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You can now run any open-source LLM from Vertex AI Model Garden or Hugging Face with BigQuery Machine Learning.

If you are working with ML models, having a reliable CI/CD (Continuous Integration and Continuous Deployment) workflow isn’t just a nice-to-have, it’s essential. Your team needs a robust, automated process to validate data, train models, and deploy them without human error slowing things down. That’s where ClearML comes in, offering a seamless solution to orchestrate, monitor, and automate your ML pipelines.

When it comes to leveraging AI to capture business value, it’s worth asking, “what kind of AI do we need exactly?” There are significant differences between the methodologies collectively referred to as AI. While 2024 might have almost convinced us that gen AI is the end-all-be-all, there is also what’s sometimes called ‘traditional’ AI, deep learning, and much more.

Gen AI applications can bring invaluable business value across multiple use cases and verticals. But sometimes it can be beneficial to experience different types of applications that can be created and operationalized with LLMs. Better understanding the potential value can help: In this blog post, we’ve curated the top gen AI demos of AI applications that can be developed with open-source MLRun. Each of these demos can be adapted to a number of industries and customized to specific needs.

By Erez Schnaider, Technical Product Marketing Manager, ClearML In a previous blog post, we demonstrated how easy it is to leverage Arm Neoverse-based Graviton instances on AWS to run training workloads. In this post, we’ll explore how ClearML simplifies the management and deployment of LLM inference using llama.cpp on Arm-based instances and helps deliver up to 4x performance compared to x86 alternatives on AWS. (Want to run llama.cpp directly?