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.
Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:
There is no exam or certification for this course.
You´ll have the perfect starting point for your training with these prerequisites:
- 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
- The Elements of Data Science (digital course), or equivalent experience
- Machine Learning Terminology and Process (digital course)
Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.
In this 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 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
This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud:
- DevOps engineers
- ML engineers
- Developers/operations with responsibility for operationalizing ML models
The Virtual Classroom is an online room, where you will join your instructor and fellow classmates in real time. Everything happens live and you can interact freely, discuss, ask questions, and watch your instructor present on a whiteboard, discuss the courseware and slides, work with labs, and review.
Yes, you can sit exams from all the major Vendors like Microsoft, Cisco etc from the comfort of your home or office.
With Readynez you do any course form the comfort of your home or office. Readynez provides support and best practices for your at-home classroom and you can enjoy learning with minimal impact on your day-to-day life. Plus you'll save the cost and the environmental burden of travelling.
Well, learning is limitless, when you are motivated, but you need the right path to achieve what you want. Readynez consultants have many years of experience customizing learner paths and we can design one for you too. We are always available with help and guidance, and you can reach us on the chat or write us at email@example.com.