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Book Review: Machine Learning for Kids

I greatly enjoyed reading and reviewing this delightful new book, Machine Learning for Kids: A Project-based Introduction to Artificial Intelligence, by Dale Lane, which was developed to introduce machine learning technology to children. It is well-written and includes everything needed to jump-start a kid’s life in data science. The book is just the thing to motivate a young person to extend their innate curiosity to data centric experimentation.

“Above the Trend Line” – Your Industry Rumor Central for 4/16/2021

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

The insideBIGDATA IMPACT 50 List for Q2 2021

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning

Scientists at Cambridge Quantum Computing (CQC) have developed methods and demonstrated that quantum machines can learn to infer hidden information from very general probabilistic reasoning models. These methods could improve a broad range of applications, where reasoning in complex systems and quantifying uncertainty are crucial. Examples include medical diagnosis, fault-detection in mission-critical machines, or financial forecasting for investment management.

The ModelOps Movement: Streamlining Model Governance, Workflow Analytics, and Explainability

In this contributed article, editorial consultant Jelani Harper discusses how the ModelOps movement either directly or indirectly addresses each of the following three potential barriers to cognitive computing success: model governance, explainability, and workflow analytics.

insideBIGDATA Latest News – 4/8/2021

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.

TOP 10 insideBIGDATA Articles for March 2021

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Tecton Announces Line-Up for First Annual Machine Learning Data Engineering Conference – apply()

Tecton, the enterprise feature store company, announced the line-up for apply(), a virtual conference that it is hosting on data engineering for applied machine learning (ML) April 21 – 22. apply() is a practitioner-focused community event for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world

New to AI Adoption? Don’t Let Data be Your Achilles Heel

In this contributed article, Jeff White is the founder and chief executive officer of Gravy Analytics, discusses the realities of big data: no data source is perfect, and despite your best efforts, issues with new technologies like machine learning and AI are bound to occur. By understanding how your underlying data is collected, cleaned, verified and assembled, organizations can derive maximum value while optimizing internal resources, improving the customer experience, and avoiding costly mistakes along the way.

The Fate of Feature Engineering: No Longer Necessary, or Much Easier?

In this contributed article, editorial consultant Jelani Harper believes that features are the definitive data traits enabling machine learning models to accurately issue predictions and prescriptions. In this respect, they’re the foundation of the statistical branch of AI. However, the effort, time, and resources required to engender those features may become obsolete by simply learning them with graph embedding so data scientists are no longer reliant on hard to find, labeled training data.