Amazon SageMaker Studio for Data Scientists Training in Saudi Arabia

Build, train, and deploy models with Amazon SageMaker Studio

Gain real-world experience in data processing, training, and deployment

Receive guidance from top industry professionals

Master everything from data wrangling to model optimization

Gain in-demand skills for high-level data science roles

Flexible and intensive Training

Convenient and hassle-free payment plans

4.8/5

4300 EnrolledEnrolled Learners

4.8/5

4300 EnrolledEnrolled Learners

Overview

What you will master with us

Master Amazon SageMaker Studio for end-to-end ML model development
Use SageMaker Data Wrangler and AWS Glue for data processing
Leverage SageMaker Experiments for model training and tuning
Learn automated ML with SageMaker Autopilot and bias detection
Deploy models using SageMaker Pipelines and Model Registry
Gain hands-on experience with SageMaker Debugger for performance optimisation

Upcoming sessions

Curriculum

JupyterLab Extensions in SageMaker Studio

Demonstration: SageMaker user interface demo

Using SageMaker Data Wrangler for data processing

Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler

Using Amazon EMR

Hands-On Lab: Analyze and prepare data at scale using Amazon EMR

Using AWS Glue interactive sessions

Using SageMaker Processing with custom scripts

Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker

Python SDK

SageMaker Feature Store

Hands-On Lab: Feature engineering using SageMaker Feature Store

SageMaker training jobs

Built-in algorithms

Bring your own script

Bring your own container

SageMaker Experiments

Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning

SageMaker Debugger

Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger

Automatic model tuning

SageMaker Autopilot: Automated ML

Demonstration: SageMaker Autopilot

Bias detection

Hands-On Lab: Using SageMaker Clarify for Bias and Explainability

SageMaker Jumpstart

SageMaker Model Registry

SageMaker Pipelines

Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker

Studio

SageMaker model inference options

Scaling

Testing strategies, performance, and optimization

Hands-On Lab: Inferencing with SageMaker Studio

Amazon SageMaker Model Monitor

Discussion: Case study

Demonstration: Model Monitoring

Meet your Trainer

Our Trainer

We take immense pride in our skilled instructors and trainers who teach the Finance for Non-Finance course at Learns Point. Our trainers are globally recognized for their expertise and experience in various aspects of financial fields. Many of our instructors have worked in companies around the globe and have a lot of practical experience to share with the students. We at Learners Point adopt a data-driven research approach to learning and teaching so the experience is highly customizable and thoroughly engaging for learners from all walks of life. The sessions are classroom-based and led by an instructor. For those who seek more flexibility, we also offer high quality live and interactive sessions online.

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:

Master data processing with SageMaker Data Wrangler and AWS Glue for seamless ML workflows

Optimise model performance using SageMaker Debugger for real-time training insights and alerts

Automate machine learning pipelines with SageMaker Autopilot and SageMaker Pipelines

Gain expertise in model deployment and version control using SageMaker Model Registry

Detect and mitigate bias using SageMaker Clarify to ensure fair and explainable models

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 Amazon SageMaker Studio for Data Scientists training provides you with the knowledge to process data, build models, and deploy machine learning solutions by leveraging SageMaker. You’ll learn essential skills such as data wrangling, model optimisation, and automated machine learning. Therefore, it crucial for any data science professional.

Participants learn to transition from local development to scalable cloud-based ML pipelines using Amazon SageMaker Studio. This includes automating workflows, model monitoring, and versioning, which are crucial in production environments. These skills are in high demand across Saudi’s growing AI sector.

Here are some of the career paths you can pursue after completing our course in KSA:

1. Data scientist
2. Machine learning engineer
3. AI specialist
4. Cloud architect
5. Data engineer

This course equips you with the most relevant and in-demand skills in data science, using tools that are widely adopted in the industry. By mastering Amazon SageMaker Studio, you’ll gain expertise in key areas like data processing, model development, and model deployment, giving you a competitive edge in Saudi Arabia’s rapidly evolving tech sector.

AWS services are central to this training. You’ll work extensively with Amazon SageMaker, Data Wrangler, SageMaker Experiments, and other AWS tools, gaining hands-on experience in a real-world cloud environment. These services allow you to process and deploy machine learning models, enabling efficient and scalable workflows.

Yes, this Amazon SageMaker Studio course teaches end-to-end automation of ML workflows. We help you scale and operationalize AI solutions which are the key priorities for Vision 2030 projects. With tools like Model Monitor and Feature Store, learners are able to ensure model accountability and performance in enterprise settings.