The Amazon SageMaker for AI Model Training course is designed to provide a deep dive into how to use Amazon SageMaker to build, train, and deploy machine learning models at scale.
Amazon SageMaker for AI Model Training
The Amazon SageMaker for AI Model Training course is designed to provide a deep dive into how to use Amazon SageMaker to build, train, and deploy machine learning models at scale. Amazon SageMaker is a fully managed service that provides everything needed for the end-to-end machine learning lifecycle, from data preparation to model deployment. In this course, you will learn how to leverage Amazon SageMaker’s powerful features and tools, including Amazon SageMaker Studio, to build and optimize AI models with ease. Through hands-on experience, you'll explore how to configure and use SageMaker for training machine learning models, experiment with advanced features, and deploy models into production. By the end of this course, you will have the practical skills to harness Amazon SageMaker for your AI model training and deployment needs.
This course is perfect for data scientists, machine learning engineers, and AI enthusiasts who want to learn how to use Amazon SageMaker for training and deploying machine learning models. If you are a beginner or intermediate user of machine learning who is interested in leveraging Amazon SageMaker to streamline and accelerate the model training process, this course will provide you with the skills needed to work efficiently within the SageMaker ecosystem. Additionally, if you are a software developer, business analyst, or data engineer aiming to enhance your AI skills and integrate machine learning models into your applications, this course will equip you with the knowledge to do so. No prior experience with Amazon SageMaker is required, though familiarity with Python and machine learning concepts will be beneficial.
Understand the fundamentals of Amazon SageMaker and its key features for AI model training.
Set up and configure Amazon SageMaker environments, including SageMaker Studio.
Prepare datasets for machine learning model training and explore data preprocessing techniques.
Build and train machine learning models using various algorithms and frameworks available within Amazon SageMaker.
Evaluate the performance of models, debug issues, and fine-tune hyperparameters for optimal results.
Deploy models to production with Amazon SageMaker for real-time inference.
Explore advanced features such as SageMaker Autopilot, hyperparameter optimization, and SageMaker Pipelines for automated workflows.
Apply your learning through a hands-on case study to build and deploy a real-world AI model.
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Explore the fundamentals of Amazon SageMaker, including its core services and features. Learn how SageMaker integrates with other AWS tools and services to streamline the machine learning lifecycle from data preparation to model deployment.
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Understand how to configure your environment in Amazon SageMaker, including setting up SageMaker Studio, creating Jupyter notebooks, and managing your training instances. Learn how to use SageMaker’s pre-built environments to simplify the development process.
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Learn how to prepare your datasets for training in Amazon SageMaker. This includes data wrangling, preprocessing, and feature engineering. You’ll also explore how to use Amazon S3 and other AWS services for storing and accessing data.
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Discover how to build and train machine learning models using various algorithms and frameworks supported by Amazon SageMaker. Learn to use SageMaker’s built-in algorithms, as well as custom models and popular machine learning frameworks like TensorFlow, PyTorch, and MXNet.
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Learn how to evaluate the performance of your trained models using metrics and visualization tools. Explore debugging techniques to troubleshoot and fine-tune your models for improved accuracy and performance.
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Explore how to deploy machine learning models into production using Amazon SageMaker. Learn about real-time inference and batch processing, and understand how to integrate deployed models into live applications for predictive analytics.
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Dive deeper into advanced Amazon SageMaker features like SageMaker Autopilot for automated machine learning, hyper parameter optimization, and SageMaker Pipelines for creating end-to-end machine learning workflows.
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Apply what you've learned in a final case study where you'll build, train, and deploy a machine learning model from scratch. This project will help you gain hands-on experience and demonstrate your skills in using Amazon SageMaker for AI model training and deployment.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
This course primarily uses Python, which is the most common language for machine learning development and is fully supported by Amazon SageMaker.
No prior experience with Amazon SageMaker is required. This course starts with the basics and gradually covers all the essential features and tools necessary to build and deploy machine learning models using SageMaker.
Yes! The course is designed for self-paced learning, allowing you to progress through the modules at your own speed and revisit content as needed.
Amazon SageMaker AI is a comprehensive, fully managed service that enables developers and data scientists to build, train, and deploy machine learning models. It provides tools for every step of the machine learning lifecycle, including data preprocessing, model building, and deployment.
To use SageMaker for model training, you’ll first set up a SageMaker environment, prepare your datasets, choose a machine learning algorithm or framework, and configure the training parameters. After training the model, you can evaluate its performance and deploy it to production for real-time inference.
With Amazon SageMaker, you can build a variety of machine learning models, including supervised learning models (e.g., regression, classification), unsupervised learning models (e.g., clustering), and deep learning models using popular frameworks like TensorFlow, PyTorch, and MXNet.
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