UbiOps - powerful AI model serving & orchestration’s Post

Domino Data Lab has expanded its capabilities beyond #MLOps to create a broader #AI platform that encompasses all aspects of model development and deployment to help enterprises build and operate AI at scale. Learn more in this perspective: https://bit.ly/4aMMqs2

Generative AI applications are still applications, so you need the following: ·     Operational databases to support the user experience for interaction steps outside of invoking generative AI models. ·     Data lakes to store your domain-specific data, and analytics to explore them and understand how to use them in generative AI. ·     Data integrations and pipelines to manage (sourcing, transforming, enriching, and validating, among others) and render data usable with generative AI. ·     Governance to manage aspects such as data quality, privacy and compliance to applicable privacy laws, and security and access controls. In this blog post, you will find a framework to implement generative AI applications enriched and differentiated with your data. https://lnkd.in/g5HReKAY

Shannon Phu wrote an amazing article about how Discord approaches leveraging generative AI to implement, deploy (at scale), and improve user experience in their different product features and points to keep in mind. Here is the article: https://lnkd.in/eAE4AVkA However, this made me reflect on which points never concern Databricks' customers who start to tap into their GenAI journey and with whom I touch on a regular basis to craft possible solution journeys. The answer is that Databricks' Data Intelligence platform is built by AI practitioners for data and AI practitioners. Therefore, the end to end user journey is very well thought through for data and AI use cases. This enables a faster development cycle, quick iteration, and, most importantly, confident monitoring of specifically GenAI applications all under one unified umbrella. Here are some capabilities that will help you make this journey with as little friction as possible. Scalability and ease of deployment are never a problem. 1️⃣ Foundational Models API: Some very well open-source models, like DBRX, Llama-2-70B, Mixtral-8x7B, and BGE-Large (embedding) are readily available and deployed as serverless endpoints. So, so that users can quickly start with prompt engineering and polish the prompt quickly. Yes, that's how easy it is. https://lnkd.in/e5XrVE-k 2️⃣ Model Serving: With serverless model serving capability, Databricks' users can easily deploy their registered and governed models with MLflow with a matter of few clicks. Yes, model serving is enhanced and tuned for LLM deployment with the same ease of T-Shirt based deployment options. So, register your classical AI or GenAI models and easily serve them on scalable by design infra. https://lnkd.in/euGCZ-Fm 3️⃣ AI Playground: An important aspect of iteration is A/B testing. Once you have deployed with few clicks your own custom model or would like to compare already available foundational models for your application, you can simply get them to test in AI Playground. It's as simple as adding different models next to each other, providing them with prompts, and checking their speed, and it's especially useful for manual validation. Check this out https://lnkd.in/e2gBNzZx 4️⃣ Inference Tables: All request responses of model serving can be easily stored as delta tables for monitoring. From an AI product development perspective, this is useful to monitor the deployed AI model and also to see how users are interacting with this service. Full circle. https://lnkd.in/eNfYPVRr and many more ... Try out this hands-on RAG demo in Databricks: https://lnkd.in/e7pU83GS #genai #databricks #ModelServing

❓What is multi-model routing❓ Multi-model routing is a process of linking multiple AI models together. The routing can either be done in series or in parallel, meaning that you use a router to send prompts to specific models. ➡️ Multi-model routing is very easy to achieve with UbiOps using our Pipelines feature... There are several uses for multi-model routing. In this article, we will discuss three: - Multimodality - Prompt enhancement - Expert routing Read all about it in our article: https://bit.ly/3LKseMx #multimodel #routing #pipelines #ubiops #mlops #ml #ai #llm #llms

Custom input and output parameters, Slack integration, OpenAI GPT 4o, and more... This is one of the biggest updates so far, enabling true flexible LLM pipeline building. #llmops #llmpipeline #llm #largelanguagemodel #ai #agent #artificialintelligence

Are organisations using MLOps practices as a mere disguise for responsible AI? The concept of "responsible AI" has gained prominence in the realm of artificial intelligence and machine learning operations. However, there is a growing concern that some organisations might not be implementing responsible AI practices despite their use of MLOps. Marcell Ferencz - Solution Architect at Databricks #AI #MLOps #ResponsibleAI https://lnkd.in/emhCjpCv

💥📢 New announcement Power Automate 📢💥 <p>We are excited to announce that GPT Prompts with Prompt Builder, a new feature of AI Builder, is now generally available! GPT Prompts are powerful tools that enable you to add Generative AI capabilities to your automated workflows.</p> #PowerAutomate #PowerPlatform #PowerAddicts #powerAddictsBE #LowCodeRevolution #LessCodeMorePower #powerplatformdude #PowerPFBot #MVP #LowCode

The idea of Large Language Model operations (LLMOps) is revolutionizing AI, shaping a new era of innovation and productivity. Ahmet Gyger's latest article in ITOps Times outlines LLMOps' unique challenges and opportunities, uncovers strategies to optimize model development, deployment, and monitoring, and ways to stay ahead in the fast-evolving AI landscape. https://lnkd.in/gpJBzZT6 #AILeadership #LLMOps #MLOps #DataScience

Looking to streamline your ML workflows? Vertex AI is here to help! With features like AutoML, Custom Training, and Generative AI, you can transform your data into actionable insights. Click here for more https://buff.ly/4d90tsx #vertex #AI #machinelearning #autoML #googlecloud #innovation #miraclesoftware #blogs

The idea of Large Language Model operations (LLMOps) is revolutionizing AI, shaping a new era of innovation and productivity. Ahmet Gyger's latest article in ITOps Times outlines LLMOps' unique challenges and opportunities, uncovers strategies to optimize model development, deployment, and monitoring, and ways to stay ahead in the fast-evolving AI landscape. https://lnkd.in/egxK8k5n #AILeadership #LLMOps #MLOps #DataScience