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AWS Machine Learning and Artificial Intelligence Training

Our AWS Machine Learning (ML) and Artificial (AI) training courses teach you how to use AWS's Data Science and ML capabilities. Students learn to create and maintain analytics solutions through data collection, processing, and visualization and prepare for the AWS ML Certification exam. As an advanced AWS training partner, Web Age Solutions has helped thousands of students earn AWS certification.

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The Machine Learning Pipeline on AWS
Course ID: AWS-ML-PL
Delivery: On-Site or Instructor-led Virtual
This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and 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. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
Developing Generative AI Applications on AWS
Course ID: AWS-GENAI-APP
Delivery: On-Site or Instructor-led Virtual

This course introduces generative artificial intelligence (Gen AI) to software developers interested in using large language models (LLMs) without fine-tuning. After an overview of AI, attendees learn how to plan an AI project, work with Amazon Bedrock, and apply prompt engineering best practices.  In addition, students understand the architecture patterns used to build AI applications with Amazon Bedrock and LangChain.
Developing Generative AI Applications on AWS with Hands-On Labs
Course ID: AWS-GENAI-LABS
Delivery: On-Site or Instructor-led Virtual

This GenAI course introduces generative artificial intelligence (Gen AI) to software developers interested in using large language models (LLMs) without fine-tuning. After an overview of AI, attendees learn how to plan an AI project, work with Amazon Bedrock, and apply prompt engineering best practices.  In addition, students understand the architecture patterns used to build AI applications with Amazon Bedrock and LangChain.
Deep Learning on AWS
Course ID: AWS-LEARN
Delivery: On-Site or Instructor-led Virtual
In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.
MLOps Engineering on AWS
Course ID: AWS-MLO-ENG
Delivery: On-Site or Instructor-led Virtual
This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
Amazon SageMaker Studio for Data Scientists
Course ID: AWS-SMS-DS
Delivery: On-Site or Instructor-led Virtual
Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle.