Category: Advanced (300)
Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML […]
High-resolution imagery is very prevalent in today’s world, from satellite imagery to drones and DLSR cameras. From this imagery, we can capture damage due to natural disasters, anomalies in manufacturing equipment, or very small defects such as defects on printed circuit boards (PCBs) or semiconductors. Building anomaly detection models using high-resolution imagery can be challenging […]
In this post, we elucidate the simple yet powerful idea of combining user profiles and item attributes to generate personalized content recommendations using LLMs. As demonstrated throughout the post, these models hold immense potential in generating high-quality, context-aware input text, which leads to enhanced recommendations. To illustrate this, we guide you through the process of integrating a feature store (representing user profiles) with an LLM to generate these personalized recommendations.
In this post, we provide an overview of popular multimodality models. We also demonstrate how to deploy these pre-trained models on Amazon SageMaker. Furthermore, we discuss the diverse applications of these models, focusing particularly on several real-world scenarios, such as zero-shot tag and attribution generation for ecommerce and automatic prompt generation from images.
Prior authorization is a crucial process in healthcare that involves the approval of medical treatments or procedures before they are carried out. This process is necessary to ensure that patients receive the right care and that healthcare providers are following the correct procedures. However, prior authorization can be a time-consuming and complex process that requires […]
Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities
In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region. Our results reveal that the classification from the KNN model is more accurately representative of the state of the current crop field in 2017 than the ground truth classification data from 2015. These results are a testament to the power of Planet’s high-cadence geospatial imagery. Agricultural fields change often, sometimes multiple times a season, and having high-frequency satellite imagery available to observe and analyze this land can provide immense value to our understanding of agricultural land and quickly-changing environments.
In the world of data-driven decision-making, time series forecasting is key in enabling businesses to use historical data patterns to anticipate future outcomes. Whether you are working in asset risk management, trading, weather prediction, energy demand forecasting, vital sign monitoring, or traffic analysis, the ability to forecast accurately is crucial for success. In these applications, […]
In the rapidly evolving world of AI and machine learning (ML), foundation models (FMs) have shown tremendous potential for driving innovation and unlocking new use cases. However, as organizations increasingly harness the power of FMs, concerns surrounding data privacy, security, added cost, and compliance have become paramount. Regulated and compliance-oriented industries, such as financial services, […]
The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon Warehouses across Europe and the MENA region. The design and deployment processes of projects involve many types of Requests for Information (RFIs) about engineering requirements regarding Amazon and project-specific guidelines. These requests range from simple retrieval of baseline […]
As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers […]