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

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.

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