Microsoft Fabric is an end-to-end suite of cloud-based tools for data analytics, encompassing data movement, data storage, data engineering, data integration, data science, real-time analytics, and business intelligence.
Stories by Martin Heller
Large language models evolved alongside deep-learning neural networks and are critical to generative AI. Here's a first look, including the top LLMs and what they're used for today.
InfoWorld’s 2023 Bossie Awards recognise the year’s leading open source tools for software development, data management, analytics, AI, and machine learning.
Use LangSmith to debug, test, evaluate, and monitor chains and intelligent agents in LangChain and other LLM applications.
Llama 2 is a family of generative text models that are optimised for assistant-like chat use cases or can be adapted for a variety of natural language generation tasks. Code Llama models are fine-tuned for programming tasks.
Many low-code and no-code development and RPA platforms now include AI capabilities, often using a version of GPT.
Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.
Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases.
Ballerina was designed to simplify the development of distributed microservices by making it easier to integrate APIs. For C, C++, C#, and Java programmers, much will feel familiar.
Dremio Cloud leaps big data in a single bound with a fast SQL engine and optimisations that can accelerate queries dramatically. Plus it lets you use other engines on the same data.
Come for the fast editing. Stay for the debugging, source code management support, and huge ecosystem of extensions.
PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here's how to get started with PyTorch.
Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset.
Human-in-the-loop machine learning takes advantage of human feedback to eliminate errors in training data and improve the accuracy of models.
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