How Feature Stores Accelerate & Simplify Deployment of AI to Production MLOPs Live #13

The breakdown:

00:00 - Intro
02:15 - MLOps Overview
05:03 - Feature Engineering
07:44 - MLOps Workflow
10:44 - Solution: Feature Store
14:25 - Feature Store Competitive Landscape
17:03 - Features of a Feature Store
21:01 - CTO: Feature Store Sneakpeak
25:55 - Python Code example
27:57 - ML Pipeline example
30:07 - Covid-19 Patient Deterioration
33:26 - LIVE DEMO
52:45 - QA

Simplifying Deployment of ML in Federated Cloud and Edge Environments - MLOPs Live #12 - with AWS

We discuss some common applications for machine learning at the edge and the main challenges associated with deploying distributed cloud and edge applications. We then wrap up the session with a live demo showing how to run a distributed cloud or edge application on Amazon Cloud and Outposts with the Iguazio Data Science Platform.

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.

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.

Announcing Iguazio Version 3.0: Breaking the Silos for Faster Deployment

We’re delighted to announce the release of the Iguazio Data Science Platform version 3.0. Data Engineers and Data Scientists can now deploy their data pipelines and models to production faster than ever with features that break down silos between Data Scientists, Data Engineers and ML Engineers and give you more deployment options . The development experience has been improved, offering better visibility of the artifacts and greater freedom of choice to develop with your IDE of choice.


How to Tap into Higher-Level Abstraction, Efficiency & Automation to Simplify your AI/ML Journey

You’ve already figured out that your data science team cannot keep developing models on their laptops or a managed automated machine learning (AutoML) service and keep their models there. You want to put artificial intelligence (AI) and machine learning (ML) into action and solve real business problems.