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
4.8/54300 EnrolledEnrolled Learners
4.8/54300 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
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
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:
1
Master data processing with SageMaker Data Wrangler and AWS Glue for seamless ML workflows
2
Optimise model performance using SageMaker Debugger for real-time training insights and alerts
3
Automate machine learning pipelines with SageMaker Autopilot and SageMaker Pipelines
4
Gain expertise in model deployment and version control using SageMaker Model Registry
5
Detect and mitigate bias using SageMaker Clarify to ensure fair and explainable models
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.
Overall ratings by our students
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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.
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