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Lessons from the field: How Generative AI is shaping software development in 2023

CIO Business Intelligence

The use of AI-generated code is still in an experimental phase for many organizations due to numerous uncertainties such as its impact on security, data privacy, copyright, and more. For example, litigation has surfaced against companies for training AI tools using data lakes with thousands of unlicensed works.

Software 120
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250
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Belcorp reimagines R&D with AI

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs. This allowed us to derive insights more easily.”

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CIOs press ahead for gen AI edge — despite misgivings

CIO Business Intelligence

Balancing risks, rewards The rate of pilot testing and POCs — this early in the game — is quite high, particularly for a rapidly advancing technology deemed by Elon Musk and others as potentially “civilization” destroying. First, we launched a private instance of GPT-3.5 for internal enterprise exploration.

Risk 141
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Of Muffins and Machine Learning Models

Cloudera

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

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Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes

AWS Big Data

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. You can use either the AWS Glue Data Catalog (recommended) or a Hive catalog for Iceberg tables.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Terminology Let’s first discuss some of the terminology used in this post: Research data lake on Amazon S3 – A data lake is a large, centralized repository that allows you to manage all your structured and unstructured data at any scale.