In November of 2023, Amazon announced the S3 Connector for PyTorch. The Amazon S3 Connector for PyTorch provides implementations of PyTorch's dataset primitives (Datasets and DataLoaders) that are purpose-built for S3 object storage. It supports map-style datasets for random data access patterns and iterable-style datasets for streaming sequential data access patterns.
The S3 Connector for PyTorch also includes
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Earlier this month, Amazon held their re:Invent conference in Las Vegas, Nevada, from December 1st to 5th - a 5-day event. If you have never been to a re:Invent conference, then the word that describes it best is “huge” - not just in terms of the number of attendees (60,000) but also the breadth of topics covered.
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In November of 2023 Amazon announced the S3 Connector for PyTorch. The Amazon S3 Connector for PyTorch provides implementations of PyTorch's dataset primitives (Datasets and DataLoaders) that are purpose-built for S3 object storage. It supports map-style datasets for random data access patterns and iterable-style datasets for streaming sequential data access patterns.
In a previous post, I introduced the
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Almost a year ago (actually 11 months ago), I wrote about the “Starving GPU Problem” and how the horsepower of Nvidia’s Graphic Processing Units (GPUs) could be so powerful that your network and your storage solution may not be able to keep up - preventing your expensive GPUs from being fully utilized. Well, in those short 11 months, a
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A mobile application is a company's brand available on demand. It is a window into any service or product an organization offers. At Kobiton, they understand this—it is their mission to improve mobile applications through testing.
Kobiton is a mobile testing platform that allows customers to perform manual and automated testing on real mobile devices from anywhere
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One of the newest features of the AIStor is a private cloud version of the highly popular, open-source project, Hugging Face. This post details how AIStor’s AIHub effectively creates an API compatible, private cloud version of Hugging Face that is fully under the enterprise's control.
Before we get started, it makes sense to introduce Hugging Face.
Hugging
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Before diving into Amazon’s S3 Connector for PyTorch, it is worthwhile to introduce the problem it is intended to solve. Many AI models need to be trained on data that cannot fit into memory. Furthermore, many really interesting models being built for computer vision and generative AI use data that cannot even fit on the disk drive that comes
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One of the biggest challenges facing organizations today for AI and data management is access to reliable infrastructure and compute resources. The Intel Tiber Developer Cloud is purpose-built for engineers who need an environment for proof-of-concepts, experimentation, model training, and service deployments. Unlike other clouds, which can be unapproachable and complex, the Intel Tiber Developer Cloud is simple and easy
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Parsec Labs is a company of engineers. Most have designed storage systems, been responsible for backups and replication, or worked in networking building switches. Founded in 2013, their Unified Data Mobility and Protection Appliance provides the most straightforward tools for migrating, replicating, and backing up data at scale.
A Common Request
As a one-time pre-sales engineer, Mark Clark, CEO of
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Microblink is an AI company specializing in image detection. They got their start in the identity space with products like BlinkID, BlinkID Verify, and BlinkCard. Most recently, their image detection capabilities have led to products that can process other types of images. For example, product detection can be performed on receipts, whereby product descriptions on a receipt are used to
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This post first appeared on The New Stack on July 29th, 2024.
Artificial Intelligence is in the middle of a perfect storm in the software industry, and now Mark Zuckerberg is calling for open-sourced AI.
Three powerful perspectives are colliding on how to control AI:
1. All AI should be open-source for sharing and transparency.
2. Keep AI closed-source and
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An embedding subsystem is one of four subsystems needed to implement Retrieval Augmented Generation. It turns your custom corpus into a database of vectors that can be searched for semantic meaning. The other subsystems are the data pipeline for creating your custom corpus, the retriever for querying the vector database to add more context to a user query, and finally,
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With all the talk in the industry today regarding large language models with their encoders, decoders, multi-headed attention layers, and billions (soon trillions) of parameters, it is tempting to believe that good AI is the result of model design only. Unfortunately, this is not the case. Good AI requires more than a well-designed model. It also requires properly constructed training
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MLOps, short for Machine Learning Operations, is a set of practices and tools aimed at addressing the specific needs of engineers building models and moving them into production. Some organizations start off with a few homegrown tools that version datasets after each experiment and checkpoint models after every epoch of training. On the other hand, many organizations have chosen to
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This post first appeared on The New Stack on June 3rd, 2024.
I previously wrote about the modern data lake reference architecture, addressing the challenges in every enterprise — more data, aging Hadoop tooling (specifically HDFS) and greater demands for RESTful APIs (S3) and performance — but I want to fill in some gaps.
The modern data lake, sometimes referred to as
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In my previous post on MLRun, we set up a development machine with all the tools needed to experiment with MLRun. Specifically, we used a docker-compose file to create containers for the MLRun UI, the MLRun API Service, Nuclio, MinIO, and a Jupyter service. Once our containers started, we ran a simple smoke test to ensure everything was working correctly.
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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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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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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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Anyone who has worked in a team environment knows that every successful team has one go-to person—that special individual who can help you regardless of the nature of your problem. On a traditional software development team, this individual is an expert programmer and is also an expert in one other technology, which could be a database technology like Snowflake
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Replication
Versioning
Observability
Encryption
Key Management Server
S3 Compatibility
Object Immutability
Catalog
Object Prompt
Identity + Access Mgt
Firewall
AIHub
Lifecycle
Cache
Events and Lambdas
Console
Erasure Code Calculator
Reference Hardware
Integrations with MinIO
Download MinIO Object Store
Events