Practical Data Science with Amazon SageMaker Course
Master ML with Amazon SageMaker
Get real-world insights from certified instructors
Apply machine learning to tackle real business challenges
Gain in-demand ML skills and boost your job prospects
Learn at your own pace with flexible, instructor-led sessions
Master the full ML workflow and its practical applications
Convenient and hassle-free payment plans
4.8/55469 EnrolledEnrolled Learners
4.8/55469 EnrolledEnrolled Learners
Overview
What our training includes:
- Learn data preparation techniques using SageMaker Data Wrangler
- Train models with powerful algorithms like XGBoost on SageMaker
- Evaluate and optimize models with hyperparameter tuning using SageMaker
- Deploy models to real-time endpoints with Amazon SageMaker
- Master MLOps practices for automating and monitoring model deployment
- Hands-on experience with no-code ML using Amazon SageMaker Canvas
Upcoming sessions
Curriculum
1
Benefits of machine learning (ML)
2
Types of ML approaches
3
Framing the business problem
4
Prediction quality
5
Processes, roles, and responsibilities for ML projects
1
Data analysis and preparation
2
Data preparation tools
3
Demonstration: Review Amazon SageMaker Studio and Notebooks
4
Hands-On Lab: Data Preparation with SageMaker Data Wrangler
1
Steps to train a model
2
Choose an algorithm
3
Train the model in Amazon SageMaker
4
Hands-On Lab: Training a Model with Amazon SageMaker
5
Amazon CodeWhisperer
6
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
1
Model evaluation
2
Model tuning and hyperparameter optimization
3
Hands-On Lab: Model Tuning and Hyperparameter
4
Optimization with Amazon SageMaker
1
Model deployment
2
Hands-On Lab: Deploy a Model to a Real-Time Endpoint
3
and Generate a Prediction
1
Responsible ML
2
ML team and MLOps
3
Automation
4
Monitoring
5
Updating models (model testing and deployment)
1
Different tools for different skills and business needs
2
No-code ML with Amazon SageMaker Canvas
3
Demonstration: Overview of Amazon SageMaker Canvas
4
Amazon SageMaker Studio Lab
5
Demonstration: Overview of SageMaker Studio Lab
6
(Optional) Hands-On Lab: Integrating a Web Application
7
with an Amazon SageMaker Model Endpoint
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 preparation using SageMaker Data Wrangler for efficient data processing
2
Train models with advanced algorithms like XGBoost and optimize with hyperparameter tuning
3
Deploy machine learning models to real-time endpoints using Amazon SageMaker for predictions
4
Apply MLOps practices for automating, monitoring, and managing deployed models
5
Gain hands-on experience with no-code machine learning through SageMaker Canvas
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Overall ratings by our students
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Frequently asked questions
The Practical Data Science with Amazon SageMaker course is a hands-on training program designed to teach you how to build, train, and deploy machine learning models using AWS SageMaker. This course covers data preparation, model optimization, real-time deployment, and MLOps practices, providing you with the essential tools and skills needed to apply machine learning to real-world business challenges. By the end of the course, you'll be proficient in SageMaker tools like Data Wrangler, XGBoost, and SageMaker Canvas.
We recommend that participants of this course have:
• AWS Technical Essentials
• Entry-level knowledge of Python programming
• Entry-level knowledge of statistics
The Practical Data Science with Amazon SageMaker certification is highly recognized in industries that use AWS tools for machine learning. It’s particularly valued in regions like North America, Europe, the Middle East, and Asia, where AWS adoption is high. This certification validates your expertise in machine learning and AWS services, enhancing your professional credibility.
After completing this course, you can pursue roles such as:
- Data Scientist: Build and deploy machine learning models to solve business problems
- Machine Learning Engineer: Design, develop, and implement ML models at scale
- DevOps Engineer: Deploy and manage machine learning models in production environments
- AI/ML Consultant: Provide expert guidance on ML model development and deployment
This course offers flexible learning options, including online self-paced modules, live instructor-led sessions, and recorded classes for review. You can study at your own pace and schedule, making it ideal for working professionals who need to balance their training with their job responsibilities.
You will master a range of AWS tools, including Amazon SageMaker Studio, SageMaker Data Wrangler, SageMaker Canvas, and XGBoost. Additionally, you will learn MLOps techniques for automating model deployment and monitoring, enabling you to manage machine learning models effectively in production.
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- Understand about our methodology
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