Denise R. Halyama, CLP’s Post

Enter a new era of productivity with #GenerativeAI capabilities built for business. Start innovating with your choice of open source or proprietary LLMs today!

It may be difficult to conceive of at the moment but barring something unexpected happening, AI will eventually become a ubiquitous software layer. There won’t be an amalgamation of different SaaS solutions, there will be amalgamations of models and techniques (think reasoning models like o1 Strawberry + LLMs like GPT-4o + RAGs). The resulting model will be capable of doing anything software does today, either on the fly if it’s something not already in user patterns or previously requested, or for those functions that have already been developed by the model, they’ll be retrieved via RAG. A good way Daniel Rothrock, PE and I like to refer to this in conversation is as a “database of programs” (a term we didn’t coin). Similarly to this, the world’s hardware, humanoid or not, will operate similarly. It’s an inevitability and one worth spending some time thinking about.

Velocity is using multimodal LLMs, like OpenAI’s GPT-4o and others, to enhance data processing, expedite software development, and drive productivity. At the forefront of this shift is Raghu Punnamraju, Velocity’s Chief Technology Officer. In this article he shares his thoughts on the rapid evolution of LLMs, and how quickly new tools are being applied to improve quality and efficiency. Read the article: https://lnkd.in/gfS7MCXZ #AI #LLM #ClinicalResearch #ClinicalTrials

Unlock the Power of MLOps: Transforming Machine Learning into Scalable Operations Check out our latest blogpost on MLOPS. https://lnkd.in/g8D72aCk

Unlock the Power of MLOps: Transforming Machine Learning into Scalable Operations Check out our latest blogpost on MLOPS. https://lnkd.in/gxVStjCB

Getting low level infrastructure control of deep learning systems lets you run faster, more efficient work AI workloads. And also make memes- UwU. Here's to $0 Operational Expense on software licensing going forward.

AI, they say. ML, they say. It's en vogue, but also a new software reality. But one more chat bot or virtual assistant won't impress anyone. You need to actually drive responsible AI by securing and delivering machine learning components as part of your software supply chain. MLOps isn't about just managing models, but your entire software ecosystem to deliver value securely, with speed. https://jfrog.com/mlops/

🚀 New Article Alert! 🚀 I’m excited to share my latest Medium article where I dive into Ray Serving with Model Multiplexing. From handling diverse models to optimizing performance and resource utilization, Ray Serving offers powerful capabilities for MLOps enthusiasts and practitioners. This piece explores the potential of model multiplexing to simplify deployment and drive efficiency in machine learning workflows. Check it out, and let’s discuss how Ray can scale your ML models in production! #MlOps #Serving #Machinelearning

OpenAI has made Custom GPTs free for anyone. You no longer need to have a Chat GPT premium subscription. 💖 🥂 This means anyone with a technology stock idea that wants to get a comprehensive analysis of their idea, based on a proven investing methodology, can use my Custom GPT "Tech Stock Analyst" for free 😃 Take it for a spin with your stock ideas https://lnkd.in/g2CD6_KD Just remember it's still AI and NOT financial advice. An input for you to consider in your financial decisions.