We’re proud to share that Iguazio has been named in Gartner's 2022 Market Guide for Data Science & Machine Learning Engineering Platforms. According to Gartner, “The AI & data science platform market is due to grow to over $10 billion by 2025 at a 21.6% compounded annual growth rate.

Here at Cloudera, we’re committed to helping make the lives of data practitioners as painless as possible. For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value.

As a very hands-on VP of Product, I have many, many conversations with enterprise data science teams who are in the process of developing their MLOps practice. Almost every customer I meet is in some stage of developing an ML-based application. Some are just at the beginning of their journey while others are already heavily invested. It’s fascinating to see how data science, a once commonly used buzz word, is becoming a real and practical strategy for almost any company.

The algorithm team at WSC Sports faced a challenge. How could our computer vision model, that is working in a dynamic environment, maintain high quality results? Especially as in our case, new data may appear daily and be visually different from the already trained data. Bit of a head-scratcher right? Well, we’ve developed a system that is doing just that and showing exceptional results!

Data science is an important skill, but the hard truth is many organizations aren’t seeing the ROI showing that data science work is making a business impact. Yet today, many organizations are still struggling to adopt a holistic approach centered around creating business value. Instead, they are focused on theoretical work. Here at Iguazio, we recently held a webinar with Noah Gift, founder of Pragmatic A.I. Labs, professor, author and MLOps consultant.

Synapse services are powerful tools for bringing data together for analytics, machine learning, reporting needs, and more. Synapse services serve the purpose of merging data integration, warehousing, and big data analysis together with the goal of gaining a unified experience to ingest, prepare, manage, and serve data for business intelligence needs.

For our latest machine learning specialist interview on our blog, we’ve welcomed Ivan Goncharov, a machine learning engineer on the growth team at a unicorn MLOps startup, Weights & Biases.

When we think of the various people and teams making use of ML and DBMS, we can place them on a spectrum based on the composition of their work.