5 free Codelabs for Google's #VertexAI: 1. Intro codelabs.developers.google.com/vertex-pipelin… 2. Build & Deploy Models codelabs.developers.google.com/codelabs/verte… 3. Fraud Detection with AutoML codelabs.developers.google.com/vertex-automl-… 4. Train & Serve Custom Models codelabs.developers.google.com/vertex_custom_… 5. Using Metadata codelabs.developers.google.com/vertex-mlmd-pi…

You most likely are using Siri, Alexa, or Google Assistant. Here is 1-page overview of Voice Assistants. With Slang Labs SDK, developers can easily map app's workflows to ready-to-use Slang Voice User Journeys, without requiring NLP or ASR expertise: docs.slanglabs.in

After 2 years, Practical Deep Learning for Coders v5 is finally ready! 🎊 This is a from-scratch rewrite of our most popular course. It has a focus on interactive explorations, & covers PyTorch, Hugging Face, DeBERTa, ConvNeXt, Gradio & other goodies 🧵 course.fast.ai

DATA... This 4-letter word is driving the today's world. Managing its pipelining efficiently determines the success or failure of your tech. If you would like to know more about Big Data Pipelines then join us along with Satish Chandra Gupta for DevFest'22 #DevFest22 #devfestbengaluru

MLOps: Machine Learning Lifecycle by Satish Chandra Gupta buff.ly/3RSoWrw

Here's the Agenda for Day two of DevFest'22 If you would love to hear about #Cloud #machinelearning and #appscript then join us! 🥳 #devfest #devfest22 #devfestbengaluru

Building machine learning products or ML-assisted product features involve two distinct disciplines: model development and model deployment. Satish Chandra Gupta expands on the machine learning lifecycle here. buff.ly/3dRSkzU

1/2 Interesting blog post by Satish Chandra Gupta detailing the cloud deployment landscape.

2/2 Link: ml4devs.com/articles/serve…

This is insane and unthinkable!

Principled Instructions for LLMs Nice set of guiding principles to improve and enhance the quality and reliability of LLM responses. Shows the effectiveness of the principled instructions and prompt designs across model sizes and scenarios. Tested on Llama 1/2 (7B, 13B, and

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM on its own directly generated responses to prompts doesn't help. But if an agentic workflow implemented