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Feature Store vs Data Warehouse
A feature store is a data warehouse of features for machine learning. Differently from a data warehouse, it is dual-database: one serving features at low latency to online applications and another storing large volumes of features. Learn how Data Scientists leverage this capability in production-deployed models.
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MLOps Is Changing How Machine Learning Models Are Developed
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.
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MLOps – “Why is it required?” and “What it is”?
Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.
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 Crack SQL Interviews
SQL is an essential programming language for data analysis and processing. So, SQL questions are always part of the interview process for data science-related jobs, including data analysts, data scientists, and data engineers. Become familiar with these common patterns seen in SQL interview questions and follow our tips on how to neatly handle each with SQL queries.
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Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds
Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.
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Data Science Volunteering: Ways to Help
No matter the field in which you hold some expertise, sharing your skills to benefit the lives of others or supporting non-profit organizations that try to make the world a better place is a noble and time-worthy personal pursuit. Many opportunities exist in data science to contribute to meaningful projects and crucial needs from your local community to a global scale.
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AI registers: finally, a tool to increase transparency in AI/ML
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
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The Ultimate Guide to Data Engineer Interviews
If you are preparing for data engineering interviews, then follow these technical recommendations regarding your resume, programming skills, SQL acumen, and system design problem-solving, as well as the non-technical aspects of your upcoming interview session.
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 Why the Future of ETL Is Not ELT, But EL(T)
The well-established technologies and tools around ETL (Extract, Transform, Load) are undergoing a potential paradigm shift with new approaches to data storage and expanding cloud-based compute. Decoupling the EL from T could reconcile analytics and operational data management use cases, in a new landscape where data warehouses and data lakes are merging.
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Introduction to Data Engineering
The Q&A for the most frequently asked questions about Data Engineering: What does a data engineer do? What is a data pipeline? What is a data warehouse? How is a data engineer different from a data scientist? What skills and programming languages do you need to learn to become a data engineer?
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Crack SQL Interviews
Why the Future of ETL Is Not ELT, But EL(T)