General conclusion:
This book is a bridge between business and analysts. Without an overload of terms, but with a deep understanding of the processes.
i.twirpx.link
Скачать Friedman Howard S., Swaminathan Akshay. Winning with Data Science: A Handbook for Business Leaders [PDF]
Columbia Business School Publishing, 2024. 272 р. ISBN 13: 978-0231206860. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science…
🎓 How Students Use AI at University — A Real-World Study of 1,000,000 Sessions from Anthropic
AI is increasingly permeating education, but most discussions have so far been based on surveys and lab experiments. In a new report, the Anthropic team conducted one of the largest studies of real-world student AI use, analyzing over 1 million anonymized conversations with Claude.ai.
🤔 Who Uses AI the Most?
✅Computer Science students are the clear winner: they make up 36.8% of all conversations with Claude, although in fact they only make up 5.4% of US graduates.
✅STEM disciplines dominate overall — students in science, math, and engineering use AI far more than students in business, medicine, or the humanities.
✅ Business makes up almost 19% of all degrees, but only 8.9% of all AI sessions. The picture is similar in healthcare and humanities.
🤔 Why students turn to AI: the most popular queries
✅Creating and editing educational materials - 39.3%
✅Solving assignments, coding, explaining theory - 33.5%
✅Analyzing and visualizing data - 11%
✅Assistance in research and tool development - 6.5%
✅Translations, proofreading, creating diagrams - the remaining percentages.
🤔How exactly do students interact with AI?
✅Direct problem solving
✅Direct content creation
✅Collaborative problem solving
✅Collaborative content creation
💡Interestingly, all 4 types are found approximately equally often (23–29% each).
⚠️ The issue of academic honesty and thinking
The study showed that Claude most often performs high-level cognitive functions according to Bloom's taxonomy in conversations with students:
✅Creation — 39.8%
✅Analysis — 30.2%
✅Application, understanding, and memorization — much less often
⚡️ All this turns the familiar "Bloom's pyramid" upside down — and raises concerns: are students starting to "delegate" critical thinking operations to AI too early?
💡This study is just the beginning of a long journey, but it already provides a lot of food for thought for teachers, students, and university administrators.
💻The full text of the article is here
AI is increasingly permeating education, but most discussions have so far been based on surveys and lab experiments. In a new report, the Anthropic team conducted one of the largest studies of real-world student AI use, analyzing over 1 million anonymized conversations with Claude.ai.
🤔 Who Uses AI the Most?
✅Computer Science students are the clear winner: they make up 36.8% of all conversations with Claude, although in fact they only make up 5.4% of US graduates.
✅STEM disciplines dominate overall — students in science, math, and engineering use AI far more than students in business, medicine, or the humanities.
✅ Business makes up almost 19% of all degrees, but only 8.9% of all AI sessions. The picture is similar in healthcare and humanities.
🤔 Why students turn to AI: the most popular queries
✅Creating and editing educational materials - 39.3%
✅Solving assignments, coding, explaining theory - 33.5%
✅Analyzing and visualizing data - 11%
✅Assistance in research and tool development - 6.5%
✅Translations, proofreading, creating diagrams - the remaining percentages.
🤔How exactly do students interact with AI?
✅Direct problem solving
✅Direct content creation
✅Collaborative problem solving
✅Collaborative content creation
💡Interestingly, all 4 types are found approximately equally often (23–29% each).
⚠️ The issue of academic honesty and thinking
The study showed that Claude most often performs high-level cognitive functions according to Bloom's taxonomy in conversations with students:
✅Creation — 39.8%
✅Analysis — 30.2%
✅Application, understanding, and memorization — much less often
⚡️ All this turns the familiar "Bloom's pyramid" upside down — and raises concerns: are students starting to "delegate" critical thinking operations to AI too early?
💡This study is just the beginning of a long journey, but it already provides a lot of food for thought for teachers, students, and university administrators.
💻The full text of the article is here
Anthropic
Anthropic Education Report: How University Students Use Claude
AI systems are no longer just specialized research tools: they’re everyday academic companions. As AIs integrate more deeply into educational environments, we need to consider important questions about learning, assessment, and skill development. Until now…
🌍 Geospatial Reasoning from Google: AI that understands geodata — and solves real problems
What if AI could not just "see" satellite images, but also understand what was happening on them? Google has launched a new large-scale project Geospatial Reasoning, combining powerful foundation models and generative AI to accelerate the analysis of geospatial data. This is not about theory, but about real-life scenarios: from assessing damage after a hurricane to improving urban planning and climate adaptation.
🔎 What's under the hood of Geospatial Reasoning?
✅Population Dynamics Foundation Model (PDFM) — a model that simulates population behavior and interaction with the environment;
✅Mobility model by trajectories — for tracking and analyzing movements;
✅New foundation models for remote sensing — trained on a huge array of satellite and aerial images with annotations.
🧠 How does it work?
The Geospatial Reasoning project allows you to combine the capabilities of Google models with your own data and create agent-based workflows. For example, after a hurricane, the system can:
✅Compare before and after images,
✅Identify which buildings are damaged,
✅Calculate estimated economic damage,
✅Assess the social vulnerability of affected areas,
✅Form priorities for assistance
🚀 Who has already joined?
✅Airbus — plans to use models to quickly analyze trillions of pixels of satellite data;
✅Maxar — integrates foundation models into its "living map of the Earth";
✅Planet Labs — accelerates the extraction of geoinsights for businesses and government agencies;
✅WPP (Choreograph) — uses PDFM to enhance media analytics based on behavioral patterns
📌 Why is this important?
Geodata is one of the most complex and potentially useful classes of information. There is a lot of it, it is heterogeneous and often requires high expertise to analyze.
💻 Read more in this article
What if AI could not just "see" satellite images, but also understand what was happening on them? Google has launched a new large-scale project Geospatial Reasoning, combining powerful foundation models and generative AI to accelerate the analysis of geospatial data. This is not about theory, but about real-life scenarios: from assessing damage after a hurricane to improving urban planning and climate adaptation.
🔎 What's under the hood of Geospatial Reasoning?
✅Population Dynamics Foundation Model (PDFM) — a model that simulates population behavior and interaction with the environment;
✅Mobility model by trajectories — for tracking and analyzing movements;
✅New foundation models for remote sensing — trained on a huge array of satellite and aerial images with annotations.
🧠 How does it work?
The Geospatial Reasoning project allows you to combine the capabilities of Google models with your own data and create agent-based workflows. For example, after a hurricane, the system can:
✅Compare before and after images,
✅Identify which buildings are damaged,
✅Calculate estimated economic damage,
✅Assess the social vulnerability of affected areas,
✅Form priorities for assistance
🚀 Who has already joined?
✅Airbus — plans to use models to quickly analyze trillions of pixels of satellite data;
✅Maxar — integrates foundation models into its "living map of the Earth";
✅Planet Labs — accelerates the extraction of geoinsights for businesses and government agencies;
✅WPP (Choreograph) — uses PDFM to enhance media analytics based on behavioral patterns
📌 Why is this important?
Geodata is one of the most complex and potentially useful classes of information. There is a lot of it, it is heterogeneous and often requires high expertise to analyze.
💻 Read more in this article
research.google
Geospatial Reasoning: Unlocking insights with generative AI and multiple foundation models
😎Top Dataset Search Engines and Data Warehouses
✅Google Dataset Search - opens access to free public datasets. You can select data on various topics and in various formats, including .pdf, .csv, .jpg, .txt and others. Using it is as easy as a regular Google search: just type the name or topic you are interested in into the search bar. As you type, the system will suggest datasets with the necessary keywords - you may accidentally stumble upon something new and interesting.
✅World Bank Open Data - open data from the World Bank is considered one of the most extensive and diverse sources of statistical information and public datasets. You can search for data by various categories. The World Bank website is unique in that it offers free resources and tools for public use, such as Data Bank, a convenient tool for analyzing and visualizing large data sets
✅Data.world - this platform allows you to access free data sets and work with them directly on the site. All you need to do is create a free account, after which you will have access to 3 free projects. If necessary, you can upgrade to paid plans with a larger storage volume. Using the search bar, you can find keywords, resources, organizations, or users. And for a more precise search, you can use the “Create advanced filter” button to find exactly what you need.
✅DataHub is a data publishing platform (SaaS) developed by Datopian, where you can browse one of the most diverse collections of public data sets organized by topic. The platform also has a blog with materials on topics related to Big Data Science.
✅Humanitarian Data Exchange - a platform for searching datasets. Here you can search for free datasets and filter the results by criteria such as location, format, organization, and license. The platform also allows you to share data by different categories.
✅UCI Machine Learning Repository - the least extensive of all the resources mentioned, it remains useful for those who want to build a machine learning model. Despite the limited number of datasets, you can also search for data by task type, attribute type, data format, and application area.
✅Academic Torrents - if you are doing research, writing an article or a master's thesis, then Academic Torrents will be a great helper. The platform offers a variety of large datasets from scientific publications, some of which are as large as 2 terabytes. Using Academic Torrents is very simple: you can search for datasets, articles, courses, and collections, and upload your own data for others to work with. The datasets are free, but you will need a torrent client installed on your device to download them.
✅Google Dataset Search - opens access to free public datasets. You can select data on various topics and in various formats, including .pdf, .csv, .jpg, .txt and others. Using it is as easy as a regular Google search: just type the name or topic you are interested in into the search bar. As you type, the system will suggest datasets with the necessary keywords - you may accidentally stumble upon something new and interesting.
✅World Bank Open Data - open data from the World Bank is considered one of the most extensive and diverse sources of statistical information and public datasets. You can search for data by various categories. The World Bank website is unique in that it offers free resources and tools for public use, such as Data Bank, a convenient tool for analyzing and visualizing large data sets
✅Data.world - this platform allows you to access free data sets and work with them directly on the site. All you need to do is create a free account, after which you will have access to 3 free projects. If necessary, you can upgrade to paid plans with a larger storage volume. Using the search bar, you can find keywords, resources, organizations, or users. And for a more precise search, you can use the “Create advanced filter” button to find exactly what you need.
✅DataHub is a data publishing platform (SaaS) developed by Datopian, where you can browse one of the most diverse collections of public data sets organized by topic. The platform also has a blog with materials on topics related to Big Data Science.
✅Humanitarian Data Exchange - a platform for searching datasets. Here you can search for free datasets and filter the results by criteria such as location, format, organization, and license. The platform also allows you to share data by different categories.
✅UCI Machine Learning Repository - the least extensive of all the resources mentioned, it remains useful for those who want to build a machine learning model. Despite the limited number of datasets, you can also search for data by task type, attribute type, data format, and application area.
✅Academic Torrents - if you are doing research, writing an article or a master's thesis, then Academic Torrents will be a great helper. The platform offers a variety of large datasets from scientific publications, some of which are as large as 2 terabytes. Using Academic Torrents is very simple: you can search for datasets, articles, courses, and collections, and upload your own data for others to work with. The datasets are free, but you will need a torrent client installed on your device to download them.
World Bank Open Data
Free and open access to global development data
You need to build CDC (Change Data Capture) from PostgreSQL. What do you prefer?
Anonymous Poll
46%
Debezium + Kafka — a popular standard with log support
29%
Apache NiFi — drag-and-drop stream from change logs
11%
LogMiner + Kafka Connect — if you work with Oracle and want unification
14%
Own agent based on logical replication + custom JSON format
Media is too big
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😱China sets the pace in medical technology
🤖 Robots in white coats
Dozens of medical robots work in the largest clinics in China. They not only disinfect and deliver medicines, but also perform complex procedures:
📌 At Zhongshan Hospital at Fudan University, a robot phlebotomist takes blood more accurately than a person
🧠 AI is not an assistant, but a full-fledged doctor
China has opened the world's first AI hospital, where patients are consulted by "virtual doctors"
⚙️ The system receives patients 24/7, analyzes symptoms, makes preliminary diagnoses and even prescribes treatment.
🔬 Hundreds of thousands of consultations per month — without fatigue and human factor
🌏 Medtech for export
✅ Chinese robots and AI systems are already working in clinics in Asia, Africa and Latin America.
✅ Negotiations are underway with the European Union and the Middle East.
🔮 What does this mean?
The future of medicine has already arrived. And it is made in China😁
🤖 Robots in white coats
Dozens of medical robots work in the largest clinics in China. They not only disinfect and deliver medicines, but also perform complex procedures:
📌 At Zhongshan Hospital at Fudan University, a robot phlebotomist takes blood more accurately than a person
🧠 AI is not an assistant, but a full-fledged doctor
China has opened the world's first AI hospital, where patients are consulted by "virtual doctors"
⚙️ The system receives patients 24/7, analyzes symptoms, makes preliminary diagnoses and even prescribes treatment.
🔬 Hundreds of thousands of consultations per month — without fatigue and human factor
🌏 Medtech for export
✅ Chinese robots and AI systems are already working in clinics in Asia, Africa and Latin America.
✅ Negotiations are underway with the European Union and the Middle East.
🔮 What does this mean?
The future of medicine has already arrived. And it is made in China😁
💡X-AnyLabeling — professional AI tool for automatic data labeling
X-AnyLabeling is an extended and improved version of the popular open-source project AnyLabeling, created for industrial use. Thanks to the support of AI and dozens of CV models, the tool makes labeling much faster and more accurately than manually.
💡 What X-AnyLabeling can do:
🔹 Automatic and semi-automatic annotation of images and videos
🔹 Support for 20+ computer vision models: YOLO, SAM, DETR, etc.
🔹 Working with real-time video — including object tracking on streams
🔹 Intuitive interface + collaboration mode
🔹 Export/import to all key annotation formats: COCO, VOC, YOLO, LabelMe, etc.
🔧 For whom:
✅ ML engineers and CV project teams
✅ Data analysts
✅ Researchers working with medical images, drones, security, etc.
✅ Companies creating commercial datasets
X-AnyLabeling is an extended and improved version of the popular open-source project AnyLabeling, created for industrial use. Thanks to the support of AI and dozens of CV models, the tool makes labeling much faster and more accurately than manually.
💡 What X-AnyLabeling can do:
🔹 Automatic and semi-automatic annotation of images and videos
🔹 Support for 20+ computer vision models: YOLO, SAM, DETR, etc.
🔹 Working with real-time video — including object tracking on streams
🔹 Intuitive interface + collaboration mode
🔹 Export/import to all key annotation formats: COCO, VOC, YOLO, LabelMe, etc.
🔧 For whom:
✅ ML engineers and CV project teams
✅ Data analysts
✅ Researchers working with medical images, drones, security, etc.
✅ Companies creating commercial datasets
GitHub
GitHub - CVHub520/X-AnyLabeling: Effortless data labeling with AI support from Segment Anything and other awesome models.
Effortless data labeling with AI support from Segment Anything and other awesome models. - CVHub520/X-AnyLabeling
🌎TOP DS-events all over the world in May
May 7-8 - Data Science Next Conference Europe 2025 - Amsterdam, Netherlands - https://dscnextcon.com/
May 7-8 - Data Innovation Summit – Stockholm, Sweden - https://datainnovationsummit.com/
May 12-13 – DataFest – Edinburgh, UK - https://thedatalab.com/datafest/
May 13-15 - Qlik Connect – Orlando, USA - https://www.qlikconnect.com/
May 14 - Real-Time Analytics Summit – Online - https://rtasummit.startree.ai/
May 14 - Rise of AI Conference - Berlin, Germany - https://dev.events/conferences/rise-of-ai-conference-2025-j9m-mlae
May 14-15 - Data Summit 2025 – Boston, USA - https://www.dbta.com/DataSummit/2025/default.aspx
May 14-16 - JOTB2025 - Malaga, Spain - https://jonthebeach.com/
May 20-21 - M3 Konferenz - Minds Mastering Machines - IHK Karlsruhe, Germany - https://dev.events/conferences/minds-mastering-machines-mgc-axtp
May 22 - DATA SCIENCE RUHRGEBIET CONGRESS - ZESS - Bochum, Germany - https://data-science.ruhr/
May 29-30 - The Data Science Conference – Chicago, USA - https://www.thedatascienceconference.com/
May 7-8 - Data Science Next Conference Europe 2025 - Amsterdam, Netherlands - https://dscnextcon.com/
May 7-8 - Data Innovation Summit – Stockholm, Sweden - https://datainnovationsummit.com/
May 12-13 – DataFest – Edinburgh, UK - https://thedatalab.com/datafest/
May 13-15 - Qlik Connect – Orlando, USA - https://www.qlikconnect.com/
May 14 - Real-Time Analytics Summit – Online - https://rtasummit.startree.ai/
May 14 - Rise of AI Conference - Berlin, Germany - https://dev.events/conferences/rise-of-ai-conference-2025-j9m-mlae
May 14-15 - Data Summit 2025 – Boston, USA - https://www.dbta.com/DataSummit/2025/default.aspx
May 14-16 - JOTB2025 - Malaga, Spain - https://jonthebeach.com/
May 20-21 - M3 Konferenz - Minds Mastering Machines - IHK Karlsruhe, Germany - https://dev.events/conferences/minds-mastering-machines-mgc-axtp
May 22 - DATA SCIENCE RUHRGEBIET CONGRESS - ZESS - Bochum, Germany - https://data-science.ruhr/
May 29-30 - The Data Science Conference – Chicago, USA - https://www.thedatascienceconference.com/
👍1
👨💻Data Science professionals need cutting-edge tools to stay ahead.
There are 5 services that can boost work efficiency:
1️⃣ DataRobot — A platform that helps automate the process of creating and optimizing machine learning models, allowing even beginners to quickly implement data analysis solutions.
2️⃣ Hugging Face — A repository and tools for working with NLP models and transformers. Essential for processing text data.
3️⃣ RapidMiner — A tool for automating data analysis processes with minimal time investment. It includes powerful features for modeling and visualization.
4️⃣ Kaggle Kernels — A platform for data science learning and competitions, offering seamless access to vast datasets and cloud-based computing resources. Ideal for collaboration, experimentation, and sharpening your skills in a hands-on environment.
5️⃣ Neptune.ai — An experiment-tracking platform for machine learning, enabling teams to log, compare, and visualize model performance effortlessly. Ideal for collaborative projects requiring reproducibility and insight into complex workflows.
💡 Why is this important?
These tools help analysts and researchers save time on routine tasks and achieve results more quickly. Investing in modern services is becoming an important factor in boosting productivity in Data Science.
There are 5 services that can boost work efficiency:
1️⃣ DataRobot — A platform that helps automate the process of creating and optimizing machine learning models, allowing even beginners to quickly implement data analysis solutions.
2️⃣ Hugging Face — A repository and tools for working with NLP models and transformers. Essential for processing text data.
3️⃣ RapidMiner — A tool for automating data analysis processes with minimal time investment. It includes powerful features for modeling and visualization.
4️⃣ Kaggle Kernels — A platform for data science learning and competitions, offering seamless access to vast datasets and cloud-based computing resources. Ideal for collaboration, experimentation, and sharpening your skills in a hands-on environment.
5️⃣ Neptune.ai — An experiment-tracking platform for machine learning, enabling teams to log, compare, and visualize model performance effortlessly. Ideal for collaborative projects requiring reproducibility and insight into complex workflows.
💡 Why is this important?
These tools help analysts and researchers save time on routine tasks and achieve results more quickly. Investing in modern services is becoming an important factor in boosting productivity in Data Science.
DataRobot
DataRobot Homepage | DataRobot
DataRobot delivers the industry-leading AI applications and platform that maximize impact and minimize risk for your business
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🔹 Qwen 3: The Next-Generation Chinese AI
🧠 Hybrid Thinking and Scalability
Qwen 3 is the third generation of large language models developed by Alibaba, designed with a focus on hybrid reasoning. It combines logical reasoning with content generation, making it a versatile tool for a wide range of tasks. The model is available in various configurations, including dense models (from 0.6B to 32B parameters) and sparse models (up to 235B parameters), offering flexibility based on user needs.
🌍 Multilingual Support and Broad Applications
Trained on 36 trillion tokens, Qwen 3 supports 119 languages and dialects, including Chinese, English, Russian, French, and Spanish. This makes it especially attractive for global enterprises aiming for multilingual support and localization. The model is already being used across diverse sectors such as software development, data analysis, education, and marketing.
📈 AI Market Competition
With the release of Qwen 3, Alibaba is intensifying competition in the AI market, particularly in China, where it aims to surpass rivals like DeepSeek and Baidu. The company claims that Qwen 3 outperforms competitor models in several key metrics, including logical reasoning capabilities and task efficiency.
🧩 Open Source and Accessibility
Qwen 3 is released under the Apache 2.0 license, making it accessible to a wide audience of developers and researchers. It is available through platforms such as chat.qwen.ai, Hugging Face, and ModelScope, allowing for easy integration into various applications and services.
🔮 Prospects and Impact
The release of Qwen 3 highlights China’s ambition to lead in the field of artificial intelligence. With enhanced functionality and broad accessibility, Alibaba demonstrates its readiness to compete with Western tech giants by offering powerful and versatile AI solutions.
🧠 Hybrid Thinking and Scalability
Qwen 3 is the third generation of large language models developed by Alibaba, designed with a focus on hybrid reasoning. It combines logical reasoning with content generation, making it a versatile tool for a wide range of tasks. The model is available in various configurations, including dense models (from 0.6B to 32B parameters) and sparse models (up to 235B parameters), offering flexibility based on user needs.
🌍 Multilingual Support and Broad Applications
Trained on 36 trillion tokens, Qwen 3 supports 119 languages and dialects, including Chinese, English, Russian, French, and Spanish. This makes it especially attractive for global enterprises aiming for multilingual support and localization. The model is already being used across diverse sectors such as software development, data analysis, education, and marketing.
📈 AI Market Competition
With the release of Qwen 3, Alibaba is intensifying competition in the AI market, particularly in China, where it aims to surpass rivals like DeepSeek and Baidu. The company claims that Qwen 3 outperforms competitor models in several key metrics, including logical reasoning capabilities and task efficiency.
🧩 Open Source and Accessibility
Qwen 3 is released under the Apache 2.0 license, making it accessible to a wide audience of developers and researchers. It is available through platforms such as chat.qwen.ai, Hugging Face, and ModelScope, allowing for easy integration into various applications and services.
🔮 Prospects and Impact
The release of Qwen 3 highlights China’s ambition to lead in the field of artificial intelligence. With enhanced functionality and broad accessibility, Alibaba demonstrates its readiness to compete with Western tech giants by offering powerful and versatile AI solutions.
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📌 Apple is remodelling Siri with a brand-new architecture built around large language models (LLMs).
📌 The team in Zurich is reportedly rebuilding Siri from the ground up to make it more conversational, context-aware and capable of synthesizing web information.
📌 Unlike other voice assistants the new Siri is expected to process queries directly on-device using Apple’s privacy-focused frameworks.
📌 Apple is also exploring integrations with AI-powered search tools like Perplexity for enhanced web responses.
🔄 Leadership shake-ups and AI strategy.
📌 John Giannandrea, Apple’s long-time AI chief, has been removed from some Siri-related projects, causing concern internally about potential layoffs or internal shifts.
📌 The Siri team has reportedly struggled with integrating generative AI into the assistant’s legacy codebase, prompting the complete rebuild.
📌 Apple has also held talks with external partners to strengthen Safari’s AI-powered search capabilities.
📅 What to expect at WWDC 2025
📌 Don’t expect a major Siri showcase at WWDC this June.
📌 Apple is planning incremental updates to iOS, iPadOS, and macOS with a focus on UI enhancements rather than Siri’s capabilities.
📌 Sources suggest the real Siri upgrade may roll out later as part of Apple’s broader AI strategy which is still under development.
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🧠 Mixture-of-Agents architecture & deep automation
Genspark routes every request through a team of nine purpose-built large-language models and an arsenal of 80+ in-house tools. Its controller decomposes a goal, assigns each sub-task to the best “mini-agent,” then stitches the results back together—boosting speed, accuracy and cost-efficiency compared with single-model agents.
🌍 Real-world adoption across functions
Early users hand off jobs such as market research, data analysis, slide-deck building, travel planning, even voice calls to suppliers. Case studies show Genspark producing full 14-slide presentations, booking hotels via API/voice, and generating marketing microsites—often in minutes rather than hours.
🔮 Roadmap & broader impact
Beyond text tasks, Genspark is rolling out multimodal agents: in-platform video generation (Genape), 3D-CAD workflows, and an “Autopilot” phone bot that can place real-world calls—all powered by its multi-LLM stack and premium datasets. If these modules mature, Genspark could sit between classic BI suites and today’s single-model assistants, offering an end-to-end autonomous workbench for enterprises.
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A San Francisco-based startup recently raised $4.7 million in a pre-seed funding round co-led by Andreessen Horowitz’s crypto accelerator and EV3
Rumi’s platform lets viewers talk to on-screen characters, athletes, or hosts in real time — while watching. The AI interprets live video and audio context to answer questions like:
“Why was that a foul?”
“Does Steph Curry look tired now?”
All responses are anchored in the actual content and scene, minimizing speculation and hallucination.
The system operates on decentralized infrastructure, indexing video as it’s viewed. This enables real-time engagement while preserving IP rights — content owners remain fully in control.
❗️ Why it matters:
Rumi is pioneering a shift in media — from passive consumption to active dialogue. This could redefine streaming platforms, sports broadcasts, educational videos, and more, by merging entertainment with interactive AI.
📌 Investments: $4.7M pre-seed (Andreessen Horowitz + EV3)
📌 Tech: Real-time video + audio parsing, context-aware AI responses, decentralized indexing
📌 Use cases: Live Q&A during shows/matches, fact checks, character insights
🔮 The big picture:
Viewers aren’t just watching — they’re interacting, learning, and engaging emotionally. Rumi aims to turn media into conversational, personalized experiences that feel like co-creation with AI.
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What can Photoleap do?
⚡️ Creative Transformations: Expand image backgrounds with AI Expand, generate professional portraits with AI Headshot Generator, or swap faces with FaceSwitch for playful or creative edits. Convert photos into paintings, anime, or retro styles using AI-driven filters.
↗️ AI-Enhanced Editing: Instantly improve photos with Auto Enhance, optimizing lighting, contrast, and sharpness. AI Remove precisely eliminates unwanted objects or backgrounds, delivering clean, professional results.
✏️ Text-to-Image Creation: Generate unique artwork from text prompts in seconds, supporting realistic, anime, or cartoon styles—perfect for custom social media graphics or digital art projects.
🎥 Motion and Animation: Bring images to life with 3D effects, live photo animations, or short videos. Create GIFs or dynamic content tailored for platforms like Instagram and TikTok.
🗒 Advanced Layer Editing: Build complex compositions with multiple layers, masks, and blending modes. Add text, stickers, or effects to craft professional designs directly on your device.
🌟 Social Media Optimized: Use templates for perfectly sized posts and stories. Crop, resize, or add digital frames to suit any platform, with direct sharing options or exports in JPEG, PNG, or MP4 formats.
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🤖 Nanobanana.ai is an AI aggregation platform that provides unified subnoscription-based access to a suite of the world's most advanced AI models for image generation and editing. It simplifies the creative workflow by offering a single gateway to multiple powerful tools, eliminating the need for separate subnoscriptions.
🎨 Text-to-Image Generation
Leverage the strengths of top-tier AI models to generate stunning visuals from simple text prompts:
Stable Diffusion 3 & SDXL: For maximum control over composition and style, enabling the creation of highly detailed and customizable imagery.
DALL·E 3: Excels at prompt adherence, generating accurate and contextually relevant images that closely match your textual denoscriptions.
Midjourney: Renowned for its distinctive artistic style and high aesthetic quality, ideal for creating visually arresting and creative artwork.
Playground v2.5 & Flux Dev: Generate images across a wide spectrum of styles, from photorealistic renders to anime and digital art.
✨ Professional Editing & Enhancement Tools
Move beyond generation and into precision editing with AI-powered tools:
AI Upscale: Intelligently increase image resolution by 2x-4x while preserving and enhancing detail, eliminating pixelation.
Inpainting/Outpainting: Seamlessly remove unwanted objects, restore missing details, or expand an image's canvas by generating coherent backgrounds.
Reimagine: Instantly create multiple variations of an original image, exploring different artistic interpretations and styles.
Background Removal: Automatically detect and remove image backgrounds with high precision, enabling quick compositing and replacement.
📌 Ideal For:
Designers & Digital Artists: Rapidly prototype concepts and create assets.
Social Media Content Creators: Produce a constant stream of engaging, original visual content.
Marketers & Advertisers: Create compelling visuals for campaigns and social ads quickly.
Bloggers & Journalists: Generate custom featured images and illustrations for publications.
Digital Art Hobbyists: Explore AI artistry and experiment with different models effortlessly.
🎨 Text-to-Image Generation
Leverage the strengths of top-tier AI models to generate stunning visuals from simple text prompts:
Stable Diffusion 3 & SDXL: For maximum control over composition and style, enabling the creation of highly detailed and customizable imagery.
DALL·E 3: Excels at prompt adherence, generating accurate and contextually relevant images that closely match your textual denoscriptions.
Midjourney: Renowned for its distinctive artistic style and high aesthetic quality, ideal for creating visually arresting and creative artwork.
Playground v2.5 & Flux Dev: Generate images across a wide spectrum of styles, from photorealistic renders to anime and digital art.
✨ Professional Editing & Enhancement Tools
Move beyond generation and into precision editing with AI-powered tools:
AI Upscale: Intelligently increase image resolution by 2x-4x while preserving and enhancing detail, eliminating pixelation.
Inpainting/Outpainting: Seamlessly remove unwanted objects, restore missing details, or expand an image's canvas by generating coherent backgrounds.
Reimagine: Instantly create multiple variations of an original image, exploring different artistic interpretations and styles.
Background Removal: Automatically detect and remove image backgrounds with high precision, enabling quick compositing and replacement.
📌 Ideal For:
Designers & Digital Artists: Rapidly prototype concepts and create assets.
Social Media Content Creators: Produce a constant stream of engaging, original visual content.
Marketers & Advertisers: Create compelling visuals for campaigns and social ads quickly.
Bloggers & Journalists: Generate custom featured images and illustrations for publications.
Digital Art Hobbyists: Explore AI artistry and experiment with different models effortlessly.
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🌐 OpenAI has released ChatGPT Atlas
Atlas is a browser with an integrated AI sidebar, built as a full-featured super-assistant for work, learning, and creativity. All interaction is conversational: you manage tasks, search, and organize routines easily while browsing. Atlas is currently available for macOS, with Windows, iOS, and Android support coming soon.
Key ChatGPT Atlas options:
⚡️ Built-in ChatGPT chat: The assistant is always available on every page, ready to summarize, analyze, and generate content directly in the sidebar.
🌟 Agent Mode: The AI-agent can book tables, purchase tickets, and execute online orders—available to Plus, Pro, and Go subscribers.
🔎 Conversational search: Type anything into the address bar, and Atlas will deliver ChatGPT-style dialog answers, while traditional links remain accessible.
🛡 Flexible privacy controls: Users decide which sites AI can access and what gets saved in "browser memory," with the choice to clear or archive at any time.
🌐 Extensibility & Chromium engine: Atlas supports most browser extensions and provides high compatibility with websites.
🔜 Quick actions on selected text: Highlight any text and AI-contextual suggestions appear, streamlining tasks and insights.
Atlas is a browser with an integrated AI sidebar, built as a full-featured super-assistant for work, learning, and creativity. All interaction is conversational: you manage tasks, search, and organize routines easily while browsing. Atlas is currently available for macOS, with Windows, iOS, and Android support coming soon.
Key ChatGPT Atlas options:
🌟 Agent Mode: The AI-agent can book tables, purchase tickets, and execute online orders—available to Plus, Pro, and Go subscribers.
🌐 Extensibility & Chromium engine: Atlas supports most browser extensions and provides high compatibility with websites.
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It dramatically reduces post-processing time while accurately replicating your signature adjustments for exposure, color, contrast, and overall look. Fully integrated with Adobe Creative Cloud, Imagen AI always keeps you in control: the AI suggests edits, and you finalize everything in your familiar Lightroom environment.
Key Features of Imagen AI:
⚡️ Personal AI Profiles
Trains on 5,000+ of your edited photos to create unlimited custom profiles tailored to different genres (weddings, portraits, etc.). Once trained, it automatically applies your exact editing style to new shoots.
🌟 AI-Powered Culling
Automatically selects the sharpest and best-composed shots, detects and groups duplicates, flags blinks and closed eyes, and ranks images — turning thousands of photos into a curated selection in minutes.
🎨 Talent Profiles
Ready-to-use signature styles from world-class photographers, including: “Natural Feels” by John Branch IV, “Clean & Crisp” by Susan Stripling, “Tierra” by Fer Juaristi, “Cinematic Luxury” by Charmi Peña and many more.
🔧 One-Click Batch Editing
Instantly applies color correction, exposure, noise reduction, sharpening, cropping, and even video enhancements across entire galleries.
🛡 Privacy & Full Control
Your photos are used only to train your personal profiles and are never stored on Imagen’s servers. All final editing and adjustments stay inside Lightroom.
🔄 Cloud Sync & Backup
Automatic cloud storage of your AI profiles, seamless style updates, and the assurance that ~90% of images are client-ready straight out of the AI.
Imagen AI turns weeks of editing into hours — while keeping every image unmistakably yours.
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