Modernize Your Machine Learning Development Process
Machine learning (ML) has become a core technology ingredient in a wide range of use cases from natural language processing and computer vision to fraud detection, demand forecasting, product recommendations, preventive maintenance, and document processing. Harnessing the benefits of machine learning at scale requires standardizing on a modern ML development process across your business. Modernizing your ML development process can accelerate your pace of innovation by providing scalable infrastructure, integrated tooling, healthy practices for responsible use of ML, a choice of tools accessible to developers and data scientists of all ML skill levels, and efficient resource management to keep costs low.
Accelerate ML innovation
Reduce ML model development time from months to weeks and get models to market faster. Improve data scientist productivity with purpose-built tools for every step of ML development. Automate ML processes with MLOps to scale model development.
Foster responsible use of ML
Detect bias across the entire ML workflow to build greater fairness and transparency into your models. Leverage the comprehensive set of AWS security and governance features to help your organization with security requirements that may apply to ML workloads.
Innovate with any ML skill level
Allow your developers and data scientists to develop ML models the way they like. Let data scientists write code in an integrated development environment, automatically build ML models, or deploy pre-built solutions for popular use cases with a few clicks.
Reduce the total cost of ownership by over 54% compared to self-managed options by automatically optimizing infrastructure and improving resource utilization.
Lyft’s autonomous vehicle division, Lyft Level 5 standardized on Amazon SageMaker for training and reduced model training times from days to under a couple of hours.
With Amazon SageMaker Clarify, The Deutsche Fußball Liga (DFL) GmbH can understand the key components of the Bundesliga Match Facts insights to deliver higher-quality insights to football fans.
Freddy’s Frozen Custard & Steakburgers used Amazon SageMaker Autopilot through Domo to deploy machine learning models without having to hire ML experts and achieved double-digit sales growth.
Using Amazon SageMaker, NerdWallet reduced ML training costs by around 75 percent, even while increasing the number of models trained.
Analyze images accurately
Develop computer vision models for a wide range of use cases including object detection, medical diagnosis, and autonomous driving. For example, healthcare customers can use SageMaker capabilities, such as image classification, to improve the diagnosis of patients, reduce the subjectivity in diagnosis, and reduce the workload of pathologists.
Automate text processing
Build ML models to automatically process, and analyze data from handwritten and electronic documents so you can analyze documents faster, more accurately, and cost-effectively. Amazon SageMaker provides built-in ML algorithms, such as BlazingText and Linear Learner, that are optimized for text classification, natural language processing (NLP), and optical character recognition (OCR). SageMaker also integrates with Hugging Face, a popular NLP model library.
Detect anomalies quickly
Identify anomalies in data for a range of applications such as fraud detection and predictive maintenance. For example, identify suspicious transactions before they occur using ML and alert your customers on time to strengthen customer trust. SageMaker provides built-in ML algorithms, such as Random Cut Forest and XGBoost, that you can use to train and deploy fraud detection models quickly.
Deliver personalized recommendations
Deliver customized online experiences to customers, improve customer satisfaction, and grow business rapidly using ML. Amazon SageMaker provides built-in ML algorithms, such as factorization machines, to build recommendation engines. You can also use SageMaker Autopilot to automatically generate a personalization model and deploy it with just a few clicks.
Featured Solutions on AWS
Discover Purpose-Built Services, AWS Solutions, Partner Solutions, and Guidance to rapidly address your business and technical use cases.
Amazon SageMaker helps you modernize your ML environment readily across your lines of business, enabling developers and data scientists at all ML skill levels to build, train, and deploy machine learning models for virtually any use case. SageMaker brings together a broad set of purpose-built ML capabilities under one unified, visual user interface, eliminating the need to build your own ML environment, so you can focus on your core business. SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices.
MLOps Workload Orchestrator
This solution helps you streamline and enforce architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services.
Guidance for Overhead Imagery Inference on AWS
Learn how to process remote sensing imagery using machine learning models that automatically detect and identify objects collected from satellites, unmanned aerial vehicles, and other remote sensing devices.
Guidance for Distributed Model Training on AWS
This Guidance helps customers who have on-premises restrictions or who have existing Kubernetes investments to use either Amazon Elastic Kubernetes Service (Amazon EKS) and Kubeflow or Amazon SageMaker to implement a hybrid, distributed machine learning (ML) training architecture.
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