Deploying AI on local AWS Outposts environments using the Iguazio platform provides a simple way for ML teams to work (and leverage the same APIs and tools) across hybrid cloud and edge environments, without compromising on speed or performance.
Deploying AI on local AWS Outposts environments using the Iguazio platform provides a simple way for ML teams to work (and leverage the same APIs and tools) across hybrid cloud and edge environments, without compromising on speed or performance.
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.
The notions of collaborative innovation, openness and portability are driving enterprises to embrace open source technologies. Anyone can download and install Kubernetes, Jupyter, Spark, TensorFlow and Pytorch to run machine learning applications, but making these applications enterprise grade is a whole different story.