Accelerating Data Science to Production with MLOps Best Practices
Accelerating Data Science to Production with MLOps Best Practices
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
May 2021
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.
Automated Model Management for CPG Trade Effectiveness
In this webinar, we will talk about enterprise scale, fully automated MLOps solutions for Trade Promotions, looking into the impact this can create for CPG companies, through a real-world example.
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.
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.
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
Iguazio Named A Fast Moving Leader by GigaOm in the 'Radar for MLOps' Report
At Iguazio, we’ve spoken and written at length about the challenges of bringing data science to production. The complexity of operationalizing ML can generate huge costs in terms of work hours and compute resources, especially as successful projects get scaled up and expanded. We’re proud to share that the Iguazio Data Science Platform has been named a fast moving leader in the GigaOm Radar for MLOps report.