ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.
2021 Kickoff Webinar: Deploying ML in federated cloud and edge environments ft. AWS - Jan 26 @ 10amPST
Explore how to use Dask over Kubernetes when handling large datasets in data preparation and ML training, with code examples and a link to a full demo, as well as practical tips to get you started.
Version 2.8 includes an exciting set of features that help users to build and manage their operational machine learning pipelines. We’ve introduced a new set of functionalities around MLOps which assists in solving some common challenges in bringing AI to production. And this is only the beginning.
Data science has come a long way, and it has changed organizations across industries profoundly. Very reliable systems and automated algorithms are being developed to harness this data to deliver increased efficiency and value to humanity.
Data science needs to quickly adapt to the fast-paced changes happening all over the world. Currently, many businesses are in a tough spot, and having the right kinds of data and intelligence enables them to react quickly to the unprecedented changes brought about by the pandemic.
Feature stores enable data scientists to reuse features instead of rebuilding these features again and again for different models, saving them valuable time and effort.
Here's what business leaders can do to reduce the costs of their AI projects and see positive business results sooner.