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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. It required a different data platform solution. It was Datawarehouse.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Journey to AI blog

It’s often described as a way to simply increase data access, but the transition is about far more than that. When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. foundation models to help users discover, augment, and enrich data with natural language. IBM watsonx.ai

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

After a few minutes, a transcript is produced with Amazon Transcribe Call Analytics and saved to another S3 bucket for processing by other business intelligence (BI) tools. PCA’s security features ensure that any PII data was redacted from the transcript, as well as from the audio file itself.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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

Artificial Corner

This period also saw the development of the first data warehouses, large storage repositories that held data from different sources in a consistent format. The concept of data warehousing was introduced by Bill Inmon, often referred to as the “father of data warehousing.”