Data Science and Analytics

DALL-E 3: Art Generator Powered by ChatGPT

   ​ OpenAI officially announced DALL·E 3, a modern text-to-image system built natively on ChatGPT to generate artwork by simply talking to the chatbot.​ ​ OpenAI officially announced DALL·E 3, a modern text-to-image system built natively on ChatGPT to generate artwork by simply talking to the chatbot. Generated with DALL-E 3: “A vibrant yellow banana-shaped couch …

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3 Lines of Code: Notebook to Production-Ready Machine Learning Project

   ​ Every machine learning project starts simple – that’s a good thing. But at some point you need to promote it to production-grade. With 3 lines of code, you can get from a notebook to a central source of truth with code, data, models, and experiments which you can share with team members.​ ​ TL;DR …

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The Official Machine Learning and AI Platform of Hacktoberfest 2023

   ​ Contribute to Machine Learning and AI projects during Hacktoberfest 2023​ ​ We’re excited to share that DagsHub is partnering with Hacktoberfest to celebrate its 10th anniversary! Join us for a month-long celebration of open-source contributions to Machine Learning and AI projects. Gain hands-on experience building datasets, models, pipelines, and more! From non-code contributors to …

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How to Build CI/CD Pipeline for Continuous Training with SageMaker

   ​ Learn how to create a simple CI/CD training pipeline for your Machine Learning project using AWS SageMaker and DagsHub​ ​ Training models is a time-consuming task, often requiring multiple iterations. While building the algorithms is challenging on its own, setting up the training process requires further understanding of MLOps which is out of scope …

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🔴 Building, Deploying and Monitoring Large Language Models with Jinen Setpal

   ​ I speak with Jinen Setpal, ML Engineer at DagsHub about actually building, deploying, and monitoring large language model applications. We dive into evaluation methods, ways to reduce hallucinations and much more. We also answer the audience’s great questions.​ ​ In this live episode, I’m speaking with Jinen Setpal, ML Engineer at DagsHub about actually …

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How to Build CI/CD Pipeline for Continuous Deployment with SageMaker

   ​ Learn how to create a simple CI/CD deployment pipeline for your Machine Learning project using AWS SageMaker and DagsHub​ ​ Most Machine Learning models are dynamic. They continuously learn and enhance their performance with additional data. This requires us to constantly update the deployed model. As we know, deployment is a repetitive process. And …

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LLMOps: Experiment Tracking with Weights & Biases for Large Language Models

   ​ We will check how Weights & Biases log prompts, document the model architecture, and effectively record versioned artifacts.​ ​ In the previous article, we explored MLflow‘s support for experiment tracking of LLM applications through logging of prompts and their outputs. Another widely used tool for tracking experiments is Weights & Biases (W&B or WandB). …

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Google’s “Gemini” is 5 Times Stronger than “GPT-4”

   ​ Learn about Google’s latest flagship model, “Gemini,” which emerges as a direct competitor to GPT-4, with 5 times the computing resources used for training and multimodal capabilities.​ ​ Google’s latest flagship model, codenamed “Gemini,” boasts an astonishing level of power that surpasses GPT-4 by a factor of five and is able to produce text …

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Tutorial: How to Setup SageMaker for Machine Learning CI/CD Pipelines

   ​ Learn how to setup AWS SageMaker environment to create and run custom CI/CD pipelines for Machine Learning​ ​ With the advent of “bigger and badder” machine learning models and their usage in production, it has become necessary to orchestrate the entire MLOps process. This process is often time-consuming, repetitive, and resource-dependent. The issue is …

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