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
4.78/56987 EnrolledEnrolled Learners
4.78/56987 EnrolledEnrolled Learners
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
Upcoming sessions
Curriculum
1
JupyterLab Extensions in SageMaker Studio
2
Demonstration: SageMaker user interface demo
1
Using SageMaker Data Wrangler for data processing
2
Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
3
Using Amazon EMR
4
Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
5
Using AWS Glue interactive sessions
6
Using SageMaker Processing with custom scripts
7
Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker
8
Python SDK
9
SageMaker Feature Store
10
Hands-On Lab: Feature engineering using SageMaker Feature Store
1
SageMaker training jobs
2
Built-in algorithms
3
Bring your own script
4
Bring your own container
5
SageMaker Experiments
6
Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning
7
SageMaker Debugger
8
Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
9
Automatic model tuning
10
SageMaker Autopilot: Automated ML
11
Demonstration: SageMaker Autopilot
12
Bias detection
13
Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
14
SageMaker Jumpstart
1
SageMaker Model Registry
2
SageMaker Pipelines
3
Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker
4
Studio
5
SageMaker model inference options
6
Scaling
7
Testing strategies, performance, and optimization
8
Hands-On Lab: Inferencing with SageMaker Studio
1
Amazon SageMaker Model Monitor
2
Discussion: Case study
3
Demonstration: Model Monitoring
1
Accrued cost and shutting down
2
Updates
1
Environment setup
2
Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
3
Challenge 2: Create feature groups in SageMaker Feature Store
4
Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
5
(Optional) Challenge 4: Use SageMaker Debugger for training performance and model
6
optimization
7
Challenge 5: Evaluate the model for bias using SageMaker Clarify
8
Challenge 6: Perform batch predictions using model endpoint
9
(Optional) Challenge 7: Automate full model development process using SageMaker Pipeline
Meet your Trainer
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.
Learning Outcomes
Upon finishing the training, you will:
1
Master data processing with SageMaker Data Wrangler and AWS Glue for streamlined ML workflows
2
Enhance model performance with real-time insights and alerts using SageMaker Debugger
3
Automate and streamline ML workflows using SageMaker Autopilot and SageMaker Pipelines
4
Gain expertise in model deployment and version control with SageMaker Model Registry
5
Detect and address bias in models using SageMaker Clarify to ensure fairness and transparency
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
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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.
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