Amazon SageMaker Studio for Data Scientists Training in US

Build, train, and deploy models with Amazon SageMaker Studio

Gain real-world experience in data processing, training, and deployment

Receive guidance from top industry professionals

Master everything from data wrangling to model optimization

Gain in-demand skills for high-level data science roles

Flexible and intensive Training

Convenient and hassle-free payment plans

4.78/5

6987 Enrolled

Overview

What you will learn:

Master end-to-end ML model development with Amazon SageMaker Studio
Utilize SageMaker Data Wrangler and AWS Glue for efficient data processing
Enhance model training and tuning with SageMaker Experiments
Harness the power of automated ML with SageMaker Autopilot and ensure fairness with bias detection
Seamlessly deploy models using SageMaker Pipelines and manage versions with Model Registry
Optimize model performance with hands-on experience using SageMaker Debugger

Learning Objectives

Upon finishing the training, you will:

Master data processing with SageMaker Data Wrangler and AWS Glue for streamlined ML workflows

Enhance model performance with real-time insights and alerts using SageMaker Debugger

Automate and streamline ML workflows using SageMaker Autopilot and SageMaker Pipelines

Gain expertise in model deployment and version control with SageMaker Model Registry

Detect and address bias in models using SageMaker Clarify to ensure fairness and transparency

Ready to get started?

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Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Related courses

Curriculum

JupyterLab Extensions in SageMaker Studio
Demonstration: SageMaker user interface demo

Using SageMaker Data Wrangler for data processing
Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
Using Amazon EMR
Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
Using AWS Glue interactive sessions
Using SageMaker Processing with custom scripts
Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker
Python SDK
SageMaker Feature Store
Hands-On Lab: Feature engineering using SageMaker Feature Store

SageMaker training jobs
Built-in algorithms
Bring your own script
Bring your own container
SageMaker Experiments
Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning
SageMaker Debugger
Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
Automatic model tuning
SageMaker Autopilot: Automated ML
Demonstration: SageMaker Autopilot
Bias detection
Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
SageMaker Jumpstart

SageMaker Model Registry
SageMaker Pipelines
Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker
Studio
SageMaker model inference options
Scaling
Testing strategies, performance, and optimization
Hands-On Lab: Inferencing with SageMaker Studio

Amazon SageMaker Model Monitor
Discussion: Case study
Demonstration: Model Monitoring

Accrued cost and shutting down
Updates

Environment setup
Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
Challenge 2: Create feature groups in SageMaker Feature Store
Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
(Optional) Challenge 4: Use SageMaker Debugger for training performance and model
optimization
Challenge 5: Evaluate the model for bias using SageMaker Clarify
Challenge 6: Perform batch predictions using model endpoint
(Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

Frequently asked questions

The Amazon SageMaker Studio for Data Scientists Training in the US is a specialist course that helps data scientists become proficient with Amazon SageMaker Studio's machine learning (ML) operations. Using AWS tools such as SageMaker Data Wrangler, SageMaker Experiments, and SageMaker Pipelines, the training covers data processing, model development, deployment, and monitoring. This course ensures mastery of end-to-end ML solutions in practical applications.

The Amazon SageMaker Studio for Data Scientists course is ideal for data scientists, machine learning professionals, or other related professionals. Although a degree in Computer Science, Data Science, or Engineering is not a prerequisite, applicants with a degree in any of these fields will benefit from it. It is advisable to have experience in Python, machine learning fundamentals, and cloud platforms such as AWS.

Our course is organized into six modules, spanning from data processing with SageMaker Data Wrangler and AWS Glue to model building, deployment, monitoring, and management with SageMaker Studio. Hands-on labs and real-world scenarios are woven in throughout, enabling practical experience with ML workflows, model tuning, and deployment with Amazon SageMaker.

To get ready for the exam, you must learn the fundamental modules, such as data wrangling with SageMaker Data Wrangler, model building, and performance tuning using SageMaker Autopilot. Use the practice labs, case studies, and demo sessions to have hands-on practice. Refresh your learning on important topics such as SageMaker Pipelines, SageMaker Model Registry, and bias detection will also be beneficial.

Amazon SageMaker Studio for Data Scientists certification is internationally accepted and well-respected, particularly in countries such as the US, Europe, and Asia. This certification proves that you can use one of the top platforms for data science and machine learning, making you more employable in the fast-growing tech sector.