Best Artificial Intelligence Software for spaCy

Find and compare the best Artificial Intelligence software for spaCy in 2025

Use the comparison tool below to compare the top Artificial Intelligence software for spaCy on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    TensorFlow Reviews
    Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.
  • 2
    PyTorch Reviews
    TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
  • 3
    Datasaur Reviews

    Datasaur

    Datasaur

    $349/month
    One tool can manage your entire data labeling workflow. We invite you to discover the best way to manage your labeling staff, improve data quality, work 70% faster, and get organized!
  • 4
    Comet LLM Reviews
    CometLLM allows you to visualize and log your LLM chains and prompts. CometLLM can be used to identify effective prompting strategies, streamline troubleshooting and ensure reproducible workflows. Log your prompts, responses, variables, timestamps, duration, and metadata. Visualize your responses and prompts in the UI. Log your chain execution to the level you require. Visualize your chain in the UI. OpenAI chat models automatically track your prompts. Track and analyze feedback from users. Compare your prompts in the UI. Comet LLM Projects are designed to help you perform smart analysis of logged prompt engineering workflows. Each column header corresponds with a metadata attribute that was logged in the LLM Project, so the exact list can vary between projects.
  • 5
    Spark NLP Reviews

    Spark NLP

    John Snow Labs

    Free
    Spark NLP is an open-source library that provides scalable LLMs. The entire code base, including the pre-trained model and pipelines, is available under Apache 2.0 license. The only NLP library that is built natively on Apache Spark. The most widely used NLP Library in the enterprise. Spark ML offers a set machine learning applications which can be built with two main components: estimators and transformors. The estimators use a method to secure and train a piece of information for such an application. The transformer is usually the result of an fitting process that applies changes to the dataset. These components are embedded in Spark NLP. Pipelines combine multiple estimators and transformators into a single workflow. They allow for multiple transformations to be chained along a machine learning task.
  • 6
    Steamship Reviews
    Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.
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