MLOps Masterclass: Theory to DevOps to Cloud-native to AutoML
47Learn to go from theory to DevOps to MLOps platforms in this MLOps Master Class.
00:00 Intro
01:18 Noah Gift Background
04:14 Why do we need MLOPs?
05:06 Where the data science industry is headed?
06:57 Without DevOps you don't have MLOps
08:46 Continuous delivery is enabled by the Cloud and IAC
10:03 DataOps is like the water hookup in your home
11:23 Platform Automation solves the complexity of the data science industry
15:06 MLOPs Feedback loop
16:33 Create Once, but Deploy Everywhere. Good Example is Google AutoML
18:16 MLOps isn't data centric or model centric there is no silver bullet
21:52 MLOps use cases: Autonomous Driving is a good example
23:00 How to invest in technology: Primary and Secondary and Research
25:50 AWS and Azure are the leaders in the cloud
27:39 Secondary considerations: Splunk, Snowflake, BigQuery, Iguazio, etc
29:00 Leverage learning platform and metacognition
30:00 Key certifications
32:00 NFSOps is using managed file systems to build new cloud-native workflows
34:00 Kubernetes is the new gold standard for many distributed systems
35:00 Sagemaker has many use cases
36:21 Azure ML Studio
37:21 Google Vertex AI
37:48 Iguazio MLRun
41:00 Current issues in distributed systems
45:00 Apple Create ML Demo
51:00 Databricks Spark Clusters
57:00 MLFlow
01:00:37 What is DevOps?
01:03:16 Creating a new Github repo
01:05 Developering with AWS Cloud9
01:20:26 Setup Github Actions
01:23:00 Walkthrough of Python MLOps cookbook example using a sklearn project
01:35:00 Pushing sklearn flask microservice to Amazon ECR
01:39:00 Setup AWS App Runner for MLOps Microservice inference
01:43:00 Setup Continuous Delivery of MLOps Microservice using AWS Code Build
02:06:00 Comparing MLOps Platforms Databricks, Sagemaker and MLRun
02:31:00 Deploying MLRun open source MLOps with Colab Notebook
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
02:06:00 Comparing MLOps Platforms Databricks, Sagemaker and MLRun
02:31:00 Deploying MLRun open source MLOps with Colab Notebook
#aws #mlops #cloudnative #automl #devops