ClearML

In our latest research, conducted this year with AI Infrastructure Alliance and FuriosaAI, we wanted to know more about global AI Infrastructure plans, including respondents’: 1) Compute infrastructure growth plans 2) Current scheduling and compute solutions experience, and 3) Model and AI framework use and plans for 2024 Here's our blog post about the survey's key findings: https://lnkd.in/gvKeXGzB If you would like to download the report, please visit: https://lnkd.in/gG9EcQUQ Happy reading! #ai #machinelearning #compute #gpu #mlops #llm #llmops

If you are at #ReutersMomentum AI in San Jose today and tomorrow, be on the look out for our very own Jack Rosenblatt! He'll be happy to talk to you about #ai, #machinelearning, #llmops, and #mlops. Have a great time everyone! More info on the event here: https://lnkd.in/ecnynakY

Our Co-founder and GM, North America, Noam Harel, is quoted in this new article on our #aiinfrastructure research, written by Tim Sandle, Ph.D., CBiol, FIScT at Digital Journal. Read it here to learn what our respondents said about their plans to expand and maximize their AI infrastructure: https://lnkd.in/dfa_u5QV #ai #machinelearning #gpu #compute #llm #llmops

ICYMI: Why RAG has a place in your #llmops. With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want, or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build. When applied to a business scenario, however, the tolerance for models that provide incorrect or missing answers rapidly approaches 0%. We are quickly learning that broad, generic LLMs are not suitable for domain-specific or company-specific information retrieval. The large datasets that go into training an LLM often result in generic or confused responses, especially around concepts and terms that are loosely defined or have industry-specific meanings. Imagine an over-enthusiastic new employee with minimal previous work experience who confidently answers every question while lacking context and the latest information – this is akin to an LLM. This is why Retrieval Augmented Generation (RAG) has a pivotal role in the AI tech stack for LLMOps. Read the blog post to learn more => https://lnkd.in/gXuPdMgE Let us know what you think! #llm #llms #ai #machinelearning

Is there a better way to start the week than with a testimonial from a satisfied customer? We don't think so! See what Alon Faktor, PhD. at Vimeo has to say about the company's use of ClearML. We're so thrilled to help! What can we do for you? Let us know by contacting us; we're always happy to hear from you => https://lnkd.in/g2HeiW9z #mlops #ai #machinelearning #llms #llmops

Get started with ClearML to supercharge your #GenAI #LLMOps #MLOps . Best of all, it's open source and free! ➡ https://app.clear.ml/login #machinelearning #ai #llms #llmops

ICYMI: We’re psyched to announce v3.22 of our end-to-end #opensource #machinelearning & #ai platform. We’ve now built in new wizard functionality to help you create experiments instead of writing code. As well, we've rolled out a new Hyper-datasets grouping capability so that Scale and Enterprise customers can sample by property type (frame or region of interest). We've also updated statistics so that you can calculate them in real time, with greater choice of what to report on (not just labels). In fact, any property can be specified, giving you more hands-on UI-based content exploration capabilities. You can now look at your dataset in real time without exporting it, enabling you to understand what’s there. The new release also includes the availability of our previously announced Resource Allocation & Policy Manager available for Enterprise customers (see the news release here: https://lnkd.in/e6DJJ9iF). This new functionality enables you to control resource reservation, quota management, and spillover and prioritization on a more granular level to help you optimize your compute. We’ve just published a new blog that dives into how to use it, which you can find here: https://lnkd.in/eW7m2jTc Happy reading! #mlops #llmops #gai #genai #generativeai

We’re psyched to announce v3.22 of our end-to-end #opensource #machinelearning & #ai platform. We’ve now built in new wizard functionality to help you create experiments instead of writing code. As well, we've rolled out a new Hyper-datasets grouping capability so that Scale and Enterprise customers can sample by property type (frame or region of interest). We've also updated statistics so that you can calculate them in real time, with greater choice of what to report on (not just labels). In fact, any property can be specified, giving you more hands-on UI-based content exploration capabilities. You can now look at your dataset in real time without exporting it, enabling you to understand what’s there. The new release also includes the availability of our previously announced Resource Allocation & Policy Manager available for Enterprise customers (see the news release here: https://lnkd.in/g5zpBres). This new functionality enables you to control resource reservation, quota management, and spillover and prioritization on a more granular level to help you optimize your compute. We’ve just published a new blog that dives into how to use it, which you can find here: https://lnkd.in/gdZ-S5vq Happy reading! #mlops #llmops #gai #genai #generativeai

Last week we won the AI Breakthrough Award for "Best MLOps Platform." We strongly believe that our recent product enhancements helped us win the category. These include: - New AI orchestration and compute management capabilities, making it the first AI platform to support Kubernetes, Slurm, PBS, and bare metal for seamless orchestration of AI and machine learning workloads. ClearML now offers the broadest support for AI and HPC workloads in the marketplace. - Open source fractional GPU functionality, enabling users to optimize their GPU utilization for free. - A Resource Allocation & Policy Management Center, providing advanced user management for superior quota/over-quota management, priority, and granular control of compute resources allocation policies. - A Model Monitoring Dashboard designed for viewing all live model endpoints and monitoring their data outflows and compute usage. - Extensive new capabilities for managing and scheduling GPU compute resources, regardless of whether they are on-premise, in the cloud, or hybrid. Customers can now fully utilize GPUs for maximal usage with minimal costs, resulting in optimized access to their organization’s AI compute – expediting time to market, time to revenue, and time to value. Learn more about ClearML on our website, https://clear.ml Learn more about our latest award at: https://lnkd.in/gcCq_8Hx #mlops #ai #llms #llmops #machinelearning #gpu #compute

CRN reported on our new partnership with Carahsoft in this new article: "ClearML Allies With Carahsoft To Provide Its MLOps Platform To Government Agencies. Under the deal ClearML’s software will be sold through Carahsoft’s reseller network and government contract vehicles to state and federal government customers." Read more => https://lnkd.in/gzzWY86V #mlops #ai #llms #llmops #machinelearning