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Building AI for business: IBM’s Granite foundation models

IBM Big Data Hub

Today we are announcing our latest addition: a new family of IBM-built foundation models which will be available in watsonx.ai , our studio for generative AI, foundation models and machine learning. Collectively named “Granite,” these multi-size foundation models apply generative AI to both language and code.

Modeling 103
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Public cloud use cases: 10 ways organizations are leveraging public cloud

IBM Big Data Hub

In the business sphere, both large enterprises and small startups depend on public cloud computing models to provide the flexibility, cost-effectiveness and scalability needed to fuel business growth. In a public cloud computing model, a cloud service provider (CSP) owns and operates vast physical data centers that run client workloads.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Your Chance: Want to test a professional logistics analytics software? 10 Essential Big Data Use Cases in Logistics Now that you’re up to speed on the perks of investing in analytics, let’s look at some practical examples that highlight the growing importance of data in logistics, based on different business scenarios.

Big Data 275
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Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

AWS Big Data

Large language models (LLMs) are becoming increasing popular, with new use cases constantly being explored. This is where model fine-tuning can help. Before you can fine-tune a model, you need to find a task-specific dataset. Next, we use Amazon SageMaker JumpStart to fine-tune the Llama 2 model with the preprocessed dataset.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. For machine learning systems used in consumer internet companies, models are often continuously retrained many times a day using billions of entirely new input-output pairs.

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Enable advanced search capabilities for Amazon Keyspaces data by integrating with Amazon OpenSearch Service

AWS Big Data

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. It empowers businesses to explore and gain insights from large volumes of data quickly. Choose the Test tab.