MLOps Cert- Basics, Deployment, Vertex AI & Feature Store


MLOps: ML Flow, TFX & Helm for CI/CD deployment in ML systems and reliable monitoring of workflows in MLOps (Grafana)

What you will learn

MLOps- What are MLOps (Machine Learning Opeartions)?

MLOps: Components including Continuous X & Versioning

MLOps: Life Cycle Process ( End to End Learning Flow)

MLOps: Model Testing & Model Packaging in PMML and ONNX

MLOps: Workflow Decomposition & Production Environment

MLOps: Pre- Computing Serving Patterns

MLOps: Data, Machine Learning and Code Pipelines

MLOps: Offline & Live Evaluation & Monitoring

MLOPs: LinkedIn as a case example of large scale ML Deployment

Description

This course introduces participants to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems on both cloud and Edge. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

This course encompasses the following topics;

1. Introduction of Data, Machine Learning Model and Code with reference to MLOps.

2. MLOps vs DevOps.

3. Where and How to Deploy MLOps.

4. Components of MLOps.

5. Continuous X & Versioning in MLOps.

6. Experiment Tracking in MLOps.

7. Three Levels of MLOps.

8. How to Implement MLOps?

9. CRISP (Q)- ML Life Cycle Process.

10. Complete MLOps Toolbox.

11. ML Flow library for MLOps.


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12. Tensor Flow Extended (TFX) for the deployment of MLOps.

13. PyCaret for the evaluation and deployment of MLOps.

14. Kubernetes as package manager for MLOps.

11. Google Cloud architectures for reliable and effective MLOps environments.

12. Working with AWS MLOps Services.

LAB Exercises with Solutions:

1. How to Deploy MLOps using Helm.

2. Make Changes with Helm.

3. Keep Track of Deployed Applications.

4. Share Helm Charts.

By the end of this course, you will be ready to:

  1. Design an ML production system end-to-end: data needs, modeling strategies, and deployment requirements.
  2. How to develop a prototype, deploy, and continuously improve a production-sized ML application.
  3. Understand data pipelines by gathering, cleaning, and validating datasets.
  4. Establish data lifecycle by leveraging data lineage.
  5. Use analytics to address model fairness and mitigate bottlenecks.
  6. Deliver deployment pipelines for model serving that require different infrastructures.
  7. Apply best practices and progressive delivery techniques to maintain a continuously operating production system.
English
language

Content

Rationale for MLOps

Introduction

MLOps Vs DevOps- How to Implement MLOps?

Why MLOps are necessary?

MLOps: Continuous X and Versioning

Continuous X and Versioning in MLOps

Levels & Components of MLOps

Three Levels of MLOps

MLOps- ML Models and Code Pipelines

Code & ML Pipelines in MLOps

MLOps Toolbox

MLOps Toolbox

Model Monitoring in MLOps

ML Model Monitoring in MLOps

TensorFlow X- ML Production Pipelines

TensorFlow X- ML Production Pipelines

MLFlow- MLOps Lifecycle Platform

ML Flow- A Platform for ML Life Cycle

MLOps- Kubernetes & HELM Package Manager

MLOps: HELM as Package Manager

Dockers for MLOps Workflow

Dockers in MLOps

PyCaret for MLOps Pipelines

PyCaret Library for ML Pipelines: MLOps

MLOps Challenges with AWS

Productionalizing MLOps & Challenges

Open Source Cloud MLOps

Design an open source cloud MLOps platform

Evaluating MLOps Planforms

Evaluation of MLOps in real time settings

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