Practical Data Science with Amazon SageMaker Course in Oman
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
4.9/54.9/5Overview
What you will learn:
- Learn data preparation techniques using SageMaker Data Wrangler
- Train models with powerful algorithms like XGBoost on SageMaker
- Evaluate and optimise 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
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
Meet your Trainer
Our Trainers
We, at Learners Point, take immense pride in our teaching methods and instructors. Our instructors are some of the best experts in their fields and employ a practical approach to learning. Many of them are globally recognised and have a diverse set of experience in their field of expertise. You are always sure to have the best in the industry as your teachers who are ready to guide you at every step and make the experience informative yet enjoyable. Apart from the focus on learning your chosen course, our instructors also encourage students to develop communication skills and interpersonal skills necessary to excel in the practical world.
Our cutting edge teaching methods make every program an immersive and productive experience for the learners. Our learning methods are research-driven and are continuously updated to stay relevant to present times as well as the future. You will enjoy practical applications of everything learned through theory and regular mock examinations to help monitor your progress. Our courses are led by an instructor in a classroom setup and we do offer online high-quality sessions as well for individuals. We also monitor the training sessions with a progress tracker to maintain high standards of instruction & ethics.
Learning Outcomes
After you complete this training, you will be able to:
1
Learn data preparation with SageMaker Data Wrangler to streamline data processing
2
Train models using advanced algorithms like XGBoost and fine-tune with hyperparameter optimisation
3
Deploy machine learning models to real-time endpoints with Amazon SageMaker for seamless predictions
4
Implement MLOps practices for automating, monitoring, and managing model deployments
5
Gain practical experience in no-code machine learning using 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.
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Frequently asked questions
The Practical Data Science with Amazon SageMaker course in Oman trains you to build, train, and deploy machine learning models using AWS SageMaker. Students learn data preparation, model optimisation, real-time deployment and MLOps practices. We help you gain expertise in SageMaker tools like Data Wrangler, XGBoost, and SageMaker Canvas.
Although there are no eligibility requirements for enrolling in this Practical Data Science with Amazon SageMaker Course, we recommend that candidates know the following:
- AWS Technical Essentials
- Entry-level knowledge of Python programming
- Entry-level knowledge of statistics
After completing this course, you can pursue in-demand roles in Oman such as:
- Data Scientist/Analyst
- Machine Learning Engineer
- DevOps Engineer
- AI/ML Consultant
- Cloud Engineer
During this course, students master a range of AWS tools. These include Amazon SageMaker Studio, SageMaker Canvas, SageMaker Data Wrangler and XGBoost. They also get hands-on with MLOps techniques for automating model deployment and monitoring. Mastering these tools helps you to manage machine learning models effectively in production.
Yes, our course, Amazon SageMaker course for Practical Data Science, is suitable for beginners. We cover all the concepts from the basic level and progress towards the advanced level. However, we strongly recommend having basic knowledge of Python programming, statistics and basics of AWS.
Our Amazon SageMaker course gives you a competitive edge by exposing you to cloud-native ML workflows. Participants learn how to automate training pipelines, monitor models post-deployment, and apply MLOps. These are essential for scalable ML solutions in production.
This training stands out for its hands-on, practical approach using real AWS tools like SageMaker Studio, Data Wrangler, and Jupyter Notebooks. Instead of focusing on theory, students work on real-world tasks like model training, deployment, and MLOps. Our certified trainers keep you engaged with interactive modules and business-focused machine learning applications.
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