Encord

Training Voice AI just got way faster! 🚀 With Encord’s agentic data workflow system and open source agent library, you can slash labeling time from hours to minutes for training Voice AI applications —without sacrificing accuracy. Teams developing production grade Voice AI apps for any use case such as customer service, patient monitoring or voice-authenticated security, need rich training datasets containing thousands of hours of audio with precise transcriptions, speaker identification, and sentiment analysis. This traditionally requires 4-5 hours of work per hour of audio, leading to major bottlenecks in AI development cycles. Watch the video below to follow along as Emil Munk demonstrates how integrating AI models for automating speaker diarization and sentiment analysis reduces processing time from hours to just 2-3 minutes per call, while maintaining 95% accuracy. Learn more about Encord Data Agents: https://lnkd.in/eQvGcqRx #VoiceAI #DataLabeling #AIWorkflows

Slash annotation time by 95% with Encord Data Agents! Join ML Solutions Engineer, Jennifer Ding, to learn how you can cut annotation times using SOTA models directly within Encord’s annotation interface. Teams building Smart City Infrastructure or Physical Intelligence models to monitor large environments have millions of images to curate and label before model training and fine-tuning. This can take months to get right, leading to long model iteration cycles and slower real world deployments. Using Data Agents, Jen has seamlessly integrated a YOLO model for lightning quick auto-detection of important objects like people and cars and an OCR model for enriching the city scenes with important context through text annotation. Learn more: https://lnkd.in/eQvGcqRx

📣 Announcing Encord Data Agents – enabling teams to build fully agentic data workflows that automate tasks across millions of multimodal files at unprecedented speed. Our platform redefines how teams handle image, video, text, and audio data at scale through automation of: - Multimodal pre-labeling (segmentation, tracking, transcription) - Intelligent content processing and routing - LLM-as-a-Judge evaluation Join our ML team on March 6th for an exclusive workshop on automating labeling tasks, fine-tuning models like R1, Grok 3, and Claude 3.7, and achieving 10x workflow throughput. Existing customers can start today with the Encord Data Agent Library. 🔗 Visit encord.com/data-agents to learn more! #AIData #MachineLearning #DataWorkflows

Don’t let manual data processes bottleneck your AI model iteration.    Join Oscar Evans and Frederik Hvilshøj on March 6th for an exclusive Data Agents workshop webinar to learn: - How to automate data labeling tasks on millions of multimodal files - How to fine-tune AI models like R1, o3, Grok 3 and more on your own data - How to 10x data labeling throughput and integrate HITL QA to prepare high-quality AI data   Register for the exclusive event now: https://lnkd.in/exUNkHJ2 

Join Encord’s ML team for a demo-packed webinar where we’ll showcase how to integrate top foundation models (R1, O3, Gemini 2, Grok 3) and custom Hugging Face models to automate manual data tasks at scale. Don’t miss it—register now! 

AI After Hours is back! Join us next Tuesday, Feb 25 at GitHub HQ to connect with AI practitioners, learn from experts, and grab a slice of pizza! Talks: ⭐ Three Paradigm Shifts in AI – Robert Nishihara (Anyscale) How scaling, unsupervised learning, and data have reshaped AI—and what’s next. ⭐ Building an Agentic Data Engine – Frederik Hvilshøj (Encord) How AI-powered data engines enhance automation and decision-making. ⭐ Scaling Robot Data for Industrial AI – Vishal Satish (Ambi Robotics) Training foundation models with 200k+ hours of production data for real-world reliability. Register for the event here: https://lu.ma/vcl9aokp

AI isn’t just about decisions—it’s about learning useful representations. Kevin Chavez from Dexterity, Inc. explains how state-action encoding helps robots optimize packing and unpacking. Watch a clip from his talk from Encord’s AI After Hours on Physical AI below. Curious for more? Find the full talk on our YouTube.

With 86% of human understanding coming from vision, leveraging AI for video analysis leads to powerful possibilities. Spot AI CEO Rish Gupta explains how rapid advancements in edge computing and AI models led to a game-changing insight: video is the richest source of data. Check out this clip from his recent talk at Encord's Physical AI edition of AI After Hours & view the complete talk on our YouTube channel.

Thanks to Matthew Pearce from Pickle Robot Company for joining us on our webinar yesterday! Matt shared that at Pickle Robot, the ML team’s goal is to turn new data into better models—fast. That means building ML pipelines that just work, without constantly revisiting them. A key part of making that happen? Reliable data labeling. Using Encord, Pickle’s ML team can: - Quickly iterate on labeling ontologies - Reuse datasets for multiple tasks - Seamlessly compose and experiment with new models Watch the full video on our YouTube - link in the comments ⬇️ #MachineLearning #ComputerVision #PhysicalAI