Press release
AWS Announces Nine New Amazon SageMaker Capabilities
Deep profiling for
Distributed Training on
Machine learning is becoming more mainstream, but it is still evolving at a rapid clip. With all the attention machine learning has received, it seems like it should be simple to create machine learning models, but it isn’t. In order to create a model, developers need to start with the highly manual process of preparing the data. Then they need to visualize it in notebooks, pick the right algorithm, set up the framework, train the model, tune millions of possible parameters, deploy the model, and monitor its performance. This process needs to be continuously repeated to ensure that the model is performing as expected over time. In the past, this process put machine learning out of the reach of all but the most skilled developers. However,
Today’s announcements build on the more than 50 new
Amazon SageMaker Data Wrangler automated data preparation:Amazon SageMaker Data Wrangler provides the fastest and easiest way to prepare data for machine learning. Data preparation for machine learning is a difficult process. This difficulty arises from the fact that data attributes (known as features) used to train a machine learning model often come from different sources and exist in various formats. This means that developers must spend considerable time extracting and normalizing this data so it’s consistently easy to use with machine learning. Customers might also want to combine features into composite features to give the machine learning model more helpful inputs. For example, a customer might want to create a feature that describes a group of customers that are prolific spenders so they can be offered loyalty program rewards by combining features for items previously purchased, amount spent, and frequency of purchases. The work associated with transforming data into features is called feature engineering, and it consumes a lot of time for developers when they’re building machine learning models.Amazon SageMaker Data Wrangler radically simplifies the process of data preparation and feature engineering. WithAmazon SageMaker Data Wrangler, customers can choose the data they want from their various data stores and import it with a single click.Amazon SageMaker Data Wrangler contains over 300 built-in data transformers that can help customers normalize, transform, and combine features without having to write any code, while managing all of the processing infrastructure under the hood. Customers can quickly preview and inspect that these transformations are what was intended by viewing them inSageMaker Studio (the first end-to-end Integrated Development Environment for machine learning). Once the features have been engineered,Amazon SageMaker Data Wrangler will save them for reuse in theAmazon SageMaker Feature Store .Amazon SageMaker Feature Store feature storage and management:Amazon SageMaker Feature Store provides a new repository that makes it easy to store, update, retrieve, and share machine learning features for training and inference. Today, customers can save their features toAmazon Simple Storage Service (S3). This works well for a simple set of features that are mapped to a single model, but most features are not mapped to only one model. Most features are used repeatedly by multiple models and multiple developers and data scientists, and as new features are created, developers also want to be able to reuse them repeatedly. This leads to multiple S3 objects to manage, which can quickly become difficult to manage. Developers and data scientists try to solve this by using spreadsheets, paper notes, and emails. Sometimes they even try to build a custom application to keep track of the features, but this is a lot of work and error-prone. Further, developers and data scientists need the same features not only to train multiple models with all of the data available and where this activity can happen over hours, but also to use during inference when the predictions need to be returned in milliseconds and often use just a subset of the data in relevant features. For example, a developer might want to create a model that predicts the next best song in a playlist. To do this, developers would train the model on thousands of songs and then provide the model the last three songs played during inference to predict the next song. Training and inference are very different uses cases. During training, the models can access the features offline and in batch, but for inference, the model needs only a subset of the features in near real-time. Since machine learning models have a single source of features that need to be consistent, these different access patterns make it challenging to keep the features consistent and up to date.Amazon SageMaker Feature Store solves this problem by providing a purpose-built feature store where developers can access and share features that make it much easier to name, organize, find, and share sets of features among teams of developers and data scientists. SinceAmazon SageMaker Feature Store resides inAmazon SageMaker Studio—close to where machine learning models are run—it provides single-digit millisecond latency for inference.Amazon SageMaker Feature Store makes it simple and easy to organize and update large batches of features for training and smaller instantiations of them for inference. That way, there’s one consistent view of features for machine learning models to use and it becomes significantly easier to generate models that produce highly accurate predictions.Amazon SageMaker Pipelines workflow management and automation:Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning. As customers can see with feature engineering, machine learning comprises multiple steps that can benefit from orchestration and automation. This is not dissimilar to traditional programming, where customers have tools like CI/CD to help them develop and deploy applications more quickly. However, with machine learning, CI/CD tools are rarely used because they don’t exist or because they are hard to set up, configure, and manage. WithAmazon SageMaker Pipelines, developers can define each step of an end-to-end machine learning workflow. These workflows include the data-load steps, transformations fromAmazon SageMaker Data Wrangler, features stored inAmazon SageMaker Feature Store , training configuration and algorithm set up, debugging steps, and optimization steps. WithAmazon SageMaker Pipelines, developers can easily re-run an end-to-end workflow fromAmazon SageMaker Studio , using the same settings to get the exact same model every time, or they can re-run the workflow on a regular schedule with new data to update a model.Amazon SageMaker Pipelines logs each step inAmazon SageMaker Experiments (anAmazon SageMaker capability that organizes and tracks machine learning experiments and model versions) every time a workflow is run. This helps developers visualize and compare machine learning model iterations, training parameters, and outcomes. WithAmazon SageMaker Pipelines, workflows can be shared and re-used between teams, either to recreate a model or to act as a starting point for making improvements through new features, algorithms, or optimizations.Amazon SageMaker Clarify bias detection and explainability:Amazon SageMaker Clarify provides bias detection across the machine learning workflow, enabling developers to build greater fairness and transparency into their machine learning models. Once developers have prepared data for training and inference, they need to try to ensure the data is free from statistical bias and that model predictions are transparent, so they can explain how the model features are contributing to predictions. Today, developers sometimes try to use open source tools to detect statistical bias in their data, but these tools require a lot of manual effort and coding and are typically error prone. WithAmazon SageMaker Clarify, developers can now more easily detect statistical bias across the entire machine learning workflow and provide explanations for predictions their machine learning models are making.Amazon SageMaker Clarify integrates withAmazon SageMaker Data Wrangler where it runs a set of algorithms on features to identify bias during data preparation with visualizations that include a description of the sources and severity of possible bias. This way, developers can take steps for mitigation.Amazon SageMaker Clarify also integrates withAmazon SageMaker Experiments to make it easier to check trained models for statistical bias. It also details how each feature inputted into the model is affecting predictions. Finally,Amazon SageMaker Clarify integrates withAmazon SageMaker Model Monitor (anAmazon SageMaker capability that continuously monitors the quality of machine learning models in production) to alert developers if the importance of model features shifts and causes model behavior to change.- Deep Profiling for
Amazon SageMaker Debugger model training profiler: Deep Profiling forAmazon SageMaker Debugger now enables developers to train their models faster by automatically monitoring system resource utilization and providing alerts for training bottlenecks. Today, developers don’t have a standard way to monitor system utilization (e.g. GPU, CPU, network throughput, and memory I/O) to identify and troubleshoot bottlenecks in their training jobs. As a result, developers can’t train models as quickly and cost effectively as possible.Amazon SageMaker Debugger solves this problem with Deep Profiling’s newly announced capabilities, which provide developers the ability to visually profile and monitor system resource utilization inAmazon SageMaker Studio . This makes it easier to root cause issues and reduce the time and cost of training machine learning models. With these new capabilities,Amazon SageMaker Debugger expands its scope to monitor the utilization of system resources, send out alerts on problems during training inAmazon SageMaker Studio or via AWS CloudWatch, and correlate usage to different phases in the training job or a specific point in time during training (e.g. 28 minutes after the training job started).Amazon SageMaker Debugger can also trigger actions based on alerts (e.g. stop a training job when irregularities in GPU usage are detected).Amazon SageMaker Debugger’s Deep Profiling works across frameworks (PyTorch, Apache MXNet, and TensorFlow) and collects necessary system and training metrics automatically without requiring any code changes in training scripts. This allows developers to visualize how their system resources were used during training inAmazon SageMaker Studio . - Distributed Training on
Amazon SageMaker accelerates training times: New Distributed Training onAmazon SageMaker makes it possible to train large, complex deep learning models up to two times faster than current approaches. Today, advanced machine learning use cases—such as natural language processing for intelligent assistants, object detection and classification for autonomous vehicles, and image classification for large-scale content moderation—demand increasingly large datasets and more graphics processing unit (GPU) memory for training. However, some of these models are too big to fit in the memory provided by a single GPU. Customers can attempt to split models across multiple GPUs, but finding the best way to split the model and adjusting training code can often take weeks of tedious experimentation. To overcome these challenges, Distributed Training onAmazon SageMaker offers two distributed training capabilities that enable developers to train large models up to two times faster at no additional cost. Distributed Training withAmazon SageMaker’s Data Parallelism engine scales training jobs from one GPU to hundreds or thousands by automatically splitting data across multiple GPUs, improving training time by up to 40%. The reduction in training time is possible becauseAmazon SageMaker’s Data Parallelism engine manages GPUs for optimal synchronization using algorithms that are purposefully built to fully utilize AWS infrastructure with near-linear scaling efficiency. Distributed Training withAmazon SageMaker’s Model Parallelism engine can efficiently split large, complex models with billions of parameters across multiple GPUs by automatically profiling and identifying the best way to partition models. They do this by using graph partitioning algorithms to optimally balance computation and minimize communication between GPUs, resulting in minimal code changes and fewer errors caused by GPU memory constraints. Amazon SageMaker Edge Manager model management for edge devices:Amazon SageMaker Edge Manager allows developers to optimize, secure, monitor, and maintain machine learning models deployed on fleets of edge devices. Today, customers useAmazon SageMaker Neo to create optimized models for edge devices that run up to twice as fast, with less than a tenth of the memory footprint and no loss in accuracy. However, after deployment on edge devices, customers still need to manage and monitor the models to ensure they continue to perform with high accuracy.Amazon SageMaker Edge Manager optimizes models to run faster on target devices and provides model management for edge devices, so customers can prepare, run, monitor, and update deployed machine learning models across fleets of devices at the edge.Amazon SageMaker Edge Manager gives customers the ability to cryptographically sign their models, upload prediction data from their devices toAmazon SageMaker for monitoring and analysis, and view a dashboard that tracks and visually reports on the operation of the deployed models within theAmazon SageMaker console.Amazon SageMaker Edge Manager extends capabilities that were previously only available in the cloud by sampling data from edge devices and sending it toAmazon SageMaker Model Monitor for analysis, so developers can continuously improve model quality by retraining them when their accuracy declines over time.Amazon SageMaker JumpStart enables the machine learning journey:Amazon SageMaker JumpStart provides developers an easy-to-use, searchable interface to find best-in-class solutions, algorithms, and sample notebooks. Today, some customers that lack experience with machine learning have difficulty getting started with machine learning deployments, while more advanced developers find it difficult to adopt machine learning for all of their use cases. With today’s launch ofAmazon SageMaker JumpStart, customers can now quickly find relevant information specific to their machine learning use cases. Developers new to machine learning will be able to select from several complete end-to-end machine learning solutions (e.g. fraud detection, customer churn prediction, or forecasting) and deploy them directly in theirAmazon SageMaker Studio environments. And, experienced users will be able to choose from more than a hundred machine learning models to quickly get started on building and training models.
“Hundreds of thousands of everyday developers and data scientists have used our industry-leading machine learning service,
With corporate operations in 70 countries and sales in 200, 3M is creating the technology and products that advance every company, enhance every home, and improve everyday life. “3M’s success is grounded in our entrepreneurial researchers and our constant focus on science. One way we have advanced the science of our products is the adoption of machine learning on AWS,” said
Deloitte is helping transform organizations around the globe. The organization continuously evolves how it works and how it looks at marketplace challenges so it can continue to deliver measurable, sustainable results for its clients and communities. “Amazon SageMaker Data Wrangler enables us to hit the ground running to address our data preparation needs with a rich collection of transformation tools that accelerate the process of machine learning data preparation needed to take new products to market,” said
A subsidiary of
Snowflake Data Cloud shatters the barriers that have prevented organizations of all sizes from unleashing the true value from their data. “One of the biggest challenges our enterprise customers face is preparing data for machine learning projects,” said
Founded in 2013 by the original creators of Apache Spark™,
MongoDB Atlas is the fully managed service for MongoDB, the popular database designed to help teams build, scale, and iterate quickly. “Our mission at MongoDB is to free the genius within everyone by making data stunningly easy to work with. MongoDB Atlas runs more than 1.5 million database clusters, powering critical applications for our customers; we want to make it easy to build, train, and deploy machine learning models based on the data those applications generate,” said
Intuit is a mission-driven, global financial platform company and proud maker of TurboTax, QuickBooks, and Mint. “We chose to build Intuit’s new machine learning platform on AWS in 2017, combining
DeNA is a leading provider of mobile and online services, including games, e-commerce, and entertainment content distribution in
iFood is an online food delivery portal and one of the largest food delivery companies in
Since naming AWS as its official technology provider in
CS DISCO is a SaaS provider that offers solutions to automate and simplify a variety of legal tasks, including discovery. “At CS DISCO we have revolutionized the way legal evidence is reviewed with our DISCO AI platform for ediscovery,” said
Turbine is a simulation-driven drug discovery company delivering targeted cancer therapies to patients. “We use machine learning to train our in silico human cell model, called Simulated Cell™, based on a proprietary network architecture. By accurately predicting various interventions on the molecular level, Simulated Cell™ helps us to discover new cancer drugs and find combination partners for existing therapies,” said Kristóf Szalay, CTO at Turbine. “Training of our simulation is something we continuously iterate on, but on a single machine each training takes days, hindering our ability to iterate on new ideas quickly. We are very excited about Distributed Training on
Latent Space is a startup focused on building the world's first fully AI-rendered 3D game engine. “At Latent Space, we're building a neural rendered game engine where anyone can create at the speed of thought. Driven by advances in language modelling, we're working to incorporate semantic understanding of both text and images to determine what to generate,” said Sara Jane, Co-founder and Chief Science Officer at Latent Space. “Our current focus is on utilizing information retrieval to augment large-scale model training, for which we have sophisticated machine learning pipelines. This setup presents a challenge on top of distributed training since there are multiple data sources and models being trained at the same time. As such, we're leveraging the new distributed training capabilities in
Lenovo is the world's largest maker of personal computers. Lenovo designs and manufactures devices such as laptops, tablets, smartphones and a variety of smart IoT devices. “At Lenovo, we’re more than a hardware provider and are committed to being a trusted partner in transforming customers’ device experience and delivering on their business goals. Lenovo Device Intelligence is a great example of how we’re doing this with the power of machine learning, enhanced by
Basler AG is a leading manufacturer of high-quality digital cameras and accessories for industry, medicine, transportation and a variety of other markets. “Basler AG delivers intelligent computer vision solutions in a variety of industries, including manufacturing, medical, and retail applications. We are excited to extend our software offering with new features made possible by
Mission Automate handcrafts software solutions on behalf of their global customers. “We constantly look for new solutions that can provide the best quality software to our customers, but as a small organization, we don’t have the same ability to specialize in silos like other organizations,” said
MyCase offers a powerful legal practice management software that helps law firms run efficiently from anywhere, provide an exceptional client experience, and easily track firm performance. “We have several business and product elements that can be improved with machine learning,” said
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