Course Content
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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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 – Generate and label unstructured data
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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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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
AWS – SageMaker For Data Scientists
About Lesson

With the SageMaker Data Wrangler UI, you can launch scale to large datasets without the need to author PySpark code, install Apache Spark, or spin up clusters.

You can launch or schedule a job to quickly process your data or export it to a SageMaker Studio notebook. SageMaker Data Wrangler offers several export options, including Amazon SageMaker Data Wrangler jobs, Amazon SageMaker Feature Store, Amazon SageMaker Autopilot, and Amazon SageMaker Pipelines, providing you the ability to integrate your data preparation flow into your ML workflow. Alternatively, you can deploy your data preparation workflow to a SageMaker hosted endpoint.

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