AWS Machine Learning Blog

Category: Amazon SageMaker

Manage AutoML workflows with AWS Step Functions and AutoGluon on Amazon SageMaker

Running machine learning (ML) experiments in the cloud can span across many services and components. The ability to structure, automate, and track ML experiments is essential to enable rapid development of ML models. With the latest advancements in the field of automated machine learning (AutoML), namely the area of ML dedicated to the automation of […]

Import data from cross-account Amazon Redshift in Amazon SageMaker Data Wrangler for exploratory data analysis and data preparation

Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations with a multi-account architecture want to avoid situations where they must extract data from one […]

Predict types of machine failures with no-code machine learning using Amazon SageMaker Canvas

Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]

Visual inspection automation using Amazon SageMaker JumpStart

According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we’re still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements in deep learning in computer vision. […]

Identify mangrove forests using satellite image features using Amazon SageMaker Studio and Amazon SageMaker Autopilot – Part 2

Mangrove forests are an important part of a healthy ecosystem, and human activities are one of the major reasons for their gradual disappearance from coastlines around the world. Using a machine learning (ML) model to identify mangrove regions from a satellite image gives researchers an effective way to monitor the size of the forests over […]

Identify mangrove forests using satellite image features using Amazon SageMaker Studio and Amazon SageMaker Autopilot – Part 1

The increasing ubiquity of satellite data over the last two decades is helping scientists observe and monitor the health of our constantly changing planet. By tracking specific regions of the Earth’s surface, scientists can observe how regions like forests, water bodies, or glaciers change over time. One such region of interest for geologists is mangrove […]

How to scale machine learning inference for multi-tenant SaaS use cases

This post is co-written with Sowmya Manusani, Sr. Staff Machine Learning Engineer at Zendesk Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. It thrives on making over 170,000 companies worldwide serve their hundreds of millions of customers efficiently. The Machine Learning team at […]

How Mantium achieves low-latency GPT-J inference with DeepSpeed on Amazon SageMaker

Mantium is a global cloud platform provider for building AI applications and managing them at scale. Mantium’s end-to-end development platform enables enterprises and businesses of all sizes to build AI applications and automation faster and easier than what has been traditionally possible. With Mantium, technical and non-technical teams can prototype, develop, test, and deploy AI […]

Prepare data faster with PySpark and Altair code snippets in Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler is a purpose-built data aggregation and preparation tool for machine learning (ML). It allows you to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. The EDA feature comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving […]

Extract insights from SAP ERP with no-code ML solutions with Amazon AppFlow and Amazon SageMaker Canvas

Customers in industries like consumer packaged goods, manufacturing, and retail are always looking for ways to empower their operational processes by enriching them with insights and analytics generated from data. Tasks like sales forecasting directly affect operations such as raw material planning, procurement, manufacturing, distribution, and inbound/outbound logistics, and it can have many levels of […]