MLOps Engineering on AWS Course Overview


The MLOps Engineering on AWS course provides an in-depth review of all the steps and skills required to deploy ML models on Amazon Web Services (AWS). This course is designed to help developers and engineers understand the challenges and best practices of MLOps.
This course covers the challenges of MLOps and the importance of using best practices and processes throughout the development life cycle. You’ll learn the fundamentals of how to set up a development pipeline, how to integrate machine learning into a continuous integration/ continuous delivery (CI/CD) workflow, and how to deploy ML models on AWS. In addition, you’ll learn about best practices for scaling ML/AI models and for monitoring and debugging mobile applications.
This course is ideal for developers, engineers, or data scientists looking to gain a practical understanding of MLOps on AWS. You should be familiar with basic AWS cloud concepts and have some experience in the development of machine learning models.
MLOps Engineering on AWS
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The 1-on-1 Advantage

Get 1-on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

Module 1: Introduction to MLOps
  • Machine learning operations
  • Goals of MLOps
  • From DevOps to MLOps
  • ML workflow
  • MLOps view of ML workflow
  • MLOps cases
  • Intro to build, train, and evaluate machine learning models
  • MLOps security
  • 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
  • Introduction to deployment operations
  • Model packaging
  • Inference
  • Lab: Deploy your model to production
  • SageMaker production variants
  • Deploying to the edge
  • Lab: Conduct A/B testing
  • 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, and Feature Store
  • Solving the Problem(s)
  • Wrap-up
Live Online Training (Duration : 24 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

1950 + If you accept merging of other students. Per Participant Incl. Official Course-Book & excluding VAT/GST
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
1700 Per Participant Incl. Official Course-Book & excluding VAT/GST
Online
12 - 14 Jun 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
03 - 05 Jul 09:00 AM - 05:00 PM CST
(8 Hours/Day)
AWS Customer Obsession Award in August 2022.
winner of the AWS Training Partner of the Year Award – APJ Region for 2022. The award was given in a glittering ceremony in Las Vegas attended by Subo
MLOps Engineering on AWS
Course Prerequisites

The prerequisites for MLOps Engineering on AWS Training are as follows:
• Basic knowledge in Machine Learning and Python programming
• Working experience in Linux-based Operating System
• Good understanding of Continuous Delivery and Continuous Integration using DevOps tools
• Familiarity with AWS Cloud Services
• Understanding of Containers (Docker and Kubernetes) and container orchestration
• Understanding of Infrastructure as Code concepts
• Experience in building CI/CD pipelines with Jenkins

Target Audience


The target audience for MLOps Engineering on AWS training would be technology professionals who have experience in building and deploying Machine Learning models, as well as individuals who want to obtain expertise in managing and executing an MLOps workflow on the AWS Cloud
This course is also geared towards teams in organizations developing an MLOps pipeline around their existing machine learning models to make them more automated and simplified
Specific roles that would benefit from this course include ML developers, ML engineers, data scientists, cloud engineers, DevOps engineers and operations professionals
This training is ideal for those who want to gain an in-depth understanding of the process, tools and technologies needed to design, execute and manage MLOps on AWS

Learning Objectives of MLOps Engineering on AWS


MLOps Engineering on AWS Training provides an understanding of the fundamentals, methods and best practices for helping developers and DevOps teams use automation, scalability and high availability in achieving a successful deployment of machine learning models on AWS.
The objectives of this training include:
1. Understand the core concepts of MLOps and the key components of its architecture
2. How to prepare, deploy, and operate ML models on AWS services, such as Amazon Sagemaker, Amazon EC2, and Amazon ECS
3. Configure data management and analytics pipelines on AWS
4. Deploy ML models in production on AWS layers
5. Monitor and optimize ML applications with Amazon Cloudwatch
6. Automate common tasks related to machine learning lifecycle using AWS CloudFormation and automation scripts
7. Understand the security and compliance requirements of deploying machine learning models to production on AWS.

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
on Trust Pilot
Student Name Feedback
Rachel Lim
United States
A1. Thanks Sachin for the well delivered course. Sachin is familiar with the course content and delivered it with clarity. He is generous with sharing his experience and shared many analogies that I found useful to understand complex topics.
Manikandan N P
United States
A1. Yes. Timely delivery and extended time provided for participants ask questions
Torsha Sett
United States
A1. Great job done
Shivani Gupta
United States
A1. he was able to answer our questions, he made the session engaging.
Frederic Marechal
United States
A1. Sachin is patient, do not hesitate to repeat concept and provide indepth explanations.

FAQ's

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1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
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The Fee includes:
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
Duration of Ultra-Fast Track is 50% of the duration of the Standard Track. Yes(course content is same).

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