You can use Amazon Augmented AI to incorporate a human review into your workflow for built-in task types , Amazon Textract and Amazon Rekognition, or your own custom tasks using a custom task type .

1 week ago - Unify data access across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party and federated data sources with a lakehouse architecture in Amazon SageMaker ... Bringing together widely adopted AWS machine learning (ML) and analytics capabilities, the next generation of Amazon SageMaker delivers an integrated experience for analytics and AI with unified access to all your data.

April 8, 2023 - Integration with Other AWS Services: To offer a full machine learning solution, SageMaker may be linked with other AWS services like Amazon S3, AWS Lambda, and AWS Glue. ... Reduced Time to Market: With SageMaker, data scientists and developers can easily and quickly create, train, and deploy machine learning models, which cuts down on the time it takes for new goods and services to hit the market. SageMaker’s ability to scale up or down to handle shifting workloads enables firms to only pay for the resources they actually use.

April 10, 2023 - Amazon SageMaker is optimized for various popular deep learning frameworks such as TensorFlow, Apache MXNet, PyTorch, and more. Frameworks are always up-to-date with the latest version and are optimized for performance on AWS. Users don’t need to manually set up these frameworks and further can use them within the built-in containers.

It caters to different types of ... platform can be used for several use cases like access control, number plate recognition, recommendation system, business analytics with NLP, etc....

From video and music streaming to internet advertising and sports, Amazon SageMaker helps customers make smarter content investments, better monetize content libraries, and delight users with personalized experiences.

December 16, 2023 - Integration with AWS Ecosystem: SageMaker integrates with other AWS services, such as S3 for data storage, IAM for access control, and CloudWatch for monitoring, providing a comprehensive ecosystem for machine learning workflows. ... Cost: While SageMaker provides cost-effective solutions, running large-scale training jobs or deploying models for high-traffic inference can increase costs. Users should carefully manage resources to optimize costs.

Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. - GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

January 30, 2025 - Learn what is AWS SageMaker✔️, advantages of sagemaker, and how to use machine learning with AWS sagemaker. Read to learn more about sagemaker

November 17, 2025 - After deploying machine learning models, SageMaker allows for both real-time and batch inference. Users can create endpoints—specific URLs that serve as access points for applications—to make real-time predictions and manage workloads efficiently. This is particularly useful for applications requiring instant responses, such as in generative AI scenarios. With features like auto scaling and integration with AWS Lambda, SageMaker provides serverless capabilities that help manage computing resources dynamically based on demand.

One of my major use cases is related to BlazingText, where I build an assistant similar to Siri or Alexa using Amazon SageMaker ( /products/amazon-sagemaker-reviews ). This solution helps me in various stages of development, such as using pre-trained models or training my own. It is a managed service, so everything is managed by AWS ( /products/amazon-aws-reviews ), reducing the hassle of handling tasks.

@feature_processor( inputs=[FeatureGroupDataSource('transactions')], output='arn:aws:sagemaker:us-east-1:111122223333:feature-group/your-feature-group-name' ) def tumbling_window_aggregates(transactions_df, spark): '''Aggregates over 1-week windows, across 1-day tumbling windows, as a SQL query.''' transactions_df.createOrReplaceTempView('transactions') return spark.sql(f''' SELECT credit_card_num, window.start, AVG(amount) AS avg, COUNT(*) AS count FROM transactions GROUP BY credit_card_num, window(txn_time, "1 week") ORDER BY window.start ''')

July 22, 2020 - Unlike traditional ML development, which requires managing complex infrastructure for training and hosting, SageMaker abstracts away the heavy lifting. It provides a unified toolset for every step of the ML lifecycle, from labeling raw data to monitoring production models for drift. SageMaker is built around three distinct stages of the ML lifecycle. Importantly, you can use these independently.

1 week ago - Using partitioning algorithms, SageMaker's distributed training libraries split large deep learning models and training datasets across AWS GPU instances in a fraction of the time it takes to do manually. SageMaker achieves these efficiencies through two techniques: data parallelism and model parallelism. Model parallelism splits models too large to fit on a single GPU into smaller parts before distributing across multiple GPUs to train, and data parallelism splits large datasets to train concurrently in order to improve training speed. ML use cases such as image classification and text-to-speech demand increasingly larger computational requirements and datasets.

Step 2.1: Specify model arn from AWS Marketplace subscription · Step 2.2: Create model from model package and deploy to endpoint · Step 3: Explore use cases and model parameters · Step 3.1: Use case 1: Assisted writing of prose · Step 3.2: Use case 2: Autonomous authoring of poem · Step ...

Text Classification - TensorFlow—a supervised algorithm that supports transfer learning with available pretrained models for text classification. SageMaker AI also provides image processing algorithms that are used for image classification, object detection, and computer vision.

2 weeks ago - See how leading organizations worldwide are using Amazon SageMaker AI to build, customize, and deploy AI models at scale

March 11, 2025 - Discover AWS SageMaker guide covering what Amazon SageMaker is, key features, detailed pricing models, costs, and instance types. Start building ML models faster.

Welcome to Amazon SageMaker. This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.