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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:
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.
Before attending this accelerated course, you should have:
Olemme kouluttaneet kymmenen vuoden aikana yli 134.561 opiskelijaa. Heitä kaikkia on pyydetty arvioimaan pikakurssimme. Tällä hetkellä 94,80% on sitä mieltä, että Firebrand on ylittänyt heidän odotuksensa:
"The trainer was absolutely fantastic. The course materials was explained to point and easily understood. All questions answered I had. I am feeling extremely confident of passing the exam and being certified."
Peter Royle, Lineage Logistics. (29/4/2024 (Maanantai) - 2/5/2024 (Torstai))
"It was a great week worth of training. Very intense course so not for the faint of heart! If you do decide to do a course here, be prepared but be excited. It's a great place to learn these courses and appreciate my instructor and everyone for making it a great experience!"
Robert Sizeland, Rhino Analytics. (29/4/2024 (Maanantai) - 2/5/2024 (Torstai))
"Good trainer. Course has a lot of content but trainer did well to make it entertaining."
Uchenna Onyia. (8/3/2024 (Perjantai) - 10/3/2024 (Sunnuntai))
"Intensive course with good content"
Karen Asamoah, BPP. (4/3/2024 (Maanantai) - 10/3/2024 (Sunnuntai))
"Very in-depth knowledge of the trainer made the course enjoyable"
A.K.. (4/3/2024 (Maanantai) - 7/3/2024 (Torstai))
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10/2/2025 (Maanantai) |
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24/3/2025 (Maanantai) |
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5/5/2025 (Maanantai) |
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16/6/2025 (Maanantai) |
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