No-code Machine Learning and Generative AI on AWS
AWS Skill Builder
Last updated June 5, 2024
No-code Machine Learning and Generative AI on AWS is a free, five-and-a-half-hour course built around a single idea: you can build, train, and deploy machine learning models, and put foundation models to work, without writing a line of code. It uses Amazon SageMaker Canvas, a visual tool for data and business analysts. On the machine learning side, you prepare raw data into a training dataset, build a model with AutoML, launch and track the training job, read the quality metrics, and deploy the model to make predictions — across both tabular and time-series data. On the generative AI side, you use Canvas's foundation-model interface for text generation, summarization, and chat, comparing models and improving their output with retrieval-augmented generation (RAG) and fine-tuning. Guided tutorials — narrated video, step-by-step instructions, and transcripts — let you try Canvas in your own AWS account.
What you'll learn
- The ML lifecycle and what ML can solve
- Preparing data into a training set in Canvas, no code
- AutoML model build, training job tracking, quality metrics
- Deploying a model + predictions (tabular and time-series)
- Foundation models for text generation, summarization, chat
- Improving output with RAG and fine-tuning
Frequently asked questions about No-code Machine Learning and Generative AI on AWS
Who is No-code Machine Learning and Generative AI on AWS for?
Data and business analysts, researchers from non-ML fields, and junior data scientists who want to build, train, and deploy machine learning models and use foundation models without writing code.
Is No-code Machine Learning and Generative AI on AWS free?
Yes — No-code Machine Learning and Generative AI on AWS is completely free to take.
What are the prerequisites for No-code Machine Learning and Generative AI on AWS?
Experience with data analysis and cleansing, a basic grasp of statistics and regression, and AWS Technical Essentials. An AWS account is needed to follow the tutorials.
Why we suggest this course
A substantial, hands-on path to the full machine learning workflow — from raw data through a deployed model that makes predictions — and to using foundation models for text, all without writing code, which puts it within reach of analysts rather than only engineers. Two things worth knowing: it pitches at the intermediate level, assuming you bring some data-analysis experience and the basics of statistics; and it is built by AWS around Amazon SageMaker Canvas, so it is vendor-specific by design, and following the tutorials in your own AWS account can incur AWS usage charges even though the course itself is free.