Why MLOps World? This initiative is created to help establish a clearer understanding of the best practices, methodologies, principles and lessons around deploying machine learning models into production environments.
Throughout weekly sessions you’ll have an opportunity to meet specialists, and form a stronger network with practitioners sharing lessons, and real world use-cases
Join us on this open exploration as we gather to cover conference proceedings, hands-on workshops, tooling & open source demos, a career exploration and more, here: (MLOpsWorld.com)
15:40 I'd add here a Task which is more 'main' than any other task. QA must understand what they do and why, they must understand business domain itself. Thank you for the video.
Hello Kartik/RU-vid Handler, I have just joined a company as a Machine Learning Engineer Intern and still a fresher. I would like to keep my Name and where I work anonymous for this specific platform. I am working on a task where I need to analyse the dataset I have been given and convert that data into text using LLM. Example Data: Date Temperature 2 Feb 30C 3 Feb 24C Example Output: Today's weather will be warmer than yesterday and a little pleasant.... <so on> The use case is a little different but this is just an example to explain what I actually want. A little more explanation: What I want is that the LLM to read the dataset completely either through an excel I have or any format like CSV and answer my queries or create a conclusion based on the dataset I gave. I would love to get some help/insights from someone as experienced as you on how I can achieve my goal. We can connect on some other platform if you are comfortable with it. You can contact me at me personal mail: rohitkhare998@gmail.com Thanks. regards, Novice ML Engineer
Awesome talk. I am preparing for a privacy preserving ML interview and this was an amazing crash course. Second, for the thermal flu issue you mentioned, can't we just use FHE or SMPC like you mentioned in the slides?
Great talk! As suggested, we do see now more "small" LLMs trained with considerably larger amounts of tokens than the "compute-optimal” recommended by the Chinchilla scaling laws
Thanks for the very good overview of training distributed systems on kubernetes, would love to see more detailed information making all the pieces fit together !
These are amazing presentations but the slides are a bit blurry on all the videos on your channel, would be great if you could fix that in the future. Thank you.
