Vivek Raghunathan’s Post

Let's learn about what is Voting architecture in ensemble learning 🤔 Voting ensemble is basically a type of ensemble learning architecture where we use different machine learning models as base models and provide same data to each of them during training.     Finally during the prediction stage the query point is provided as input to all base models and outputs from each of the model is finally aggregated and based on the type of problem we are solving like classification or regression we either do the majority count or calculate the average of all models.

TensorFlow Architecture: Three Essential Steps Master the TensorFlow Architecture in 3 key steps; Pre-process data efficiently, build a customized model, and achieve accurate results through effective training. Elevate your machine learning journey today! #xevensolutions #tensorflow #machinelearning #artificialintelligence

Why GenAI? Unprecedented productivity. Sounds a lot like Snowflake. Snowflake eliminates the need to manage your infrastructure, platform, storage and compute — activating your teams to activate your data. That impacts every workload. With Snowflake, “it just works” — from simple data sharing and data warehousing to innovative AI and ML. No more wasteful compute, storage and labor cost. Instead, elevated teams and skillsets. That’s Snowflake. Learn more about Snowflake Cortex AI, our serverless suite of LLMs and no-code AI development, in this outstanding hands-on workshop in July.

Back from Snowflake Build and again into the grind. #RAG for Complex PDFs by LlamaIndex, unstructured.io and AI Makerspace Why it’s interesting: - what we can get frlm PDFs is more tham just text - it’s also data (tables, charts, graphics) And the conjunction between unstructured and structured is the most juicy part of RAG architectures. A least for me. If you want to join me - here is the link: https://lnkd.in/dYKdCubP

Just completed this 44 hour Machine Learning Engineer track on DataCamp! Topics covered included Dev/MLOps, Continuous Integration and Delivery (CI/CD), Machine Learning Pipelines, and more!

TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. 🤖💡 Here is a list of a few alternatives to TensorFlow Serving #TensorFlowServing #MLDeployment #AIInProduction #ModelServing

📝📝Next LLM series of blogs under preparation: 💡Demystifying Layer Normalization in the Transformer Neural Network Architecture: With Code 💡LLMs and SQL 🏃🏃Coming Soon. Watch this space! #llms #transformers #sql

🚀 Exciting News! 🚀 Discover a powerful architecture for Linear Latent Models (LLMs) that simplifies model creation and implementation for everyday scenarios. 🌟 This architecture includes code for training LLMs using optimization techniques like gradient descent, enabling you to iterate over data, compute gradients, and minimize loss functions. 💡 Plus, find a sample script demonstrating how to use LLMs on a dataset, from loading data to evaluating performance. 📊 Ready to level up your machine learning skills? Check it out: https://lnkd.in/gepgFWFB #MachineLearning #DataScience #LLMs

Snowflake has quietly introduced Arctic, an open-source Large Language Model (LLM) under the Apache 2.0 license. Arctic boasts a unique Dense-MoE Hybrid transformer architecture. In enterprise metrics such as coding (HumanEval+ & MBPP+), SQL (Spider), and instruction following (IFEval), Arctic demonstrates performance on par with Llama3 70B. What's truly impressive is its claim of utilizing a mere 17 times less compute budget than Llama 3 70B. The training compute costs are estimated to be under $2 million, which equates to less than 3,000 GPU weeks. This development marks a significant milestone in the democratization of LLMs, accelerating at an unprecedented pace. #LLM #Ai

As AI becomes a top priority for organizations, they must prioritize the right strategy, skills, and data quality. Learn how to overcome integration complexity, data engineering challenges, and security concerns in building AI applications from this AI Cookbook. Discover six reference architectures with MongoDB Atlas and Vector Search to kickstart your journey towards AI-powered solutions. 👇 https://lnkd.in/gR76isgg