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Apply machine learning (ML), artificial intelligence (AI), and deep learning (DL) to your business, unlocking new insights and value.
Deep Learning on AWS
1 Day | Level: Intermediate
This one-day course educates you on cloud-based deep learning solutions on the AWS platform. You'll learn how to run your models on the cloud using Amazon EC2‒based deep learning Amazon Machine Image (AMI) and Apache MXNet on AWS frameworks. You will also discover techniques to use Amazon SageMaker and deploy your deep learning models using AWS services like AWS Lambda and Amazon Elastic Container Service (Amazon ECS), all while designing intelligent systems on AWS.
This course is aimed at:
- Developers who are responsible for developing deep learning applications
- Developers who want to understand concepts behind deep learning and how to implement a deep learning solution on AWS
Learn to:
- Define machine learning and deep learning
- Identify the concepts in a deep learning ecosystem
- Leverage Amazon SageMaker and MXNet programming frameworks for deep learning workloads
- Fit AWS solutions for deep learning deployments
- And much more
The Machine Learning Pipeline on AWS
4 Days | Level: Intermediate
This course is intended to educate individual on how to use the machine learning (ML) pipeline to solve real business problems in a project-based learning environment. Individuals will be taught about each phase of the pipeline from instructor presentations and demonstrations. Students will then put into practice their 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.
This course is aimed at:
- Developers
- Solutions architects
- Data engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
Learn to:
- Select and justify the appropriate ML approach for a given business problem
- Use the ML pipeline to solve a specific business problem
- Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
- Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
Practical Data Science with Amazon SageMaker
1 Day | Level: Intermediate
This intermediate-level course will highlight ways to solve a real-world use case with machine learning (ML) and produce actionable results using Amazon SageMaker. It will guide you through the stages of a typical data science process for machine learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker.
This course is aimed at:
- Developers
- Data scientists
Learn to:
- Prepare a dataset for training
- Train and evaluate a machine learning model
- Automatically tune a machine learning model
- Prepare a machine learning model for production
- And much more
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