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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Flipboard

For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

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In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

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Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink

AWS Machine Learning Blog

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. It offers an AWS CloudFormation template for straightforward deployment in an AWS account.

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations. Data integration: Integrate data from various sources into a centralized cloud data warehouse or data lake. Ensure that data is clean, consistent, and up-to-date.

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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning Blog

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language. Nitin Eusebius is a Sr.

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