What you'll learn
- Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
- Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
- And much more
Who should take this course
- Experienced data scientists who are proficient in ML and deep learning fundamentals.
- Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
What experience you'll need
We recommend that all students complete the following AWS course prior to attending this course:
- AWS Technical Essentials (1–day AWS instructor led course)
We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:
Type: Classroom (virtual and in person)
Length: 3 days
This course is offered in English.
We regularly update our courses based on customer feedback and AWS service updates. As a result, course content may vary between languages while we localize these updates.
Need more information?
Download the course outline for more information about what this course covers.
Looking for private training for your team?
With AWS-delivered private training, your team will learn actionable best practices together, tailored to your specific use cases.
Thinking about taking an exam?
Find a related exam to reinforce your learning.