Introducing the Pegacorn Club. By Kenn So and Ben Lorica. [Also see our followup post: The Data Pegacorns.] Nearly 600 companies became unicorns in 2021. 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 marketContinue reading “The AI $100M Revenue Club”
Category Archives: Uncategorized
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”
2022 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 321 respondents from 41 countries participated in the survey.Continue reading “2022 AI in Healthcare Survey Report”
The Data Integration Market
By Ben Lorica. As much as I like talking and writing about machine learning and AI, I am equally keen to point out there are also many impressive1 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 “The Data Integration Market”
Most State-Of-The-Art AI Systems Are Trained With Extra Data
According to the 2022 AI Index Report, nine state-of-the-art AI systems out of the ten benchmarks they tested against are trained with extra data. By Ben Lorica. Stanford’s AI Index Report has just come out – one of my favorite annual reads. This report tracks several metrics including performance on machine learning benchmarks, volume ofContinue reading “Most State-Of-The-Art AI Systems Are Trained With Extra Data”
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”
Data Management Trends You Need to Know
Insights and trends that will help you navigate the data management landscape. By Assaf Araki and Ben Lorica. Given our focus on data, analytics, and machine learning, 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.Continue reading “Data Management Trends You Need to Know”
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”
Ratio of Data Scientists to Data Engineers
As companies get more proficient in using data and AI to drive decision making and operations, team members with disparate backgrounds – analysts, product mangers, decision makers – begin using data on a regular basis. But when they’re first starting out, the requisite data may not be in place, and data processing and analysis tendContinue reading “Ratio of Data Scientists to Data Engineers”
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”