With interest in MLOps surging, companies are bound to reassess their tools, as well as the composition of their data and ML teams. About five years ago we published a post that highlighted the emergence of a role focused on making data science work in production. We were prompted by job postings (in the SFContinue reading “Where Do Machine Learning Engineers Work?”
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2022 Trends in Data and AI
Subscribe • Previous Issues FREE Report: Trends in Data, Machine Learning, and AI This short guide identifies trends that will be relevant to organizations across all industries and sectors over the next 12-18 months. Download What is Graph Intelligence? In a new post with Leo Meyerovich of Graphistry, we highlight the current state of Graph Intelligence, aContinue reading “2022 Trends in Data and AI”
2022 Trends Report: Data, Machine Learning, and AI
By Ben Lorica, Mikio Braun, and Jenn Webb. In this short report, we list key trends in big data, machine learning, and AI, with a bias toward items that will impact companies and organizations across all sectors over the next 12-18 months. Tools to help companies put Data and AI to work are definitely gettingContinue reading “2022 Trends Report: Data, Machine Learning, and AI”
What is Graph Intelligence?
How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading “What is Graph Intelligence?”
Data Remains the Key Challenge In Computer Vision Projects
Datagen recently surveyed about 300 professionals in computer vision about the value of data. The survey comes at a time of renewed focus on the importance of tools for helping ML teams address data related challenges. Data-centric AI represents a recent shift among researchers, away from focusing on models and toward the underlying data used inContinue reading “Data Remains the Key Challenge In Computer Vision Projects”
Experimentation Tools; Surge in MLOps; 2021 Books
Subscribe • Previous Issues Data Exchange podcast Modern Experimentation Platforms: Che Sharma is the founder and CEO of Eppo, an experimentation framework that integrates with modern data platforms (lakehouses and cloud data warehouses). Investors and engineers have created an abundance of companies that specialize in ML infrastructure and MLOps, while applications like experimentation have received little attention.Continue reading “Experimentation Tools; Surge in MLOps; 2021 Books”
One Simple Graphic: Interest in MLOps is surging
The overall job market in the U.S. has recovered from the depths of the pandemic. In the area of “machine learning”, demand for MLOps talent appears to be growing rapidly. This could be an early indicator that machine learning and AI initiatives are beginning to graduate from R&D projects and prototypes, into production systems: RelatedContinue reading “One Simple Graphic: Interest in MLOps is surging”
One Simple Chart: Data Engineering areas of focus
Related Content: FREE Report: Data Engineering Survey Results One Simple Chart: Data Engineering jobs in the U.S. One Simple Graphic: Interest in MLOps is surging Subscribe to our Newsletter:
What’s new in the Ray Distributed Library Ecosystem
By Ben Lorica and Ion Stoica. [This post appeared originally on the Anyscale blog.] The Ray community of users, contributors, libraries, and production use cases have grown substantially since we first described the Ray ecosystem over a year ago. The majority of Ray users continue to come from libraries and third-party integrations. They include developers who useContinue reading “What’s new in the Ray Distributed Library Ecosystem”
Confidential Computing; DataOps and MLOps
Subscribe • Previous Issues Get Ready For Confidential Computing A comprehensive data privacy and security policy involves protecting the confidentiality and integrity of data in any of these three states: at rest, in use, and in transit. In a new post with Intel Capital’s Assaf Araki, we describe the ecosystem of tools focused on protecting data while in use.Continue reading “Confidential Computing; DataOps and MLOps”