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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data platform architecture has an interesting history. A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

A feature store is a data platform that supports the creation and use of feature data throughout the lifecycle of an ML model, from creating features that can be reused across many models to model training to model inference (making predictions). It can also transform incoming data on the fly. What is a feature store?

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

foundation models to help users discover, augment, and enrich data with natural language. Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access.

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Building ML Platform in Retail and eCommerce

The MLOps Blog

You may also like Building a Machine Learning Platform [Definitive Guide] Consideration for data platform Setting up the Data Platform in the right way is key to the success of an ML Platform. In the following sections, we will discuss best practices while setting up a Data Platform for Retail.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

Cloud-based data storage solutions, such as Amazon S3 (Simple Storage Service) and Google Cloud Storage, provide highly durable and scalable repositories for storing large volumes of data. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Morgan Kaufmann.

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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah. Thanks for having me.

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