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Modern Software Development For Today's Systems & Applications
Building a Single Pipeline for Data Integration and ML with Azure Synapse Analytics and Iguazio
Across organizations large and small, ML teams are still faced with data silos that slow down or halt innovation. Read on to learn about how enterprises are tackling these challenges, by integrating with any data types to create a single end-to-end pipeline and rapidly run AI/ML with Azure Synapse Analytics with Iguazio.
Iguazio Product tutorial 2021
The Iguazio Data Science Platform enables you to develop, deploy and manage real-time AI applications at scale. It provides data science, data engineering and DevOps teams with one platform to operationalize machine learning and rapidly deploy operational ML pipelines. The platform includes an online and offline feature store, fully integrated with automated model monitoring and drift detection, model serving and dynamic scaling capabilities, all packaged in an open and managed platform.
Accelerating Data Science to Production with MLOps Best Practices
Accelerating Data Science to Production with MLOps Best Practices
Expert Panel: AI for Connected Vehicles
In this panel, experts from NetApp, NVIDIA, Strategy Analytics and Iguazio will discuss the main use cases, and also examine the challenges and solutions that exist for building AI applications for connected vehicles.
AWS & Iguazio Bring Data Science to Life: Develop on SageMaker | Deploy on Iguazio
Learn more about the joint AWS & Iguazio solution: https://www.iguazio.com/partners/aws/
Start working with MLRun, the open-source MLOps orchestration framework: https://github.com/mlrun/mlrun
End-to-End MLOps Demo of the Iguazio Feature Store
Easily engineer online and offline features, share them across teams and ML applications with minimal development and integration effort. Find out how in this demo.
Automating and Governing AI over Production Data on Azure - MLOPs Live #14 w/Microsoft
Many enterprises today face numerous challenges around handling data for AI/ML. They find themselves having to manually extract datasets from a variety of sources, which wastes time and resources. In this session, we discuss end-to-end automation of the production pipeline and how to govern AI in an automated way. We touch upon setting up a feedback loop, generating explainable AI and doing all of this — at scale.
Industrializing Enterprise AI with the Right Platform - MLOps Live #9 - With NVIDIA
We discuss how enterprises need a platform that brings together tools to streamline data science workflow with leading edge infrastructure that can tackle the most complex ML models — one that can bring innovative concepts into production sooner, integrated within your existing IT/DevOps-grounded approach.