A collection of 122 posts tagged with "AI/ML"
About MLflow MLflow is an open-source platform designed to manage the complete machine learning lifecycle. Databricks created it as an internal project to address challenges faced in their own machine learning development and deployment processes. MLflow was later released as an open-source project in June 2018. As a tool for managing the complete lifecycle, MLflow contains the following components. * MLflow
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Making the serving of your AI models more lightweight by leveraging the simplicity of MinIO’s object store. tl;dr MinIO object storage can be used as a ‘single source of truth’ for your machine learning models and, in turn, make serving with PyTorch Serve more efficient when managing changes to Large Language Models (LLMs). As always, sample code is
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This post was written in collaboration with Harinder Mashiana from cnvrg.io. Large language models (LLMs) have revolutionized the world of technology, offering powerful capabilities for text analysis, language translation, and chatbot interactions. The revolution will heavily impact businesses, according to OpenAI, approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by
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Introduction In a few previous posts on AI/ML, I mentioned that one of the benefits of MinIO is that you have tools for Versioning, Lifecycle Management, Object Locking, Object Retention and Legal Holds. These capabilities have a variety of uses. You may need a simple way to keep track of training experiments. You could also use these features to
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Introduction In a previous post, I covered Building an ML Data Pipeline with MinIO and Kubeflow v2.0. The data pipeline I created downloaded US Census data to a dedicated instance of MinIO. This is different from the MinIO instance Kubeflow Pipelines (KFP) uses internally. We could have tried to use KFP’s instance of MinIO - however, this is
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This post first appeared in The New Stack. Developers gravitate to technologies that are software defined, open source, cloud native and simple. That essentially defines object storage. Introduction Choosing the best storage for all phases of a machine learning (ML) project is critical. Research engineers need to create multiple versions of datasets and experiment with different model architectures. When a
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Engineers like to play and learn locally. It does not matter which tool is under investigation: a high-end storage solution, a workflow orchestration engine, or the latest thing in distributed computing. The best way to learn a new technology is to find a way to cram it all on a single machine so that you can put your hands on
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Kubeflow Pipelines (KFP) is the most popular feature of Kubeflow. A Python engineer can turn a function written in plain old Python into a component that runs in Kubernetes using the KFP decorators. If you used KFP v1, be warned - the programming model in KFP v2 is very different - however, it is a big improvement. Transforming plain old
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This tutorial will use R, H2O, and MinIO to build a very simple statistical arbitrage model using foreign exchange (Forex) data.
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Let open source software help you with simplifying enterprise conversational AI needs and let MinIO handle the storage solutions to enable continuous learning and optimize the knowledge base for improved chatbot experience.
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In this tutorial, you'll learn how to create a production capable log processing workflow using R, H2o and MinIO.
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Learn about anomaly detection and autoencoding with R, H2O and MinIO.
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I’ve been working with neural networks and machine learning since the late ‘80. Yes, I’m that old. The first product I bought was California Scientific Software BrainMaker Professional. I loved that product because it got me started with neural networks. I still have it: It was a 3 layer neural network product that came with source code in
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This post first appeared in The New Stack. With the constant evolution of the enterprise, machine learning and artificial intelligence have become board-level initiatives. Marketing claims aside, capabilities that seemed almost mythical a few years ago are now taken for granted as AI/ML becomes baked into every software stack and architecture. This is becoming known as AI-first architecture. In
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Kubeflow is a modern solution to design, build and orchestrate Machine Learning pipelines using the latest and most popular frameworks. Out of the box, Kubeflow ships with MinIO inside to store all of its pipelines, artifacts and logs, however that MinIO is limited to a single PVC and thus cannot benefit from all the features a distributed MinIO brings to
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Stellar performance-at-scale, flexibility and consistency make object storage the best choice for cloud-native enterprises.
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Learn how to run Kubeflow on Azure Kubernetes Service with MinIO.
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We are living in a transformative era defined by information and AI. Massive amounts of data are generated and collected every day to feed these voracious, state-of-the-art, AI/ML algorithms. The more data, the better the outcomes. One of the frameworks that has emerged as the lead industry standards is Google's TensorFlow. Highly versatile, one can get started
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With another Strata in the rearview mirror, it is time to reflect on what we saw and heard during the week. Strata is clearly a data science show at this point but data science is broad topic. Our perspective, as a provider of high performance object storage, is framed accordingly and we focus on the data stack more than we
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One of the key challenges in any digitization journey is the adoption of machine learning techniques. Given the explosion of tools and frameworks, it can be difficult
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