We can host this training at your preferred location. Contact us!

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators. The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors

Who should take this course

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

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)

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 inference
Describe why monitoring is important
Detect data drifts in the underlying input data
Demonstrate how to monitor ML models for bias
Explain how to monitor model resource consumption and latency
Discuss how to integrate human-in-the-loop reviews of model results in production

Day 1

Module 0: Welcome

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

Day 2

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

Day 3

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, and Feature Store
Solving the Problem(s)
Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

Course review
Activity: MLOps Action Plan Workbook
Wrap-up

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

MLOps Engineering on AWS Training Course in Germany

The Federal Republic of Germany is the second most populous country in Europe and is located in Central Europe. The official language of the country is German. Germany is one of the richest countries in the world. The main exports of the country include motor vehicles and iron and steel products.

Here are some fun facts about Germany:
The fairy tale writer, the Brothers Grimm, came from Germany and wrote many famous stories such as Cinderella, Snow White, and Sleeping Beauty.
Germany is home to the largest theme park in Europe, the Europa-Park.
The famous composer Ludwig van Beethoven was born in Germany.
The Autobahn, the German highway system, is known for having no general speed limit.

Berlin was divided by the Berlin Wall from 1961 to 1989. Known for its street art, Berlin has many colorful murals and graffiti throughout the city. Also, Berlin is home to many famous museums, such as the Pergamon Museum and the Museum Island. Many clubs and bars stay open until the early hours of the morning in this big city.

Another popular city is Munich, which is famous for its Oktoberfest beer festival that attracts millions of visitors every year. Munich is also home to many historic buildings, including Nymphenburg Palace and the Marienplatz town square.

The country's capital and largest city is Berlin, however Frankfurt is considered to be the business and financial center of Germany. It is home to the Frankfurt Stock Exchange, the European Central Bank, and many other financial institutions. Because of its central location within Europe and its status as a major financial hub, Frankfurt is often referred to as the "Mainhattan," a play on the city's name and its association with the Manhattan financial district in New York City.

Frankfurt is also a major transportation hub, with the largest airport in Germany and one of the largest in Europe, Frankfurt Airport. Additionally, it is a popular destination for tourists, with its historic city center, beautiful parks, and vibrant cultural scene.

Some of the top German technology companies like Siemens AG, Bosch, SAP SE, Deutsche Telekom, Daimler AG and Volkswagen has business centers in Frankfurt. The country has a strong tradition of engineering and innovation, and is home to many other world-class technology companies and research institutions.

Tailored to meet the specific needs of Germany, Bilginç IT Academy combines cutting-edge training methodologies with our comprehensive range of Certification Exam preparation courses and accredited corporate training programs. Experience a transformative approach to IT training that will redefine your expectations.

MLOps Engineering on AWS Training in Germany

Upcoming Trainings

MLOps Engineering on AWS Training in Germany

Prerequisites

What You Will Learn

Outline

HOW TO LEARN MLOPS IN 2024

Upcoming Trainings