Amazon SageMaker Studio for Data Scientists Training in Bahrain
Build, train, and deploy ML models using SageMaker Studio
Attain practical experience in data processing & model training
Learn from experienced industry trainers
Master skills from data wrangling to model tuning
Acquire job-ready skills for advanced data science roles
Flexible, focused, & career-oriented training
Easy and convenient payment options available
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Overview
What our training includes
- Provides skills to build full ML workflows with SageMaker Studio
- Prepares to process data using Data Wrangler & AWS Glue
- Trains to improve models with SageMaker Experiments
- Explains how to automate ML & detect bias with Autopilot
- Delivers model deployment & version control using Pipelines
- Teaches performance tuning with SageMaker Debugger hands-on
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 Objectives
Upon finishing the training, you will:
1
Master data preparation with SageMaker Data Wrangler & AWS Glue
2
Enhance training & performance tracking with SageMaker Debugger
3
Automate the ML pipeline using SageMaker Autopilot & SageMaker Pipelines
4
Learn to deploy models & manage versions through SageMaker Model Registry
5
Ensure fairness in models by identifying & reducing bias using SageMaker Clarify
Overall ratings by our students
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Frequently asked questions
The Amazon Sagemaker Studio for Data Scientists Training in Bahrain is all about helping professionals master Amazon SageMaker Studio for Machine Learning. You will learn to build, train, monitor, and deploy machine learning models using Amazon SageMaker Studio tools. This training will also provide practical lab work and actual challenges for better learning.
This Amazon Sagemaker Studio for Data Scientists course will be beneficial to professionals who want to deepen their machine learning skills using AWS tools. This includes -
- Data scientists
- Machine learning engineers
- AI specialists
- Developers working with AWS services
- Tech professionals transitioning into data science roles
- Anyone interested in automating end-to-end ML workflows
This training is available through various flexible learning modes-
- Offline group classes
- Live online classes
- Individual one-on-one sessions
The entire course curriculum is divided into separate modules, including the following topics -
- Amazon SageMaker Studio Setup
- Data Processing
- Model Development
- Deployment and Inference
- Monitoring
- Managing SageMaker Studio Resources and Updates
- Capstone
Some familiarity with Python or programming basics is helpful, but not mandatory. Our course includes hands-on labs with guided instructions. Tools like SageMaker Autopilot automate many steps, allowing you to focus on the ML logic without writing full code from scratch.
In the Amazon SageMaker Studio for Data Scientists Certification in Bahrain, professionals will learn to work with -
- Amazon SageMaker Studio
- SageMaker Data Wrangler, Experiments, Pipelines, Autopilot
- AWS Glue and Amazon EMR
- SageMaker Model Monitor, Debugger, and Clarify
- SageMaker Feature Store and Model Registry
Our graduates begin applying for jobs immediately after completing the final project or capstone, as it showcases their end-to-end ML knowledge. Roles like Junior ML Engineer, AI Analyst, or Model Deployment Specialist are achievable within weeks of course completion, especially if paired with a strong resume.
Do you want to learn more about Learners Point Academy?
- Learn more about courses
- Understand about our methodology
- Let’s talk about Corporate trainings
- Anything else that you want to know, we are here for you!
