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

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

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

MLFlow, Sage Maker, TFX & Helm for CI/CD deployment in MLOps and reliable monitoring of workflows in MLOps (Grafana)

Language: english

Note: 3.7/5 (54 notes) 13,125 students

Instructor(s): Junaid Zafar

Last update: 2022-08-05

What you’ll 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

 

Requirements

  • No prior experience is needed. You will learn everything you need to know.

 

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.

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.


 

Who this course is for

  • Beginner students and researchers curious to know about MLOps
  • Individuals looking to enter the data and AI industry.

 

Course 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
    • Code & ML Pipelines in MLOps
  • MLOps- ML Models and Code Pipelines
    • Three Levels of MLOps
  • MLOps Toolbox
    • MLOps Toolbox
  • MLOps Deployment Strategies
    • Deployment Strategies of MLOps including Blue- Green, Canary & A/ B
  • 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
  • TonY: LinkedIn as Case Example for large scale MLOps Deployment
    • TonY: LinkedIn as a large scale MLOps Deployment
  • Data Centric MLOps- An Introduction
    • MLOps- Model to Data Centric
  • DarkNet- Vetex AI for end to end ML Cycle
    • DarkNet- Vetex AI for MLOps
  • DeepChecks- How and when to use them in MLOps?
    • DeepChecks- How and when to use them in MLOps?
  • Challenges faced by MLOps
    • MLOps: A rising star with Challenges
  • 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
  • Deploy with Helm
    • Deployment of MLOps using Helm
  • Deployment Update using Helm
    • Keep Track of Deployed App via Helm
  • Sharing of Helm Charts
    • Helm Charts
  • Deep Fakes in MLops
    • Deep Fakes in MLOps/ AIOps
  • How Deep Fakes influence MLOps
    • Deep Fakes: Possible Threat to MLOps
  • What is DarkNet?
    • Introduction to Darknet
  • How Deep Fakes are created?
    • Deep Fakes posing challenges for MLOps
  • MLOps on AWS
    • Continuous Delivery of MLOps on AWS
    • MLOps vs AIOps
    • MLOps using Model Registry
    • Prometheus vs Grafana vs Zabbix
  • Knowledge Distillation in MLOps
    • Knowledge Distillation MLOps
    • VAEs in MLOps
    • Self Organizing Maps
    • ML Optimisation via Reinforcements
  • Tiny ML in MLOps
    • Tiny ML for MLops
  • Amazon Sage Maker
    • Amazon Sage Maker in MLOps
  • Feature Store Hopsworks- MLOps
    • Feature Store in MLOps

 

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