Modernize Your Machine Learning Development Process

Accelerate machine learning innovation at scale while reducing costs

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. 

Modernize your machine learning development using Amazon SageMaker (1:45)

Benefits

Accelerate ML innovation

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

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 regardless of ML skill level

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 cost

Reduce costs

Reduce the total cost of ownership by over 54% compared to self-managed options by automatically optimizing infrastructure and improving resource utilization. 

Customer stories

Lyft

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. 

Read blog to learn more »

Bundesliga

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.

Read the blog »

Freddy's

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. 

Learn more »

NerdWallet

Using Amazon SageMaker, NerdWallet reduced ML training costs by around 75 percent, even while increasing the number of models trained.

Read the case study »

Use cases

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.

AWS Solution

Amazon SageMaker

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.

Learn more about Amazon SageMaker »

Resources

Amazon SageMaker step-by-step guide

Watch the video »

Amazon SageMaker ten-minute tutorial

See tutorial »

Pre-built solutions available in Amazon SageMaker JumpStart

Get started with SageMaker »

Ready to get started?

Contact sales
Contact us

Contact us for more information on ML modernization.

Contact us 
Find a partner
Find a Partner

Contact the AWS Partner Network, to work with our global Technology and Consulting Partners.

Get started 
Get started with executing initiatives
Get started on executing your ML modernization initiatives

The AWS Professional Services organization is a global team of experts that can help you realize your desired business outcomes using AWS.

Learn more