As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle. A common challenge in that effort is managing the right set of user permissions across different groups and ML activities. For example, a data scientist in your team that builds and trains models usually requires different permissions than an MLOps engineer that manages ML pipelines. Another challenge is improving visibility over ML projects. For example, model information, such as intended use, out-of-scope use cases, risk rating, and evaluation results, is often captured and shared via emails or documents. In addition, there is often no simple mechanism…
Month: November 2022
Join the Preview – AWS Glue Data Quality
Back in 1980, at my second professional programming job, I was working on a project that analyzed driver’s license data from a bunch of US states. At that time data of that type was generally stored in fixed-length records, with values carefully (or not) encoded into each field. Although we were given schemas for the data, we would invariably find that the developers had to resort to tricks in order to represent values that were not anticipated up front. For example, coding for someone with heterochromia, eyes of different colors. We ended up doing a full scan of the data ahead of our actual time-consuming and expensive analytics run in order to make sure that we were dealing with known…
New – Trusted Language Extensions for PostgreSQL on Amazon Aurora and Amazon RDS
PostgreSQL has become the preferred open-source relational database for many enterprises and start-ups with its extensible design for developers. One of the reasons developers use PostgreSQL is it allows them to add database functionality by building extensions with their preferred programming languages. You can already install and use PostgreSQL extensions in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service for PostgreSQL. We support more than 85 PostgreSQL extensions in Amazon Aurora and Amazon RDS, such as the pgAudit extension for logging your database activity. While many workloads use these extensions, we heard our customers asking for flexibility to build and run the extensions of their choosing for their PostgreSQL database instances. Today, we are announcing the general availability of Trusted…
Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data
You use map apps every day to find your favorite restaurant or travel the fastest route using geospatial data. There are two types of geospatial data: vector data that uses two-dimensional geometries such as a building location (points), roads (lines), or land boundary (polygons), and raster data such as satellite and aerial images. Last year, we introduced Amazon Location Service, which makes it easy for developers to add location functionality to their applications. With Amazon Location Service, you can visualize a map, search points of interest, optimize delivery routes, track assets, and use geofencing to detect entry and exit events in your defined geographical boundary. However, if you want to make predictions from geospatial data using machine learning (ML), there…