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
What our training includes:
Upcoming sessions
Benefits of machine learning (ML)
Types of ML approaches
Framing the business problem
Prediction quality
Processes, roles, and responsibilities for ML projects
Data analysis and preparation
Data preparation tools
Demonstration: Review Amazon SageMaker Studio and Notebooks
Hands-On Lab: Data Preparation with SageMaker Data Wrangler
Steps to train a model
Choose an algorithm
Train the model in Amazon SageMaker
Hands-On Lab: Training a Model with Amazon SageMaker
Amazon CodeWhisperer
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
Model evaluation
Model tuning and hyperparameter optimization
Hands-On Lab: Model Tuning and Hyperparameter
Optimization with Amazon SageMaker
Model deployment
Hands-On Lab: Deploy a Model to a Real-Time Endpoint
and Generate a Prediction
Responsible ML
ML team and MLOps
Automation
Monitoring
Updating models (model testing and deployment)
Different tools for different skills and business needs
No-code ML with Amazon SageMaker Canvas
Demonstration: Overview of Amazon SageMaker Canvas
Amazon SageMaker Studio Lab
Demonstration: Overview of SageMaker Studio Lab
(Optional) Hands-On Lab: Integrating a Web Application
with an Amazon SageMaker Model Endpoint
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 optimise 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
Overall ratings by our students
Our Practical Data Science with Amazon SageMaker course in Bahrain 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 optimisation, 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.
Our participants need to have:
1. AWS Technical Essentials
2. Entry-level knowledge of Python programming
3. Entry-level knowledge of statistics
After completing our Practical Data Science with Amazon SageMaker Course in Bahrain, you can pursue roles such as:
1. Data Scientist
2. Machine Learning Engineer
3. DevOps Engineer
4. AI/ML Consultant
You will master a range of AWS tools, including:
1. Amazon SageMaker Studio
2. SageMaker Data Wrangler
3. 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.
Yes, our certification program is available in locations across GCC regions. We offer comprehensive instruction and training globally, which helps students to gain this important certification.
Yes, this Practical Data Science with Amazon SageMaker course is structured to guide beginners through the full ML lifecycle. You get hands-on training using SageMaker tools to build, train, and deploy models. By the end, students have the foundational knowledge and skills needed for junior data science or ML roles in Bahrain.
Learn now, pay later
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