• Private Data Anonymization for LLM prompting

    Private Data Anonymization for LLM prompting

    Preserving data confidentiality has become an increasingly challenging task in the era of Large Language Models (LLMs), such as ChatGPT or Bard, which have ushered in a new era of Natural Language Understanding and Generation, making it possible to extract valuable insights from vast amounts of textual data. However, this […]

  • [RO] Soluții cu Inteligență Artificială pentru Procesare de Limbaj Natural

    [RO] Soluții cu Inteligență Artificială pentru Procesare de Limbaj Natural

    Zetta Cloud este o companie românească specializată în dezvoltarea de soluții software cu Inteligență Artificială (AI) pentru Procesarea de Limbaj Natural (NLP). Produsele software ale companiei utilizează algoritmi de Învățare Automată (Machine Learning) pentru procesarea limbajului vorbit (voce) și scris (documente sau pagini web) pentru peste 50 de limbi, inclusiv […]

  • ML adaptation for RNN and LLM models in AI Factory 2.0

    ML adaptation for RNN and LLM models in AI Factory 2.0

    The AI Factory no-code Machine Learning dashboard offers training capabilities for Classification and Named Entity Recognition. Each option relies on different algorithms: The reason why you would use either the RNN Classifier or the Transformers-based (XT) Classifier has much to do with the amount of training data you have and […]

  • AI Factory 2.0: Accelerated productization of Text Analytics engines with no-code Machine Learning

    AI Factory 2.0: Accelerated productization of Text Analytics engines with no-code Machine Learning

    Zetta Cloud is announcing a major release of its flagship “no code” Machine Learning platform AI Factory, version 2.0. The new release aims at bridging the gap from data to production for Text Analytics AI engines, with unprecedented ease of use designed for both machine learning professionals and non-technical business […]

  • Multi-class vs. Multi-label Classification

    Multi-class vs. Multi-label Classification

    The most common AI Text Analytics task is Classification. Document classification works for a plethora of use- cases, from sentiment/emotion analysis, intent detection, news categorization, email classification, and so on. There are few business-related processes that do not require a classifier in order to be automated, and there are so […]

  • It’s AutoMagical! How does the AI Factory hyperparameter optimization work

    It’s AutoMagical! How does the AI Factory hyperparameter optimization work

    A new and exciting feature was added to the AI Factory version 2.0 release: The AutoMagical Settings switch for the Classifier trainers. With just a click, Factory will automatically (and magically!) determine the best hyperparameters for the training process to obtain the best AI classifier from your data. Read the […]

  • Decentralizing AI: From GPU Challenges to No-Code Machine Learning

    Decentralizing AI: From GPU Challenges to No-Code Machine Learning

    Just by looking at the hype happening in all media and driven by the fast pace of changes in the AI industry, one could be tempted to think that everything’s coming up roses, with generative AI LLMs being the panacea for all technological challenges. Emil Ștețco, founder and CEO of […]

  • How to build high-quality Classifiers with limited training data

    How to build high-quality Classifiers with limited training data

    Automate Classifiers are the bread and butter of automated content understanding. They help categorize documents against any desired taxonomy, from IPTC Newscodes to sentiment, intent, stance, or emotion. Building your own classifier with Factory no-code AI is as easy as it gets (and secure, too!), but what happens if you […]