Charts and Graphics

Distributed Computing for AI: A Status Report

An update on the central role of distributed computing in modern AI. By Ben Lorica and Kenn So. In our previous post we introduced a class of AI startups (“pegacorns”) that have at least $100 million in annual revenue. Many of the AI pegacorns sell applications rather than infrastructure, and many of their founders citedContinue reading “Distributed Computing for AI: A Status 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”

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”

Where Do Machine Learning Engineers Work?

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?”

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”

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”

Applications of Reinforcement Learning: Recent examples from large US companies

When I wrote about enterprise applications of reinforcement learning (RL) a little over a year ago, I cited a few examples of applications for recommenders and personalization systems. At the time, the examples I listed came from large technology companies, specifically Netflix, JD, Facebook, and YouTube, as only large companies tended to have the resourcesContinue reading “Applications of Reinforcement Learning: Recent examples from large US companies”

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.”

One Simple Chart: online learning platforms, a year into the pandemic

Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies. Last year I examined usage of a few online learning platforms and found that a month into the pandemic, many of them were growing rapidly. Now that we have data to compare pre and post pandemic usage, let’sContinue reading “One Simple Chart: online learning platforms, a year into the pandemic”

One Simple Chart: Demand for Reinforcement Learning Holds Steady

Late last year I started running into more companies using reinforcement learning (RL). Inspired by some of the things I was hearing about, early this year I wrote about emerging RL use cases in simulation & optimization, as well as examples of RL in recommendation and personalization systems. With the global pandemic taking a tollContinue reading “One Simple Chart: Demand for Reinforcement Learning Holds Steady”

One Simple Chart: primary applications of AI in Financial Services

Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies. In a recent post, I highlighted technologies are most important to large banks using results from a BankDirector survey. That survey hinted that Data and AI are high priorities within large banks, and Automation and Data Analytics emergedContinue reading “One Simple Chart: primary applications of AI in Financial Services”

2020 NLP Survey cover

NLP Survey Results: An Overview

The term “NLP”—or natural language processing—encompasses a wide range of business use cases that are mostly text based. Consider that people use text to record and transmit their communications in general, and, as such, it’s one of the most widely available and “interoperable” data formats. While some industry sectors such as finance and healthcare haveContinue reading “NLP Survey Results: An Overview”

One Simple Graphic: companies that offer deep neural network accelerators

In 2018, I sat down and listed companies (mainly based in the US and China) that were offering specialized hardware for deep learning. There were plenty of startups in the hardware space at that time but things have changed and that particular list is a bit outdated. Many companies have pivoted, or gone bust, orContinue reading “One Simple Graphic: companies that offer deep neural network accelerators”

One Simple Chart: what technologies are most important to large banks

As someone who speaks with many technology startups in the software space, I know how important the financial services sector can be to software companies. Companies in this sector have significant technology budgets and increasing competition from fintech startups has accelerated their adoption of new technologies. More financial companies are using cloud platforms and machineContinue reading “One Simple Chart: what technologies are most important to large banks”

One Simple Chart: how open source projects interact with users

Core members of an open source projects make an effort to interact with users through a variety of means. They give talks at local events (now moved online), they answer questions online, and they resolve issues identified by users (using tools like GitHub). As far as answering questions from users, over the past couple ofContinue reading “One Simple Chart: how open source projects interact with users”

One Simple Chart: the number of cloud native developers worldwide

I recently came across a developer survey (The State of Cloud Native Development) from the Cloud Native Computing Foundation (CNCF) and /Data, focused on estimating the number of cloud native developers worldwide. CNCF defines cloud native technologies as follows: Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public,Continue reading “One Simple Chart: the number of cloud native developers worldwide”

One Simple Chart: where do consumers prefer AI data be processed

With machine learning and AI being embedded in a growing number of products and systems, privacy and security become central for users and companies. Every company now has a Privacy Policy to comply with regulations like GDPR and CCPA. And in the not-so-distant future, companies will have teams focused on managing risks stemming from dataContinue reading “One Simple Chart: where do consumers prefer AI data be processed”

One Simple Chart: which sectors are using reinforcement learning

Interest in reinforcement learning has grown steadily over the last decade. In a recent post, I described emerging applications of RL in recommendation and personalization systems, and in business simulation and optimization. In this post, I wanted to examine which industry sectors have been mentioning reinforcement learning in their job postings. Let’s place demand forContinue reading “One Simple Chart: which sectors are using reinforcement learning”

One Simple Chart: Technology Adoption in the U.S.

I just came across a new paper that analyzes results from the 2018 Annual Business Survey, a study conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics. This survey was conducted over the second of half of 2018, and while the data is over a year old,Continue reading “One Simple Chart: Technology Adoption in the U.S.”

man using stylus pen for touching the digital tablet screen

One Simple Chart: Computational Limits of Deep Learning

While deep learning proceeds to set records across a variety of tasks and benchmarks, the amount of computing power needed is becoming prohibitive. A recent paper – “The Computational Limits of Deep Learning” – from M.I.T., Yonsei University, and the University of Brasilia, estimates of the amount of computation, economic costs, and environmental impact thatContinue reading “One Simple Chart: Computational Limits of Deep Learning”

view of airport

One Simple Chart: job postings that mention “serverless”

Ray is the future of the serverless API: Learn all about it at Ray Summit, a FREE virtual conference showcasing best practices, real-world case studies, and the latest in AI and scalable systems built on Ray. Join Dave Patterson, Ion Stoica, Manuela Veloso, Azalia Mirhoseini, Michael Jordan, Zoubin Ghahramani, Oriol Vinyals, and many other speakersContinue reading “One Simple Chart: job postings that mention “serverless””

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