Discover Iguazio's "cloud-like" Intelligent Edge, powered by NVIDIA EGX, which enables data and compute intensive processing with seamless usability.
ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.
Discover Iguazio's "cloud-like" Intelligent Edge, powered by NVIDIA EGX, which enables data and compute intensive processing with seamless usability.
It's a wrap! We had a full house at MLOps NYC, Iguazio's annual conference about managing and automating machine learning pipelines in order to bring data science into business applications. With an outstanding caliber of speakers and audience, the MLOps conference went beyond theory, shedding light on painful and successful machine learning experiences which involve...
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.
You’ve played around with machine learning, learned about the mysteries of neural networks, almost won a Kaggle competition and now you feel ready to bring all this to real world impact. It’s time to build some real AI-based applications.
Ever wonder if it’s possible to train machine learning (ML) models with regulated data which can’t be sent to the cloud? Has your edge solution gathered so much data that it just doesn’t make sense to send it all to
the cloud?