In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and stunning art in just a few seconds. Although creating impressive images can find use in industries ranging from art to NFTs and beyond, today we also expect AI to be personalizable. Today, we announce that you can personalize the image generation model to your use case by fine-tuning it on your custom dataset in Amazon SageMaker JumpStart. This can be useful when creating art, logos, custom designs, NFTs, and so on, or fun stuff such as generating custom AI images of your pets or avatars of yourself. In this post, we provide an overview of how to fine-tune the Stable Diffusion model in two ways: programmatically through JumpStart APIs available in the SageMaker Python SDK, and JumpStart's user interface (UI) in Amazon SageMaker Studio. We also discuss how to make design choices including dataset quality, size of training dataset, choice of hyperparameter values, and applicability to multiple datasets.

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. For detailed documentation, including the API reference, see Read the Docs.

As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when they are not. In this post, we explore how companies can improve visibility into their models with centralized dashboards and detailed documentation of their models using two new features: SageMaker Model Cards and the SageMaker Model Dashboard. Both these features are available at no additional charge to SageMaker customers. Model governance is a framework that gives systematic visibility into model development, validation, and usage.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use. VentureBeat connected with Bratin Saha, AWS VP of Machine Learning, to discuss where Amazon SageMaker Serverless fits into the big picture of Amazon's machine learning offering and how it affects ease of use and TCO, as well as Amazon's philosophy and process in developing its machine learning portfolio. Inference is the productive phase of ML-powered applications.

Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also create a real-time endpoint for online inference. You can access Autopilot's one-click features in Amazon SageMaker Studio or by using the AWS SDK for Python (Boto3) or the SageMaker Python SDK. In this post, we show how to make batch predictions on an unlabeled dataset using an Autopilot-trained model.

These examples show you how to use SageMaker Processing jobs to run data processing workloads. These examples show you how to use SageMaker Pipelines to create, automate and manage end-to-end Machine Learning workflows. These examples show you how to train and host in pre-built deep learning framework containers using the SageMaker Python SDK. These examples show you how to build Machine Learning models with frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK. These examples show how to use Amazon SageMaker for model training, hosting, and inference through Apache Spark using SageMaker Spark.

Until recently, customers who wanted to use a deep learning (DL) framework with Amazon SageMaker Processing faced increased complexity compared to those using scikit-learn or Apache Spark. This post shows you how SageMaker Processing has simplified running machine learning (ML) preprocessing and postprocessing tasks with popular frameworks such as PyTorch, TensorFlow, Hugging Face, MXNet, and XGBoost. Training an ML model takes many steps. One of them, data preparation, is paramount to creating an accurate ML model. Likewise, you often need to run postprocessing jobs (for example, filtering or collating) and model evaluation jobs (scoring models against different test sets) as part of your ML model development lifecycle.

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can't be deployed in the same AWS account where they are used. This post walks through an implementation using the SageMaker Python SDK. It's divided into two sections: The solution described in this post is provided in the Jupyter notebook available in this GitHub repository. For a full explanation of Autopilot, you can refer to the examples available in GitHub, particularly Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK).

XGBoost is a popular Python library for gradient boosted decision trees. The implementation allows practitioners to distribute training across multiple compute instances (or workers), which is especially useful for large training sets. One tool used at Zalando for deploying production machine learning models is the managed service from Amazon called SageMaker. XGBoost is already included in SageMaker as a built-in algorithm, meaning that a prebuilt docker container is available. This container also supports distributed training, making it easy to scale training jobs across many instances.