LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.

We’re thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): “Summarization with Gemini from Vertex AI”. An AMP is a pre-built, high-quality minimal viable product (MVP) for Artificial Intelligence (AI) use cases that can be deployed in a single-click from Cloudera AI (CAI). AMPs are all about helping you quickly build performant AI applications. More on AMPs can be found here.

Sorry for the click-bait title, but everyone is talking about AI Agents, and for a good reason. With the proliferation of LLMs, everyone – from software engineers using LLMs as a coding copilot to people using AI to plan vacations – is looking for new ways to use the technology that isn’t just answering questions or searching knowledgebases.

As organizations evolve – onboarding new team members, expanding use cases, and broadening the scope of model development, their compute infrastructure grows increasingly complex. What often begins as a single cloud account using available credits can quickly expand into a hybrid mix of on-prem and cloud resources that come with different associated costs and are tailored to diverse workloads.

Enterprise organizations collect massive volumes of unstructured data, such as images, handwritten text, documents, and more. They also still capture much of this data through manual processes. The way to leverage this for business insight is to digitize that data. One of the biggest challenges with digitizing the output of these manual processes is transforming this unstructured data into something that can actually deliver actionable insights.

ClearML’s end-to-end AI Platform supports AI builders through every stage of the process, from data preparation and management to experimentation, deployment, and performance monitoring. At the heart of ClearML’s data management capabilities is its unique approach to visual data handling, known as Hyper-datasets. We’re sure you know all about the importance of data versioning, but here’s a quick reminder: effective data management is essential for.

We’re excited to announce the ClearML 3.23 Enterprise Release. This release further helps AI builders speed models into production as well as monitor their usage, combining new features with new views and shortcuts that improve ease of use.

When it comes to building AI models, the process is often oversimplified as “just get a GPU and start building.” While securing access to GPUs can be a challenge, gaining access to GPU clusters is only the beginning of the journey. The real complexity lies in effectively leveraging GPU capabilities to deliver meaningful business impact.

As the open-source maintainers of MLRun, we’re proud to announce the release of MLRun v1.7. MLRun is an open-source AI orchestration tool that accelerates the deployment of gen AI applications, with features such as LLM monitoring, fine-tuning, data management, guardrails and more. We provide ready-made scenarios that can be easily implemented by teams in organizations.