Amazon SageMaker Studio for Data Scientists Training
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 our training includes
- Master Amazon SageMaker Studio for end-to-end ML model development
- Use SageMaker Data Wrangler and AWS Glue for data processing
- Leverage SageMaker Experiments for model training and tuning
- Learn automated ML with SageMaker Autopilot and bias detection
- Deploy models using SageMaker Pipelines and Model Registry
- Gain hands-on experience with SageMaker Debugger for performance optimization
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 seamless ML workflows
2
Optimize model performance using SageMaker Debugger for real-time training insights and alerts
3
Automate machine learning pipelines with SageMaker Autopilot and SageMaker Pipelines
4
Gain expertise in model deployment and version control using SageMaker Model Registry
5
Detect and mitigate bias using SageMaker Clarify to ensure fair and explainable models
Overall ratings by our students
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Frequently asked questions
Amazon SageMaker Studio for Data Scientists Training is an advanced, hands-on course designed to help experienced data scientists master the tools and techniques for building, training, and deploying machine learning models using Amazon SageMaker Studio.
This comprehensive training covers data wrangling, model optimization, automated machine learning, deployment strategies, and real-time monitoring. Upon completion, students will gain in-depth knowledge of AWS machine learning tools and techniques, preparing them for high-demand roles in data science and AI.
Yes, this course dives into advanced SageMaker Studio tools like SageMaker Pipelines, Debugger, Experiments, and Model Registry. It’s ideal for those who want to move beyond the basics and master production-ready ML solutions on AWS.
The certification provided upon course completion is globally recognized, especially in regions where AWS services are widely adopted, such as the UAE, Dubai, and the Middle East.
The demand for AWS-certified professionals is rapidly growing as businesses increasingly adopt AWS cloud solutions. This certification equips you with the skills needed to work with one of the most powerful and scalable machine learning platforms globally.
After completing the training, you can pursue various career paths such as:
- Data Scientist
- Machine Learning Engineer
- AI Specialist
- ML Architect
These roles are in high demand in industries like tech, healthcare, finance, and e-commerce.
Our Amazon SageMaker Studio course covers SageMaker Debugger for identifying model issues during training and SageMaker Model Monitor for post-deployment tracking. Participants learn how to keep models accurate and stable over time which is crucial for real-world deployment.
The course offers flexible learning options, including live online sessions and self-paced learning. This flexibility allows professionals to balance their full-time job while gaining valuable skills. With hands-on labs and real-time feedback, the course ensures that learners get the most out of their study time, even with busy schedules.
Industries like finance, healthcare, e-commerce, retail, and technology are actively hiring data scientists with expertise in Amazon SageMaker Studio. These sectors are increasingly adopting cloud-based machine learning solutions to drive innovation, and professionals with these skills are in high demand.
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