Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis. The common process for data preparation starts with collecting data, then cleaning it, labeling it, and finally validating and visualizing it. Getting the data right with high quality can often be a complex and time-consuming process. This is why customers who build machine learning (ML) workloads on AWS appreciate the ability of Amazon SageMaker Data Wrangler. With SageMaker Data Wrangler, customers can simplify the process of data preparation and complete the required processes of the data preparation workflow on a single visual interface. Amazon SageMaker Data Wrangler helps to reduce the time it…
Category: AWS
Reposts from Amazon Web Services (AWS).
Announcing Additional Data Connectors for Amazon AppFlow
Gathering insights from data is a more effective process if that data isn’t fragmented across multiple systems and data stores, whether on premises or in the cloud. Amazon AppFlow provides bidirectional data integration between on-premises systems and applications, SaaS applications, and AWS services. It helps customers break down data silos using a low- or no-code, cost-effective solution that’s easy to reconfigure in minutes as business needs change. Today, we’re pleased to announce the addition of 22 new data connectors for Amazon AppFlow, including: Marketing connectors (e.g., Facebook Ads, Google Ads, Instagram Ads, LinkedIn Ads). Connectors for customer service and engagement (e.g., MailChimp, Sendgrid, Zendesk Sell or Chat, and more). Business operations (Stripe, QuickBooks Online, and GitHub). In total, Amazon AppFlow…
New ML Governance Tools for Amazon SageMaker – Simplify Access Control and Enhance Transparency Over Your ML Projects
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…
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…