AWS Machine Learning Blog

Category: Technical How-to

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

Machine learning (ML) models are taking the world by storm. Their performance relies on using the right training data and choosing the right model and algorithm. But it doesn’t end here. Typically, algorithms defer some design decisions to the ML practitioner to adopt for their specific data and task. These deferred design decisions manifest themselves […]

Implementing Amazon Forecast in the retail industry: A journey from POC to production

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time-series forecasts. Recently, based on Amazon Forecast, we helped one of our retail customers achieve accurate demand forecasting, within 8 weeks. The solution improved the manual forecast by an average of 10% in regards to the […]

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

A well-designed CI/CD pipeline is essential to scale any software development workflow effectively. When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. At AWS, we’re continuing to innovate to simplify the MLOps workflow. In this post, we discuss some […]

Malware detection and classification with Amazon Rekognition

According to an article by Cybersecurity Ventures, the damage caused by Ransomware (a type of malware that can block users from accessing their data unless they pay a ransom) increased by 57 times in 2021 as compared to 2015. Furthermore, it’s predicted to cost its victims $265 billion (USD) annually by 2031. At the time […]

Get more control of your Amazon SageMaker Data Wrangler workloads with parameterized datasets and scheduled jobs

Data is transforming every field and every business. However, with data growing faster than most companies can keep track of, collecting data and getting value out of that data is a challenging thing to do. A modern data strategy can help you create better business outcomes with data. AWS provides the most complete set of […]

Run machine learning inference workloads on AWS Graviton-based instances with Amazon SageMaker

Today, we are launching Amazon SageMaker inference on AWS Graviton to enable you to take advantage of the price, performance, and efficiency benefits that come from Graviton chips. Graviton-based instances are available for model inference in SageMaker. This post helps you migrate and deploy a machine learning (ML) inference workload from x86 to Graviton-based instances […]

Serve multiple models with Amazon SageMaker and Triton Inference Server

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. It helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. In 2021, AWS announced the integration of NVIDIA Triton Inference Server in SageMaker. You […]

Automated exploratory data analysis and model operationalization framework with a human in the loop

Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase […]

Deploy a machine learning inference data capture solution on AWS Lambda

Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, […]

Create synthetic data for computer vision pipelines on AWS

Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve successfully collected data, you still have […]