Data Science and Analytics

   ​ How to productionize LLMs: from data prep and prompt design to evaluation, privacy, and future trends like in-context learning.​ ​ Dean Pleban and Liron Itzhakhi Allerhand explore what it really takes to move LLMs into production. They cover how to define clear requirements, prep data for RAG, engineer effective prompts, and evaluate model performance …

👏 A Practical Approach to Building LLM Applications with Liron Itzhaki Allerhand Read More »

   ​ ImprovementsSince moving to DagsHub, Beewise has seen major improvements:Model iteration time dropped from 2–3 weeks to just 3–5 days.Debugging time went from days to just a few hours.Annotation mistakes decreased by ~30%.Active learning management: 1 annotator now handles 100,000+ images​ ​ Improvements Since moving to DagsHub, Beewise has seen major improvements: Model iteration time …

Accelerating CV workflows by 73% with data management and versioning tools Read More »

DagsHub is now integrated with Red Hat OpenShift and OpenShift AI, providing a complete AI development and deployment workflow.​ TL;DR: Enterprises need to deploy AI securely while handling complex, unstructured data. DagsHub’s integration with Red Hat OpenShift and OpenShift AI provides an end-to-end, on-premise solution for dataset management, experiment tracking, and model deployment—helping teams build …

Bringing AI to Production with DagsHub and Red Hat OpenShift Read More »

This article will make you understand what text data labeling is, its importance in machine learning, and the different techniques used to annotate text efficiently. You will explore manual, automated, and semi-automated labeling approaches, along with modern strategies like active learning and weak supervision. Additionally, you will learn about the​ This article will make you …

Top Advanced Text Data Labeling: A Comprehensive Guide Read More »

Learn how duplicate data affects machine learning models and uncover strategies to identify, analyze, and manage duplicate data effectively.​ In today’s data-driven world, machine learning practitioners often face a critical yet underappreciated challenge: duplicate data management. A massive amount of diverse data powers today’s ML models. Though gathering massive datasets has become easier than ever, …

Mastering Duplicate Data Management in Machine Learning for Optimal Model Performance Read More »

   ​ Learn about the essential best practices for image labeling that can help you improve your computer vision model accuracy.​ ​ Image labeling or image annotation is the cornerstone of computer vision. It is the process of assigning meaningful labels or annotations to image data to enable computer vision models to learn patterns and make …

Essential Best Practices for Image Labeling: A Complete Guide for Model Accuracy Read More »

   ​ Learn how semantic segmentation and deep learning can transform unstructured documents into actionable data. Explore techniques, industry applications, and best practices for document segmentation | DagsHub​ ​[[{“value”:” Every day, businesses manage an extensive volume of documents—contracts, invoices, reports, and correspondence. Critical data, often in unstructured formats that can be challenging to extract, is embedded …

A Guide to Semantic Segmentation for Documents Read More »

   ​ Traditional approaches might suggest randomly selecting images for annotation, but this can be inefficient and wasteful. Active Learning takes a more strategic approach: it identifies and prioritizes the most valuable samples that could contribute most significantly to improving model performance​ ​[[{“value”:” Traditional approaches might suggest randomly selecting images for annotation, but this can be …

How Active Learning Can Improve Your Computer Vision Pipeline Read More »

   ​ Discover the most popular methods for evaluating classification models and some best practices for working with classifiers.​ ​[[{“value”:” A classification model or a classifier is a type of machine learning algorithm that assigns categories or labels to data points. For example, a model could analyze an email and determine whether it classifies as spam. …

Evaluating Classification Models: Metrics, Techniques, and Best Practices Read More »

   ​ Dean and Natanel discuss AI and ML topics like model efficiency, data quality, APIs vs. self-hosting, and success metrics. They explore data scientists’ evolving role with LLMs, collaboration with product teams, and the future of robotics in AI.​ ​ In this episode, Dean and Natanel Davidovits explore the intricacies of AI and machine learning, …

📡 Building Scalable ML Models with Natanel Davidovits Read More »