To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment. The main objective of Amazon SageMaker Data Wrangler is to make it easy to do data preparation and data processing workloads. With SageMaker Data Wrangler, customers can simplify the process of data preparation and all of the necessary steps of data preparation workflow on a single visual interface. SageMaker Data Wrangler reduces the time to rapidly prototype and deploy data processing workloads to production, so customers can easily integrate…
New — Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources
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…
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…