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 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.
Let’s explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning.
Today we all choose between the simplicity of Python tools (pandas, Scikit-learn), the scalability of Spark and Hadoop, and the operation readiness of Kubernetes. We end up using them all.