ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.
ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.
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.
Using GPUaaS in this way simplifies and automates data science, boosting productivity and significantly reducing time to market.
Summary of my MLOps NYC talk, major AI/ML & Data challenges and how they will be solved with emerging open source technologies
Discover Iguazio's "cloud-like" Intelligent Edge, powered by NVIDIA EGX, which enables data and compute intensive processing with seamless usability.
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.