AWS Machine Learning Blog

Category: Artificial Intelligence

Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

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, […]

Startups across AWS Accelerators use AI and ML to solve mission-critical customer challenges

Relentless advancement in technology is improving the decision-making capacity of humans and enterprises alike. Digitization of the physical world has accelerated the three dimensions of data: velocity, variety, and volume. This has made information more widely available than before, allowing for advancements in problem-solving. Now, with cloud-enabled democratized availability, technologies like artificial intelligence (AI) and […]

Model hosting patterns in Amazon SageMaker, Part 5: Cost efficient ML inference with multi-framework models on Amazon SageMaker 

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 […]

Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

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 […]

Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

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 […]

Reduce food waste to improve sustainability and financial results in retail with Amazon Forecast

With environmental, social, and governance (ESG) initiatives becoming more important for companies, our customer, one of Greater China region’s top convenience store chains, has been seeking a solution to reduce food waste (currently over $3.5 million USD per year). Doing so will allow them to not only realize substantial operating savings, but also support corporate […]

Amazon SageMaker Automatic Model Tuning now supports grid search

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 […]

Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit

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 […]

AWS Celebrates 5 Years of Innovation with Amazon SageMaker

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, […]

Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS

This post was co-written with Sachin Kadyan, a leading developer of OpenFold. In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive, and time consuming, and can take […]