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10 most in-demand generative AI skills

CIO

TensorFlow Developed by Google as an open-source machine learning framework, TensorFlow is most used to build and train machine learning models and neural networks. Lauded features include dynamic computation graphics, a Python foundation, and automatic differentiation for creating and training deep neural networks.

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“Zen” and “Fresh” Way for Technical Support

Xoriant

My previous blog post highlighted the role of technical support in software development. This blog post is to compare two CRM tools that will help you in achieving your goal of providing great technical support experience. A good CRM tool is necessary today for providing efficient technical support.

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Step Into the Cloud-based Workspace Management of the Future

Ivanti

We can make it easier for you to switch to a new solution by supplying a fit-for-purpose licensing structure and technical support through our professional service department and our partner network.

Cloud 95
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Exadel Launches ML1 — Ticket Automation Software for Jira

Exadel

How ML1 Works: From Training a Model to Predicting Field Values. Its accuracy is based on automatically-performed model training. ML1 sends historical data from Jira tickets to an ML server that retrieves summaries and descriptions which are used for the training process. Previous article.

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Legal NLP Releases Law Stack Exchange Classifier, Subpoena NER and more

John Snow Labs

Law Stack Exchange Classifier This new model is a text classifier trained on the Law Stack Exchange dataset. Subpoena NER This model is trained on an in-house dataset to identify entities in Subpoena documents. We’ve got 30-days free licenses for you with technical support from our legal team of technical and SME.

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Bringing fresh capabilities for Legal research and applications with Legal NLP

John Snow Labs

To support the utilization, training, and fine-tuning of models for the legal domain, Legal NLP is introducing new models: word embedding models, which generate vector representations of words (or tokens), and sentence embedding models, which create vector representations for longer pieces of text such as sentences, paragraphs, and documents.

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Better embeddings and better models in Finance NLP

John Snow Labs

To provide the capabilities to use, train, and finetune models for Finance, Finance NLP is releasing two categories of embedding models: word embedding models that create vector representations of words (or tokens), and sentence embedding models that create vector representations of longer chunks of text (e.g., NER, Relation Extraction).