Why you should build your AI Applications with Ray

Compute-intensive applications that incorporate machine learning should be built on top of Ray. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] Introduction As machine learning and AI become prevalent in software services and applications, most backend platforms now consist of business logic and machine learning (inference).  Business logic andContinue reading “Why you should build your AI Applications with Ray”

Gradient Flow #34: Modernizing Data Governance, DataOps for ML, Declarative Interfaces

Subscribe • Previous Issues This edition has 510 words which will take you about 3 minutes to read. “If something cannot go on forever it will stop.” – Herbert Stein Data Exchange podcast Injecting Software Engineering Practices and Rigor into Data Governance  As the amount and importance of data grows within organizations, there is growing interest inContinue reading “Gradient Flow #34: Modernizing Data Governance, DataOps for ML, Declarative Interfaces”

Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML

Subscribe • Previous Issues This edition has 548 words which will take you about 3 minutes to read. “While you are looking, you might as well also listen, linger and think about what you see.”  – Jane Jacobs Data Exchange podcast How Technology Companies Are Using Ray  Zhe Zhang is an Engineering Manager at Anyscale where heContinue reading “Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML”

Gradient Flow #32: Data Cascades, Demand for Data Engineers, Exploiting ML models

Subscribe • Previous Issues This edition has 428 words which will take you about 2 minutes to read. “I would believe only in a god who could dance.” – Friedrich Nietzsche. Data Exchange podcast Machine Learning in Healthcare  I speak with Parisa Rashidi, Associate Professor at the Department of Biomedical Engineering and Director of the Intelligent HealthContinue reading “Gradient Flow #32: Data Cascades, Demand for Data Engineers, Exploiting ML models”

One Simple Chart: Data Engineering jobs in the U.S.

It’s been a few months since I looked at data on job postings. In my most recent post in Dec/2020 I focused on reinforcement learning (RL), which in terms of number of job postings, barely grew on a year-over-year basis. The good news is that it appears that employers are once again starting to postContinue reading “One Simple Chart: Data Engineering jobs in the U.S.”

Data Cascades: Why we need feedback channels throughout the machine learning lifecycle

A team from Google Research shares lessons learned from high-stakes domains. Data has been an undervalued component of AI development since the dawn of AI. We are now seeing the beginnings of a much-needed shift in how data is viewed. In a recent post, we described the growing interest in metadata management systems as aContinue reading “Data Cascades: Why we need feedback channels throughout the machine learning lifecycle”

Gradient Flow #31: AI in Healthcare, Data Quality, Understanding Neural Networks

Subscribe • Previous Issues This edition has 368 words which will take you about 2 minutes to read. “There’s a Fog of War, but there’s also a Fog of Peace.” – Eric Grosse Data Exchange podcast The Mathematics of Data Integration and Data Quality    Ryan Wisnesky is the CTO and co-founder of Conexus, a startup thatContinue reading “Gradient Flow #31: AI in Healthcare, Data Quality, Understanding Neural Networks”

2021 AI in Healthcare Survey Report

By Ben Lorica and Paco Nathan. Applications of AI in Healthcare ​pose a number of challenges and considerations which differ substantially from other business verticals. We conducted an industry survey specifically about AI in healthcare, to understand more about current trends and issues. A total of 373 respondents from 49 countries participated in the survey.Continue reading “2021 AI in Healthcare Survey Report”