This course covers the learning outcomes and goals of transitioning from theory to DevOps to MLOps platforms, including Cloud-native and AutoML. The course teaches skills such as understanding the importance of DevOps in MLOps, leveraging Cloud services like AWS and Azure, using tools like Kubernetes, Sagemaker, and MLRun, and setting up Continuous Delivery pipelines. The teaching method includes theoretical explanations, practical demonstrations, and comparisons of different MLOps platforms. The intended audience for this course includes data scientists, machine learning engineers, DevOps professionals, and anyone interested in deploying machine learning models efficiently in production environments.
MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML
Overview
Syllabus
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
Taught by
Pragmatic AI Labs
