|
|
Get started on machine learning training with content built by AWS experts
AWS-ML: The Machine Learning Pipeline on AWS
3 Days
The Machine Learning Pipeline on AWS 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.
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 using Amazon SageMaker
- Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
- Apply machine learning to a real-life business problem after the course is complete
AWS-DEEPL: Deep Learning on AWS
1 Day
In this one-day Deep Learning on AWS course, you’ll learn 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.
In addition, you’ll learn how 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.
Learn to:
- Define machine learning (ML) 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
AWS-PDSASM: Practical Data Science with Amazon SageMaker
1 Day
In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.
Learn to:
- Apply Amazon SageMaker to a specific use case and dataset
- Practice all the steps of the typical data science process
- Visualize and understand the dataset
- Explore how the attributes of the dataset relate to each other
- Prepare the dataset for training and More..
AWS-MLOPS: MLOps Engineering on AWS
3 Days
This MLOps Engineering on AWS 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.
Learn to:
- Describe machine learning operations
- Understand the key differences between DevOps and MLOps
- Describe the machine learning workflow
- Discuss the importance of communications in MLOps
- Explain end-to-end options for automation of ML workflows and More..
|
|
© 2021 TRAINOCATE MALAYSIA SDN BHD. ALL RIGHTS RESERVED
CONNECT WITH US