Subscribe • Previous Issues Distributed Computing for AI Kenn So of Shasta Ventures and I recently introduced a class of AI startups (“pegacorns”) that have at least $100 million in annual revenue. We found that more AI pegacorn founders cited proficiency in distributed systems compared to ML and AI. In this new post, we examine metrics thatContinue reading “Scale, Scale, Scale”
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Revisiting the unicorn concept
Subscribe • Previous Issues The AI $100 Million Revenue Club Everyday there’s a new unicorn. It used to be a status that meant that a startup has graduated from being a startup and into a mature company worth being listed on the public stock market backed by revenue. In today’s climate becoming a unicorn is increasingly aContinue reading “Revisiting the unicorn concept”
Data For AI
Subscribe • Previous Issues The Data Integration Market As much as I like talking and writing about machine learning and AI, the truth is that there are probably more impressive startups in the data engineering and data infrastructure (DE) category. DE companies address fundamentals that need to be in place before companies can rely on reports andContinue reading “Data For AI”
Supercharging Your Data and AI Platforms
Subscribe • Previous Issues Data Management Trends You Need to Know Intel Capital’s Assaf Araki and I both focus on data, analytics, and machine learning, thus we regularly hear pitches from startups building new data management solutions. Data management is a broad area that includes solutions for different workloads, data types, and use cases. Our post listsContinue reading “Supercharging Your Data and AI Platforms”
Practical Reinforcement Learning and Differential Privacy
Subscribe • Previous Issues Ratio of Data Scientists to Data Engineers A fun topic of discussion among leaders of data teams is the ratio between the number of data scientists and data engineers. There is no ideal answer. It really depends on the tools and infrastructure you have in place, the maturity and availability of use casesContinue reading “Practical Reinforcement Learning and Differential Privacy”
Locating Machine Learning Engineers
Subscribe • Previous Issues Where Do Machine Learning Engineers Work? About five years ago we published a post that highlighted the emergence of a role focused on making data science work in production. At the time we noticed job postings (mainly in the SF Bay Area) that used the title “machine learning engineer” to describe individuals skilledContinue reading “Locating Machine Learning Engineers”
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”
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”
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”
Gradient Flow #46: Smarter Language Models; Data Engineering Trends; Graph Intelligence
Subscribe • Previous Issues “It’s time for big data scientists to become social scientists, not just computer scientists.” – Justin Grimmer FREE Report: Data Engineering Survey Results We examine the changing landscape of data engineering challenges, tools, and opportunities. This report is based on a global online survey that drew 372 respondents. Download Data Exchange podcast MakingContinue reading “Gradient Flow #46: Smarter Language Models; Data Engineering Trends; Graph Intelligence”