London Fintech week is in full swing 🔝 Our CEO and Managing Partner, @DariaLeschenko, along with @labelyourdata CEO, @KarynaNaminas, are joining the event to engage with fintech leaders and gain more insights about the industry. #FTWLondon2023 #fintechevent2023

🚀 Building AI-ready pipelines starts with solid data extraction. From PDFs to APIs, schema drift to OCR noise – this stage defines model quality. We break it all down in our guide 👉 https://t.co/9qrJIlUx35 #DataExtraction #MachineLearning #DataLabeling #AI

Looking for top SuperAnnotate competitors that ML teams use in 2025? We studied and compared 10 leading alternatives for scale, pricing, and QA👉 https://t.co/zBgvBykwmZ #MachineLearning #AI #DataAnnotation

💡 RAG evaluation means tracking retrieval, generation & end-to-end performance – while balancing cost, latency & compliance. 👉Full guide: https://t.co/Izg6csApD0 #RAGEvaluation #LLM #AI

Dense cities, micro-polygons, multilingual tiles 🌍 Map labeling challenges can sink your ML model if ignored. Learn how to fix them 👉 https://t.co/pYsH6cEmxr #AI #Geospatial #DataAnnotation #ML

Worried about data drift breaking your ML models? 🔄 We break down what it is, how to detect it, and the best ways to manage it in production in our full guide 👉 https://t.co/tIQ3CcDzFc #MachineLearning #DataDrift #MLOps

Choosing between #LabelYourData and #SuperAnnotate for data labeling? 🧐We break down pricing, QA, support, and client reviews so ML teams can pick the right fit. Read the full vendor comparison 👉 https://t.co/PXJYvKBg0k

Human in the Loop Machine Learning: The Key to Better Models from @labelyourdata. Found on https%3A%2F% https://t.co/Z4J04K4tLR%2Farticles%2Fhuman-in-the-loop-in-machine-learning%2F

Outsourcing data labeling? ⚠️Vendor lock-in, quality risks, and budget overruns are real. Here’s what to check before signing any deal 👉 https://t.co/1fOi5gdR1E #MachineLearning #AI #DataLabeling #Outsourcing

⚡ An auto annotation tool can speed up your ML pipeline, but where does it fail, and which tools actually work? Read our full breakdown of top auto annotation tools and HITL strategies for 2025 →

Which image classification models are leading 2025? 🔎 From EfficientNetV2 to ViT & MobileNetV3, we review the top picks for ML pipelines. Full guide 👉 https://t.co/GP4mJJJNHO #MachineLearning #ComputerVision #DataAnnotation

📱Big model, small device?📉 Knowledge distillation helps you shrink LLMs without killing performance. Learn how teacher–student training works👉 https://t.co/rctfZgGJkA #MachineLearning #LLM #KnowledgeDistillation

🧠 Working on #multimodalAI? We compared top 10 multimodal #annotation companies for ML teams building models across text, images, audio, video, and 3D. Compare the vendors 👉 https://t.co/ILZXHCBVWv #Multimodal

Choosing the best data annotation company? We compared Label Your Data vs Scale AI for ML teams: accuracy, pricing, flexibility & more. 📝See which one fits your workflow: https://t.co/uplwkhRuPT #DataAnnotation #LabelYourData #ScaleAI

#SLM vs #LLM?🤔The decision isn’t about size alone. Accuracy, latency, and cost all shift with the workload. Our latest breakdown shows when smaller models can outperform giants in real-world ML pipelines: https://t.co/PRkMwE5qkz🚀

🚀 In 2025, scaling data labeling isn’t about adding annotators — it’s about automation, synthetic data, and QA-first workflows. 👉Read our full guide to AI data labeling: https://t.co/Y77S2MsvRZ #AIDataLabeling #AutoLabeling #AutoAnnotation

Intent classification fails fast without clean labels ⚠️ Which models and techniques actually scale? 📖 Read more in our new post → https://t.co/pi0tct9e4v #NLP #IntentClassification #ML

পৰিবৰ্তন অপৰিহাৰ্য। ...... #সাংবাদিক সকলেসঠিক......। অন্যথা প্ৰতিযোগিতাৰ পৰা আতৰিবলৈ বাধ্য হব। Artificial Intelligence in the New Media Industry: যুগান্তকাৰী পৰিবৰ্তনৰ দিশত প্ৰবেশ কৰা কৃত্ৰিম বুদ্ধিমত্ততাই (Artificial Intelligence) সৃষ্টিকৰা সংবাদ...👇 https://t.co/GOixZfizVX

Wondering which #Labelbox competitors stack up on pricing, tools, and real-world performance? Read the full comparison guide from our team 👉 https://t.co/U53Mpbpkyh #MachineLearning #AI #DataLabeling