English | Size: 1.33 GB
Genre: eLearning
Learn 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
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