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Classroom Contents
MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML
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- 1 Intro
- 2 Noah Gift Background
- 3 Why do we need MLOPs?
- 4 Where the data science industry is headed?
- 5 Without DevOps you don't have MLOps
- 6 Continuous delivery is enabled by the Cloud and IAC
- 7 DataOps is like the water hookup in your home
- 8 Platform Automation solves the complexity of the data science industry
- 9 MLOPs Feedback loop
- 10 Create Once, but Deploy Everywhere. Good Example is Google AutoML
- 11 MLOps isn't data centric or model centric there is no silver bullet
- 12 MLOps use cases: Autonomous Driving is a good example
- 13 How to invest in technology: Primary and Secondary and Research
- 14 AWS and Azure are the leaders in the cloud
- 15 Secondary considerations: Splunk, Snowflake, BigQuery, Iguazio, etc
- 16 Leverage learning platform and metacognition
- 17 Key certifications
- 18 NFSOps is using managed file systems to build new cloud-native workflows
- 19 Kubernetes is the new gold standard for many distributed systems
- 20 Sagemaker has many use cases
- 21 Azure ML Studio
- 22 Google Vertex AI
- 23 Iguazio MLRun
- 24 Current issues in distributed systems
- 25 Apple Create ML Demo
- 26 Databricks Spark Clusters
- 27 MLFlow
- 28 What is DevOps?
- 29 Creating a new Github repo
- 30 Developering with AWS Cloud9
- 31 Setup Github Actions
- 32 Walkthrough of Python MLOps cookbook example using a sklearn project
- 33 Pushing sklearn flask microservice to Amazon ECR
- 34 Setup AWS App Runner for MLOps Microservice inference
- 35 Setup Continuous Delivery of MLOps Microservice using AWS Code Build
- 36 Comparing MLOps Platforms Databricks, Sagemaker and MLRun
- 37 Deploying MLRun open source MLOps with Colab Notebook
- 38 Comparing MLOps Platforms Databricks, Sagemaker and MLRun
- 39 Deploying MLRun open source MLOps with Colab Notebook
