The Machine Learning Pipeline 

This 4-day course explores best practices when using the machine learning (ML) pipeline to solve significant business problems in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. 

Gain an understanding in:  

• Selecting and justifying the appropriate ML approach for a given business problem 
• Using the ML pipeline to solve a specific business problem 
• Training, evaluating, deploying, and tuning an ML model in Amazon SageMaker 
• Describing some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS 

Level: Intermediate | Duration: 4 Days | Mode: Live Virtual

Date: 13 Dec 2021 

Fee: RM 7, 200

Who is this course for? 

• Developers 
• Solutions architects 
• Data engineers 
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker 

What experience you require: 

• Basic knowledge of Python programming language  
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)  
• Basic experience working in a Jupyter notebook environment 

Associated Certification:  

AWS Certified Machine Learning – Specialty 

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems. 

You will be validate in your ability to:  

• Select and justify the appropriate ML approach for a given business problem 
• Identify appropriate AWS services to implement ML solutions 
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions