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MLOps Engineering on AWS

This course builds on and extends the DevOps methodology used in software development to build, train and deploy machine learning (ML) models. In this three days course you will learn about the four-level MLOps maturity framework. The course outlines the importance of data, model and code to successful ML deployments. The course also discusses the use of tools and processes to monitor and take action when the model prediction shifts from the key performance indicators.

The course is intended for MLOps engineers who want to produce and monitor ML models in the AWS Cloud. This course is also for DevOps engineers who are responsible for deploying and maintaining ML models. We recommend that the attendees have completed AWS Technical Essentials, DevOps Engineering on AWS and Practical Data Science with Amazon SageMaker

The program is taught with the help of presentations, hands-on labs, demonstrations and group activities. You will be able to prepare for the AWS Certified Machine Learning certification

3 Days

Live Class

Certificate on completion

45,000

Choose a date

You will learn about the following

  • Describe machine learning operations
  • Understand the key differences between DevOps and MLOps
  • Describe the machine learning workflow
  • Discuss the importance of communications in MLOps
  • Explain end-to-end options for automation of ML workflows
  • List key Amazon SageMaker features for MLOps automation
  • Build an automated ML process that builds, trains, tests, and deploys models
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for
    ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inferenceDescribe why monitoring is important

What experience you need

Required:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience

Recommended:

    • The Elements of Data Science (digital course), or equivalent experience
    • Machine Learning Terminology and Process (digital course)

Who should take this course

  • ML data platform engineers
  • DevOps engineers
  • Developers/operations staff with responsibility for operationalizing ML models

Activities

  • How to deploy your own models in the AWS Cloud
  • How to automate workflows for building, training, testing, and deploying ML models
  • The different deployment strategies for implementing ML models in production
  • How to monitor for data drift and concept drift that could affect prediction and alignment with business expectation

Module 0: Introduction

  • Course Introduction

Module 1: Introduction to MLOps

  • Machine learning operations
  • Goals of MLOps
  • Communication
  • From DevOps to MLOps
  • ML workflow
  • Scope
  • MLOps view of ML workflow
  • MLOps cases

Module 2: MLOps Development

  • Intro to build, train, and evaluate machine learning models
  • MLOps security
  • Automating
  • Apache Airflow
  • Kubernetes integration for MLOps
  • Amazon SageMaker for MLOps
  • Lab: Bring your own algorithm to an MLOps pipeline
  • Demonstration: Amazon SageMaker
  • Intro to build, train, and evaluate machine learning models
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

  • Introduction to deployment operations
  • Model packaging
  • Inference
  • Lab: Deploy your model to production
  • SageMaker production variants
  • Deployment strategies
  • Deploying to the edge
  • Lab: Conduct A/B testing
  • Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

  • Lab: Troubleshoot your pipeline
  • The importance of monitoring
  • Monitoring by design
  • Lab: Monitor your ML model
  • Human-in-the-loop
  • Amazon SageMaker Model Monitor
  • Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry & Feature Store
  • Solving the Problem(s)
  • Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

  • Course review
  • Activity: MLOps Action Plan Workbook
  • Wrap-up

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Exam Readiness

AWS Certified Machine Learning - Speciality

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certification

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified MLOps Engineer – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

FAQs

Yes, we are an AWS Advanced Tier Training Partner

Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.

To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

You may reach out at the contact number listed on our official website or write to us at info@cloudwizard.wpenginepowered.com.

Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order

There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.

1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course completion certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).

MOBILE LAYOUT

MLOps Engineering on AWS

3 Days

Live Class

Certificate on completion

45,000

(Taxes Extra)

Choose a date

In this MLOps Engineering on AWS course, you will learn to:

  • Describe machine learning operations
  • Understand the key differences between DevOps and MLOps
  • Describe the machine learning workflow
  • Discuss the importance of communications in MLOps
  • Explain end-to-end options for automation of ML workflows
  • List key Amazon SageMaker features for MLOps automation
  • Build an automated ML process that builds, trains, tests, and deploys models
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for
    ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inferenceDescribe why monitoring is important

Attendees of this MLOps Engineering course are advised to have the following: 

Required:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience

Recommended:

    • The Elements of Data Science (digital course), or equivalent experience
    • Machine Learning Terminology and Process (digital course)

The following group of individuals are likely to benefit

  • ML data platform engineers
  • DevOps engineers
  • Developers/operations staff with responsibility for operationalizing ML models

In this course, you will:

  • How to deploy your own models in the AWS Cloud
  • How to automate workflows for building, training, testing, and deploying ML models
  • The different deployment strategies for implementing ML models in production
  • How to monitor for data drift and concept drift that could affect prediction and alignment with business expectation

Module 0: Introduction

  • Course Introduction

Module 1: Introduction to MLOps

  • Machine learning operations
  • Goals of MLOps
  • Communication
  • From DevOps to MLOps
  • ML workflow
  • Scope
  • MLOps view of ML workflow
  • MLOps cases

Module 2: MLOps Development

  • Intro to build, train, and evaluate machine learning models
  • MLOps security
  • Automating
  • Apache Airflow
  • Kubernetes integration for MLOps
  • Amazon SageMaker for MLOps
  • Lab: Bring your own algorithm to an MLOps pipeline
  • Demonstration: Amazon SageMaker
  • Intro to build, train, and evaluate machine learning models
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

  • Introduction to deployment operations
  • Model packaging
  • Inference
  • Lab: Deploy your model to production
  • SageMaker production variants
  • Deployment strategies
  • Deploying to the edge
  • Lab: Conduct A/B testing
  • Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

  • Lab: Troubleshoot your pipeline
  • The importance of monitoring
  • Monitoring by design
  • Lab: Monitor your ML model
  • Human-in-the-loop
  • Amazon SageMaker Model Monitor
  • Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry & Feature Store
  • Solving the Problem(s)
  • Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

  • Course review
  • Activity: MLOps Action Plan Workbook
  • Wrap-up

Exam Readiness

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certifications

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified MLOps Engineer – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Tablet View

MLOps Engineering on AWS

This course builds on and extends the DevOps methodology used in software development to build, train and deploy machine learning (ML) models. In this three days course you will learn about the four-level MLOps maturity framework. The course outlines the importance of data, model and code to successful ML deployments. The course also discusses the use of tools and processes to monitor and take action when the model prediction shifts from the key performance indicators.

The course is intended for MLOps engineers who want to produce and monitor ML models in the AWS Cloud. This course is also for DevOps engineers who are responsible for deploying and maintaining ML models. We recommend that the attendees have completed AWS Technical Essentials, DevOps Engineering on AWS and Practical Data Science with Amazon SageMaker

The program is taught with the help of presentations, hands-on labs, demonstrations and group activities. You will be able to prepare for the AWS Certified Machine Learning certification

3 Days

Live Class

Certificate on completion

45,000

Choose a date

Objectives

In this MLOps Engineering on AWS course, you will learn to:

  • Describe machine learning operations
  • Understand the key differences between DevOps and MLOps
  • Describe the machine learning workflow
  • Discuss the importance of communications in MLOps
  • Explain end-to-end options for automation of ML workflows
  • List key Amazon SageMaker features for MLOps automation
  • Build an automated ML process that builds, trains, tests, and deploys models
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for
    ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inferenceDescribe why monitoring is important

Prerequisites

Attendees of this MLOps Engineering course are advised to have the following: 

Required:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience

Recommended:

    • The Elements of Data Science (digital course), or equivalent experience
    • Machine Learning Terminology and Process (digital course)

Intended Audience

The following group of individuals are likely to benefit

  • ML data platform engineers
  • DevOps engineers
  • Developers/operations staff with responsibility for operationalizing ML models

Activities

In this course, you will:

  • How to deploy your own models in the AWS Cloud
  • How to automate workflows for building, training, testing, and deploying ML models
  • The different deployment strategies for implementing ML models in production
  • How to monitor for data drift and concept drift that could affect prediction and alignment with business expectation

Module 0: Introduction

  • Course Introduction

Module 1: Introduction to MLOps

  • Machine learning operations
  • Goals of MLOps
  • Communication
  • From DevOps to MLOps
  • ML workflow
  • Scope
  • MLOps view of ML workflow
  • MLOps cases

Module 2: MLOps Development

  • Intro to build, train, and evaluate machine learning models
  • MLOps security
  • Automating
  • Apache Airflow
  • Kubernetes integration for MLOps
  • Amazon SageMaker for MLOps
  • Lab: Bring your own algorithm to an MLOps pipeline
  • Demonstration: Amazon SageMaker
  • Intro to build, train, and evaluate machine learning models
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

  • Introduction to deployment operations
  • Model packaging
  • Inference
  • Lab: Deploy your model to production
  • SageMaker production variants
  • Deployment strategies
  • Deploying to the edge
  • Lab: Conduct A/B testing
  • Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

  • Lab: Troubleshoot your pipeline
  • The importance of monitoring
  • Monitoring by design
  • Lab: Monitor your ML model
  • Human-in-the-loop
  • Amazon SageMaker Model Monitor
  • Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry & Feature Store
  • Solving the Problem(s)
  • Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

  • Course review
  • Activity: MLOps Action Plan Workbook
  • Wrap-up

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Exam Readiness

AWS Certified Machine Learning - Speciality

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certification

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified MLOps Engineer – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

FAQs

Yes, we are an AWS Advanced Tier Training Partner

Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.

To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

You may reach out at the contact number listed on our official website or write to us at info@cloudwizard.wpenginepowered.com.

Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order

There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.

1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course completion certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).

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