AWS has designed its Machine Learning courses and certifications to provide the broadest and deepest set of its machine learning services knowledge whilst supporting cloud infrastructures, effectively putting machine learning in the hands of every developer, data scientist and expert practitioner. AWS is helping customers accelerate their machine learning journey, by creating awareness on ways to build, train and deploy models quickly while using Machine Learning technology.
Reasons to build machine learning skills with AWS
AWS Machine Learning can propel your business forward
Join our expert-led FOC workshop on Amazon Sagemaker
Build ML Apps 10x Faster with Amazon Sagemaker
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Date & Time: 15 Oct, 2pm-5pm
Mode: Teams Live Event
Speaker: Miguel Saavedra
Price: FREE
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Propel your business forward with AWS’ Machine Learning Training & Certifications
Explores best practices when using the machine learning (ML) pipeline to solve real 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. Students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Learn about cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform during this 3-day course. Discover how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. The course also teaches you how to create Big Data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena, and Amazon Kinesis, and leverage best practices to design Big Data environments for security and cost-effectiveness.
In this intermediate-level, 1-day course, you will discover ways to solve a real-world use case with machine learning (ML) and produce actionable results using Amazon SageMaker. This course walks 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.
In this one-day course, you will learn cloud-based deep learning solutions on the AWS platform. You will 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 will 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.
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