Brooke Wenig introduces you to Apache Spark 2.0 core concepts with a focus on Spark's machine learning library, using text mining on real-world data as the primary end-to-end use case.

Salesforce recently released Einstein, which brings AI into its core platform to power every business. The secret behind Einstein is an underlying platform that accelerates AI development at scale for both internal and external data scientists. Simon Chan shares his experience building this unified platform for a multitenancy, multibusiness cloud enterprise.

Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video.

Google has invested deeply in machine learning for many years and is using it successfully across its highly successful consumer businesses. Philippe Poutonnet explains how to leverage the power of ML with Google Cloud, using the platform's powerful data management tools, support for collaborative experiments, and predictions at Google scale.

Paco Nathan reviews use cases where Jupyter provides a frontend to AI as the means for keeping humans in the loop. This process enhances the feedback loop between people and machines, and the end result is that a smaller group of people can handle a wider range of responsibilities for building and maintaining a complex system of automation.

Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video.

Amy Unruh offers a quick overview of machine learning on Google Cloud Platform and demonstrates a couple of the Google Cloud ML APIs. She then briefly highlights a few OSS TensorFlow models and explains how to use transfer learning to fine-tune them with your own data.

Clara Labs is fusing machine learning (ML) with distributed human labor for natural language tasks. The result is a virtuous cycle: ML predictions improve workers efficiency, and workers help improve prediction models. Jason Laska explores the challenges of building a real-time(ish) knowledge workforce, how to integrate automation, and key strategies Clara Labs learned that enable scale.

Chris Fregly explores an often-overlooked area of machine learning and artificial intelligencethe real-time, end-user-facing "serving layer in hybrid-cloud and on-premises deployment environmentsand shares a production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.

Big data used to be synonymous with Hadoop, but our ecosystem has evolved over time with new database, streaming and machine learning solutions which dont necessarily benefit from the Hadoop deployme …

What if you had to build more models than there are data scientists in the worlda feat enterprise companies serving hundreds of thousands of businesses often have to do? Leah McGuire offers an overview of Salesforce's general-purpose machine-learning platform that automatically builds per-company optimized models for any given predictive problem at scale, beating out most hand-tuned models.

Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms.