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

4.9/5

4504 EnrolledEnrolled Learners

4.9/5

4504 EnrolledEnrolled Learners

Overview

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:

Learn data preparation with SageMaker Data Wrangler to streamline data processing

Train models using advanced algorithms like XGBoost and fine-tune with hyperparameter optimisation

Deploy machine learning models to real-time endpoints with Amazon SageMaker for seamless predictions

Implement MLOps practices for automating, monitoring, and managing model deployments

Gain practical experience in no-code machine learning using SageMaker Canvas

Ready to get started?

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.