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Intro

Noah Gift Background

Why do we need MLOPs?

Where the data science industry is headed?

Without DevOps you don't have MLOps

Continuous delivery is enabled by the Cloud and IAC

DataOps is like the water hookup in your home

Platform Automation solves the complexity of the data science industry

MLOPs Feedback loop

Create Once, but Deploy Everywhere. Good Example is Google AutoML

MLOps isn't data centric or model centric there is no silver bullet

MLOps use cases: Autonomous Driving is a good example

How to invest in technology: Primary and Secondary and Research

AWS and Azure are the leaders in the cloud

Secondary considerations: Splunk, Snowflake, BigQuery, Iguazio, etc

Leverage learning platform and metacognition

Key certifications

NFSOps is using managed file systems to build new cloud-native workflows

Kubernetes is the new gold standard for many distributed systems

Sagemaker has many use cases

Azure ML Studio

Google Vertex AI

Iguazio MLRun

Current issues in distributed systems

Apple Create ML Demo

Databricks Spark Clusters

MLFlow

What is DevOps?

Creating a new Github repo

Developering with AWS Cloud9

Setup Github Actions

Walkthrough of Python MLOps cookbook example using a sklearn project

Pushing sklearn flask microservice to Amazon ECR

Setup AWS App Runner for MLOps Microservice inference

Setup Continuous Delivery of MLOps Microservice using AWS Code Build

Comparing MLOps Platforms Databricks, Sagemaker and MLRun

Deploying MLRun open source MLOps with Colab Notebook

Comparing MLOps Platforms Databricks, Sagemaker and MLRun

Deploying MLRun open source MLOps with Colab Notebook

Description:

Dive into a comprehensive masterclass on MLOps, covering the journey from theory to practical implementation. Explore the evolution of data science, the importance of DevOps in MLOps, and the role of cloud-native technologies and AutoML. Learn about key MLOps concepts, including continuous delivery, platform automation, and feedback loops. Discover various MLOps use cases, investment strategies in technology, and leading cloud platforms. Gain insights into essential certifications, distributed systems, and popular MLOps tools like Kubernetes, SageMaker, and Vertex AI. Follow hands-on demonstrations of creating GitHub repositories, setting up CI/CD pipelines, and deploying microservices using AWS services. Compare different MLOps platforms and explore open-source solutions. Perfect for data scientists, engineers, and professionals looking to master the intricacies of operationalizing machine learning in modern cloud environments.

MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML

Pragmatic AI Labs

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#Computer Science #Machine Learning #MLOps #DevOps #Programming #Cloud Computing #Amazon Web Services (AWS) #Kubernetes #AutoML