Amazon SageMaker Studio for Data Scientists Training in Dubai

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.

Our Trainers

Learning Outcomes

Upon finishing the training, you will:

1

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

2

Enhance model performance with real-time insights and alerts using SageMaker Debugger

3

Automate and streamline ML workflows using SageMaker Autopilot and SageMaker Pipelines

4

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

5

Detect and address bias in models using SageMaker Clarify to ensure fairness and transparency

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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.

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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 in Dubai is a specialized course designed for data science professionals to master machine learning (ML) workflows using Amazon SageMaker Studio. The training covers data processing, model development, deployment, and monitoring with AWS tools like SageMaker Data Wrangler, SageMaker Experiments, and SageMaker Pipelines.

This course prepares data scientists to create scalable ML models and deploy them efficiently, ensuring mastery over end-to-end ML solutions in real-world applications.

This training opens up multiple career opportunities in and outside UAE. You can pursue roles such as Data Scientist, ML Engineer, or AI Specialist. Graduates gain valuable skills that are highly sought after by employers in Dubai and globally. Companies are increasingly adopting AWS for machine learning, and this certification enhances your profile, positioning you for senior roles in ML and AI-driven positions.

This Amazon SageMaker Studio course offers flexible learning options. It includes live sessions, on-demand courses, and interactive hands-on labs. This flexibility allows professionals in Dubai to pursue the course while managing their full-time work commitments. Online learning options further enable learners to access course materials and complete assignments at their convenience.

The Amazon SageMaker Studio for Data Scientists Training in Dubai is designed for working professionals. It offers flexible scheduling with online modules and live sessions. This allows learners to complete their training at their own pace without disrupting their work schedules. The hands-on labs ensure that learning is practical and aligned with real-world applications.

As a senior professional, this course offers you a structured way to scale ML operations, lead data science teams, and adopt MLOps best practices. Tools like SageMaker Model Registry and Pipelines allow for better collaboration, version control, and reproducibility across teams. It’s ideal if you're moving into leadership roles like ML Architect or AI Manager.

Our Amazon SageMaker Studio for Data Scientists Training stands out because it focuses specifically on Amazon SageMaker, one of the most powerful ML platforms available. Unlike other courses, this training provides deep insights into AWS-specific tools and services. It ensures that learners gain specialized knowledge directly applicable to real-world projects using AWS.

This course is offered online and across multiple locations, including:

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