Systems | Development | Analytics | API | Testing

%term

How Data Affects Healthcare | Rise of The Data Cloud | Snowflake

Data driven healthcare, anonymized data hackathons in a digital data sandbox, how to leverage the power of data for good, compute on demand, how the pandemic has affected digital adoption and how shifting to the cloud impacts patients are just some of the topics being covered in today's episode of Snowflake's Rise of the Data Cloud. Join us as Ashok Chennuru, Chief Data and Analytics Officer at Anthem gives us a peek into the world of AI and healthcare.

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

APIOps for Standardization Without Hindrance (Destination: Scale)

Typically, there are two options to ensuring APIs have the right governance: manual checks or long documentation (or both). There is now a third option in APIOps — integrating your GitOps process with the API lifecycle, automating the enforcement of API standards from design time. This ensures API security, quality, consistency and resiliency across distributed teams at scale, therefore improving productivity for developers and operators whilst reducing risk overall.