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Foundational models at the edge

IBM Journey to AI blog

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. Large language models (LLMs) have taken the field of AI by storm. IBM watsonx consists of the following: IBM watsonx.ai

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How the DataRobot AI Platform Is Delivering Value-Driven AI

DataRobot Blog

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. This narrow focus can lead to accurate and true insights that are not really useful, leaving business stakeholders feeling frustrated.

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Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

It relates to employing algorithms to find and examine data patterns to forecast future events. Through practice, machines pick up information or skills (or data). Algorithms and models Predictive analytics uses several methods from fields like machine learning, data mining, statistics, analysis, and modeling.

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Why Software Engineers Should Be Embracing AI: A Guide to Staying Ahead

ODSC - Open Data Science

AI for DevOps and CI/CD: Streamlining the Pipeline Continuous Integration and Continuous Delivery (CI/CD) are essential components of modern software development, and AI is now helping to optimize this process. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time.

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Top Synthetic Data Tools/Startups For Machine Learning Models in 2023

Marktechpost

Information created intentionally rather than as a result of actual events is known as synthetic data. Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets.

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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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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Checking at intervals to make sure that model performance isn’t degrading in production is a good MLOps practice for both teams and platforms.