ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

The MLOps Blog

OCTOBER 3, 2023

So I tell people honestly, I’ve spent the last eight years working up and down the data and ML value chain effectively – a fancy way of saying “job hopping.” How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? And later, an MLOps engineer.

ML 52

ML Data Scientist Machine Learning Data Science 52

Snorkel AI

MAY 26, 2023

And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation.

ML 52

ML Python Machine Learning Data Scientist 52

Snorkel AI

MAY 26, 2023

And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation.

ML 52

ML Python Machine Learning Data Scientist 52

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Unite.AI

JUNE 28, 2023

Xavier Conort is a visionary data scientist with more than 25 years of data experience. He began his career as an actuary in the insurance industry before transitioning to data science. He’s a top-ranked Kaggle competitor and was the Chief Data Scientist at DataRobot before co-founding FeatureByte.

Data Scientist 264

Data Scientist Data Science Machine Learning ML Engineer 264

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

SEPTEMBER 12, 2024

In fact, 96 percent of all AI/ML unicorns—and 90 percent of the 2024 Forbes AI 50—are AWS customers. We’re empowering data scientists, ML engineers, and other builders with new capabilities that make generative AI development faster, easier, more secure, and less costly.

Generative AI 95

Generative AI AI AI ML 95