Setting Up A Development Machine with MLRun and MinIO

Setting Up A Development Machine with MLRun and MinIO

MLOps is to machine learning what DevOps is to traditional software development. Both are a set of practices and principles aimed at improving collaboration between engineering teams (the Dev or ML) and IT operations (Ops) teams. The goal is to streamline the development lifecycle, from planning and development to deployment and operations, using automation. One of the primary benefits of

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Improve RAG Performance with Open-Parse Intelligent Chunking

Improve RAG Performance with Open-Parse Intelligent Chunking

If you are implementing a generative AI solution using Large Language Models (LLMs), you should consider a strategy that uses Retrieval-Augmented Generation (RAG) to build contextually aware prompts for your LLM. An important process that occurs in the preproduction pipeline of a RAG-enabled LLM is the chunking of document text so that only the most relevant sections of a document

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The Architect’s Guide: A Modern Datalake Reference Architecture

The Architect’s Guide: A Modern Datalake Reference Architecture

An abbreviated version of this post appeared on The New Stack on March 26th, 2024. Businesses aiming to maximize their data assets are adopting scalable, flexible, and unified data storage and analytics approaches. This trend is driven by enterprise architects tasked with crafting infrastructures that align with evolving business demands. A Modern Datalake architecture addresses this need by integrating the

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Architect’s Guide to a Reference Architecture for an AI/ML Datalake

Architect’s Guide to a Reference Architecture for an AI/ML Datalake

An abbreviated version of this post appeared on The New Stack on March 19th, 2024. In enterprise artificial intelligence, there are two main types of models: discriminative and generative. Discriminative models are used to classify or predict data, while generative models are used to create new data. Even though Generative AI has dominated the news of late, organizations are still

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MinIO Enterprise Cache: A Distributed DRAM Cache for Ultra-Performance

MinIO Enterprise Cache: A Distributed DRAM Cache for Ultra-Performance

As the computing world has evolved and the price of DRAM has plummeted, we find that server configurations often come with 500GB or more of DRAM. When you are dealing with larger deployments, even those with ultra-dense NVMe drives, the number of servers multiplied by the DRAM on those servers can quickly add up – often to several TBs. That DRAM

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The Strengths, Weaknesses and Dangers of LLMs

The Strengths, Weaknesses and Dangers of LLMs

Much has been said lately about the wonders of Large Language Models (LLMs). Most of these accolades are deserved. Ask ChatGPT to describe the General Theory of Relativity and you will get a very good (and accurate) answer. However, at the end of the day ChatGPT is still a computer program (as are all other LLMs) that is blindly executing

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Distributed Training and Experiment Tracking with Ray Train, MLflow, and MinIO

Distributed Training and Experiment Tracking with Ray Train, MLflow, and MinIO

Over the past few months, I have written about a number of different technologies (Ray Data, Ray Train, and MLflow). I thought it would make sense to pull them all together and deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. This post integrates the code I presented

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Distributed Data Processing with Ray Data and MinIO

Distributed Data Processing with Ray Data and MinIO

Introduction Distributed data processing is a key component of an efficient end-to-end distributed machine-learning training pipeline. This is true if you are building a basic neural network for statistical predictions where distributed training could mean each experiment runs in 10 minutes vs. an hour. It is also true if you are training or fine-tuning a Large Language Model (LLM) where

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Generative AI for the Enterprise

Generative AI for the Enterprise

Introduction Generative AI represents the latest technique an enterprise can employ to unlock the data trapped within its boundaries. The easiest way to conceptualize what is possible with Generative AI is to imagine a customized Large Language Model - similar to the one powering ChatGPT - running inside your firewall. Now, this custom LLM is not the same as the

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The Disruptive Nature of Data Lakehouses

The Disruptive Nature of Data Lakehouses

Introduction In 1997, Clayton Christensen, in his book The Innovator’s Dilemma, identified a pattern of innovation that tracked the capabilities, cost, and adoption by market segment between an incumbent and a new entrant. He labeled this pattern “Disruptive Innovation.” Not every successful product is disruptive - even if it causes well-established businesses to lose market share or even fail

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