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Intro on AI/ML & AWS sign up
Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It is designed to help data scientists and developers build, train, and deploy machine learning models at scale. With SageMaker, you can easily create, train, and deploy machine learning models without the need to manage the underlying infrastructure.
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Key AI/ML Components in AWS
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Popular Amazon models
Amazon Web Services (AWS) provides a rich set of services and tools for implementing artificial intelligence (AI) and machine learning (ML) solutions.
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IAM & access mgmt
Amazon Identity and Access Management (IAM) is a service provided by Amazon Web Services (AWS) that enables you to manage user access and permissions to AWS resources. IAM allows you to create and manage user accounts, assign fine-grained permissions, and control access to AWS services and resources.
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Amazon S3
Amazon Simple Storage Service (S3) is a highly scalable and durable object storage service provided by Amazon Web Services (AWS). It is designed to store and retrieve any amount of data from anywhere on the web. S3 offers a simple interface to store and retrieve data, making it a popular choice for storing a wide range of data types, including documents, images, videos, backups, logs, and more.
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Introductions to Amazon Sagemaker
Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It is designed to simplify the process of building, training, and deploying machine learning models at scale. SageMaker provides a comprehensive set of tools and services that enable data scientists and developers to focus on the core tasks of developing and refining models, without the need to manage the underlying infrastructure.
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Prepare data – (Data Wrangler)
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Prepare data – Generate and label unstructured data
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Prepare data – Store, share, and manage model features
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Build Models – SageMaker Studio notebooks
Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case.
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Build models – SageMaker notebook instance
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Train and tune the model – Fully managed infrastructure at scale
Train and tune the model – High-performance distributed training
Train and tune the model – Built-in tools for the highest accuracy and lowest cost
Train and tune the model – Built-in tools for interactivity and monitoring