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On this accelerated Amazon SageMaker Studio for Data Scientists course, you will learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.
This 2 day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.
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
At the end of this course, you’ll achieve your Amazon SageMaker Studio for Data Scientists certification.
Through Firebrand’s Lecture | Lab | Review methodology, you’ll get certified at twice the speed of the traditional training and get access to courseware, learn from certified instructors, and train in a distraction-free environment.
This course is ideal for:
Other accelerated training providers rely heavily on lecture and independent self-testing and study.
Effective technical instruction must be highly varied and interactive to keep attention levels high, promote camaraderie and teamwork between the students and instructor, and solidify knowledge through hands-on learning.
Module 1: Amazon SageMaker Setup and Navigation
Module 2: Data Processing Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data.
Module 3: Model Development Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices.
Module 4: Deployment and Inference Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model.
Module 5: Monitoring Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift.
Module 6: Managing SageMaker Studio Resources and Updates List resources that accrue charges.
The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. Students can choose among basic, intermediate, and advanced versions of the instructions. Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK.
At the end of this accelerated course, you’ll achieve your Amazon SageMaker Studio for Data Scientists.
Your accelerated course includes:
Before attending this accelerated course, you should have:
Unsure whether you meet the prerequisites? Don’t worry. Your training consultant will discuss your background with you to understand if this course is right for you.
Here's the Firebrand Training review section. Since 2001 we've trained exactly 134561 students and asked them all to review our Accelerated Learning. Currently, 96.41% have said Firebrand exceeded their expectations.
Read reviews from recent accelerated courses below or visit Firebrand Stories for written and video interviews from our alumni.
"Highly recommended!"
Yuchen Wang, Engineer. (8/5/2023 (Monday) to 11/5/2023 (Thursday))
"Great instructor, very disciplined, good knowledge and very patiently explained. Its like a boot camp and a very structured approach is taken to make you ready for exam and also increase your knowledge. Any one can join if you meet the prerequisites."
Mahesh Kukrani. (8/5/2023 (Monday) to 11/5/2023 (Thursday))
"Very skilled teacher and helpful discussions."
Anonymous (8/5/2023 (Monday) to 11/5/2023 (Thursday))
"Thanks for this intensive and quite productive training session !"
Alain Dedeurwaerder. (22/5/2018 (Tuesday) to 24/5/2018 (Thursday))
"Very impressed with the dedication from the instructor and how well structured the sessions are. The course is very intense since it's only 3 days long, however, the quality of the training makes it worthwhile. I'd recommend going in person since you will find it much easier to focus and likely get more out of the course overall."
Simon Brown, Softcat. (8/9/2023 (Friday) to 10/9/2023 (Sunday))
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