Category: Amazon SageMaker

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select the target column to predict, […]

Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with high availability while maximizing resource utilization […]

In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning […]

This post is co-written with Chaim Rand from Mobileye. Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. When both types of tasks run on the same […]

Today Amazon SageMaker announced the support of Grid search for automatic model tuning, providing users with an additional strategy to find the best hyperparameter configuration for your model. Amazon SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using a range of hyperparameters that you […]

Amazon SageMaker Serverless Inference is a purpose-built inference option that makes it easy for you to deploy and scale machine learning (ML) models. It provides a pay-per-use model, which is ideal for services where endpoint invocations are infrequent and unpredictable. Unlike a real-time hosting endpoint, which is backed by a long-running instance, compute resources for […]

In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, […]

As AI adoption is accelerating across the industry, customers are building sophisticated models that take advantage of new scientific breakthroughs in deep learning. These next-generation models allow you to achieve state-of-the-art, human-like performance in the fields of natural language processing (NLP), computer vision, speech recognition, medical research, cybersecurity, protein structure prediction, and many others. For […]

In this post, we introduce a new analysis in the Data Quality and Insights Report of Amazon SageMaker Data Wrangler. This analysis assists you in validating textual features for correctness and uncovering invalid rows for repair or omission. Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from […]

Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your own containers with custom AI/ML algorithms. […]