Best Artificial Intelligence Software for Python - Page 13

Compare the Top Artificial Intelligence Software that integrates with Python as of June 2025 - Page 13

This a list of Artificial Intelligence software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.

  • 1
    Avanzai

    Avanzai

    Avanzai

    Avanzai helps accelerate your financial data analysis by letting you use natural language to output production-ready Python code. Avanzai speeds up financial data analysis for both beginners and experts using plain English. Plot times series data, equity index members, and even stock performance data using natural prompts. Skip the boring parts of financial analysis by leveraging AI to generate code with relevant Python packages already installed. Further edit the code if you wish, once you're ready copy and paste the code into your local environment and get straight to business. Leverage commonly used Python packages for quant analysis such as Pandas, Numpy, etc using plain English. Take financial analysis to the next level, quickly pull fundamental data and calculate the performance of nearly all US stocks. Enhance your investment decisions with accurate and up-to-date information. Avanzai empowers you to write the same Python code that quants use to analyze complex financial data.
  • 2
    Amazon SageMaker Model Building
    Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
  • 3
    Amazon SageMaker Studio Lab
    Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately.
  • 4
    Gradio

    Gradio

    Gradio

    Build & Share Delightful Machine Learning Apps. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share.
  • 5
    MosaicML

    MosaicML

    MosaicML

    Train and serve large AI models at scale with a single command. Point to your S3 bucket and go. We handle the rest, orchestration, efficiency, node failures, and infrastructure. Simple and scalable. MosaicML enables you to easily train and deploy large AI models on your data, in your secure environment. Stay on the cutting edge with our latest recipes, techniques, and foundation models. Developed and rigorously tested by our research team. With a few simple steps, deploy inside your private cloud. Your data and models never leave your firewalls. Start in one cloud, and continue on another, without skipping a beat. Own the model that's trained on your own data. Introspect and better explain the model decisions. Filter the content and data based on your business needs. Seamlessly integrate with your existing data pipelines, experiment trackers, and other tools. We are fully interoperable, cloud-agnostic, and enterprise proved.
  • 6
    Quadratic

    Quadratic

    Quadratic

    Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. Raw values, 1/2D arrays, and Pandas DataFrames are supported by default. Pull or fetch data from an external API, and it updates automatically in Quadratic's cells. Navigate with ease, zoom out for the big picture, and zoom in to focus on the details. Arrange and navigate your data how it makes sense in your head, not how a tool forces you to do it.
  • 7
    UnionML

    UnionML

    Union

    Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. ‍ Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior.
  • 8
    Dify

    Dify

    Dify

    Dify is an open-source platform designed to streamline the development and operation of generative AI applications. It offers a comprehensive suite of tools, including an intuitive orchestration studio for visual workflow design, a Prompt IDE for prompt testing and refinement, and enterprise-level LLMOps capabilities for monitoring and optimizing large language models. Dify supports integration with various LLMs, such as OpenAI's GPT series and open-source models like Llama, providing flexibility for developers to select models that best fit their needs. Additionally, its Backend-as-a-Service (BaaS) features enable seamless incorporation of AI functionalities into existing enterprise systems, facilitating the creation of AI-powered chatbots, document summarization tools, and virtual assistants.
  • 9
    Granica

    Granica

    Granica

    The Granica AI efficiency platform reduces the cost to store and access data while preserving its privacy to unlock it for training. Granica is developer-first, petabyte-scale, and AWS/GCP-native. Granica makes AI pipelines more efficient, privacy-preserving, and more performant. Efficiency is a new layer in the AI stack. Byte-granular data reduction uses novel compression algorithms, cutting costs to store and transfer objects in Amazon S3 and Google Cloud Storage by up to 80% and API costs by up to 90%. Estimate in 30 mins in your cloud environment, on a read-only sample of your S3/GCS data. No need for budget allocation or total cost of ownership analysis. Granica deploys into your environment and VPC, respecting all of your security policies. Granica supports a wide range of data types for AI/ML/analytics, with lossy and fully lossless compression variants. Detect and protect sensitive data even before it is persisted into your cloud object store.
  • 10
    Wherobots

    Wherobots

    Wherobots

    Wherobots enables users to easily develop, test, and deploy geospatial data analytics and AI pipelines within the user's existing data stack. That can be deployed in the cloud. Users do not have to worry about the hassle of resource administration, workload scalability, and geospatial processing support/optimization. Connect your Wherobots account to the cloud database where the data is stored using our SaaS web interface. Develop your geospatial data science, machine learning, or analytics application using Sedona Developer Tool. Schedule automatic deployment of your geospatial pipeline to the cloud data platform and monitor the performance in Wherobots. Consume the outcome of your geospatial analytics task. The consumption model can be through a single geospatial map visualization or API calls.
  • 11
    Editor.do

    Editor.do

    Editor.do

    Editor.do is an all-in-one online IDE and hosting platform that allows you to create, code, host and deploy stunning & fast static websites in seconds. You can easily deploy your site files or a zip containing all your project files to our NVMe SSD storage servers, ensuring the fastest possible loading speed for your site. Our IDE supports over 150 programming languages with real-time code rendering and a panel of shortcuts and tools to search, replace, cut, select, and quickly manipulate your code. Editor.do offers over 1000 free and open-source templates covering a wide range of categories and libraries that can be imported directly from GitHub. Plus, ChatGPT is integrated and is always close at hand to help you correct, complete, or improve your code or text. Editor.do is an ideal platform for developers and designers of all skill levels who want to create stunning, fast, and secure websites in a fraction of the time.
    Starting Price: $3 per month
  • 12
    Sweep AI

    Sweep AI

    Sweep AI

    Spend time reviewing code generated by AI, not writing it. Sweep generates repository-level code at your command. Cut down your dev time on mundane tasks, like tests, documentation, and refactoring. Review all changes by Sweep, directly in Github, and comment if any changes need to be made. Push the commit if all looks good. All you have to do is write a ticket, and Sweep will do all of the heavy-lifting for you, allowing you to focus on the more important engineering problems.
  • 13
    Parsagon

    Parsagon

    Parsagon

    Tell Parsagon what you want to do - our AI will write code to do it.
  • 14
    Monster API

    Monster API

    Monster API

    Effortlessly access powerful generative AI models with our auto-scaling APIs, zero management required. Generative AI models like stable diffusion, pix2pix and dreambooth are now an API call away. Build applications on top of such generative AI models using our scalable rest APIs which integrate seamlessly and come at a fraction of the cost of other alternatives. Seamless integrations with your existing systems, without the need for extensive development. Easily integrate our APIs into your workflow with support for stacks like CURL, Python, Node.js and PHP. We access the unused computing power of millions of decentralised crypto mining rigs worldwide and optimize them for machine learning and package them with popular generative AI models like Stable Diffusion. By harnessing these decentralized resources, we can provide you with a scalable, globally accessible, and, most importantly, affordable platform for Generative AI delivered through seamlessly integrable APIs.
  • 15
    dstack

    dstack

    dstack

    It streamlines development and deployment, reduces cloud costs, and frees users from vendor lock-in. Configure the hardware resources, such as GPU, and memory, and specify your preference for using spot instances. dstack automatically provisions cloud resources, fetches your code, and forwards ports for secure access. Access the cloud dev environment conveniently using your local desktop IDE. Configure the hardware resources you need (GPU, memory, etc.) and indicate whether you want to use spot or on-demand instances. dstack will automatically provision cloud resources and forward ports for secure and convenient access. Pre-train and finetune your own state-of-the-art models easily and cost-effectively in any cloud. Have cloud resources automatically provisioned based on your configuration? Access your data and store output artifacts using declarative configuration or the Python SDK.
  • 16
    Vaex

    Vaex

    Vaex

    At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine.
  • 17
    AskYourDatabase

    AskYourDatabase

    AskYourDatabase

    Forget complex SQL or Python scripts, connect your database, and start chatting with your data. This plugin is a ChatGPT demo for further development. It only supports PostgreSQL, but you can copy and use it as a starting point for developing your own plugin. After you install it, you can try it with your PostgreSQL database, if you don't have one, you can try Vercel Postgres and get a free Postgres DB.
  • 18
    Ever Efficient AI

    Ever Efficient AI

    Ever Efficient AI

    Ever Efficient AI offers a powerful yet accessible AI automation platform helping businesses maximize efficiency - without hiring a full-time AI engineer on the team. Their flexible subscription plans connect you with AI experts who build customized data solutions tailored to your specific workflows and industry. No complex modeling or coding is required on your end. The collaborative process lets those with domain expertise guide tool development. EverEfficientAI's team manages the technical complexity behind the scenes, converting your datasets and processes into optimized AI systems. Bi-weekly agile sprints adapt the tools over time while providing transparency. With a focus on usability and rapid integration, EverEfficientAI makes advanced AI easily actionable by any organization. The future of work is here - are you ready to let your data work smarter for you?
    Starting Price: $3,497 per month
  • 19
    StableCode

    StableCode

    Stability AI

    StableCode offers a unique way for developers to become more efficient by using three different models to help in their coding. The base model was first trained on a diverse set of programming languages from the stack-dataset (v1.2) from BigCode and then trained further with popular languages like Python, Go, Java, Javascript, C, markdown and C++. In total, we trained our models on 560B tokens of code on our HPC cluster. After the base model had been established, the instruction model was then tuned for specific use cases to help solve complex programming tasks. ~120,000 code instruction/response pairs in Alpaca format were trained on the base model to achieve this result. StableCode is the ideal building block for those wanting to learn more about coding, and the long-context window model is the perfect assistant to ensure single and multiple-line autocomplete suggestions are available for the user. This model is built to handle a lot more code at once.
  • 20
    IBM watsonx Code Assistant
    Enable hybrid cloud developers of all experience levels to write code with AI-generated recommendations. What if you could translate plain English to code? IBM watsonx Code Assistant allows you to do just that. Powered by IBM watsonx.ai foundation models (FM), IBM watsonx Code Assistant makes it easier for anyone to write code with AI-generated recommendations, bringing the power of IT automation to your entire organization as a strategic, accessible asset for more users—not just the subject-matter experts. This means automatically suggesting code for developers based on natural language inputs. IBM watsonx Code Assistant is infused with watsonx.ai FMs that are purpose-built, created with deployment efficiency in mind, and which enable organizations to customize the models, while also applying enterprise standards and best practices.
  • 21
    NVIDIA Holoscan
    NVIDIA® Holoscan is a domain-agnostic AI computing platform that delivers the accelerated, full-stack infrastructure required for scalable, software-defined, and real-time processing of streaming data running at the edge or in the cloud. Holoscan supports a camera serial interface and front-end sensors for video capture, ultrasound research, data acquisition, and connection to legacy medical devices. Use the NVIDIA Holoscan SDK’s data transfer latency tool to measure complete, end-to-end latency for video processing applications. Access AI reference pipelines for radar, high-energy light sources, endoscopy, ultrasound, and other streaming video applications. NVIDIA Holoscan includes optimized libraries for network connectivity, data processing, and AI, as well as examples to create and run low-latency data-streaming applications using either C++, Python, or Graph Composer.
  • 22
    Yamak.ai

    Yamak.ai

    Yamak.ai

    Train and deploy GPT models for any use case with the first no-code AI platform for businesses. Our prompt experts are here to help you. If you're looking to fine-tune open source models with your own data, our cost-effective tools are specifically designed for the same. Securely deploy your own open source model across multiple clouds without the need to rely on third-party vendors for your valuable data. Our team of experts will deliver the perfect app tailored to your specific requirements. Our tool enables you to effortlessly monitor your usage and reduce costs. Partner with us and let our expert team address your pain points effectively. Efficiently classify your customer calls and automate your company’s customer service with ease. Our advanced solution empowers you to streamline customer interactions and enhance service delivery. Build a robust system that detects fraud and anomalies in your data based on previously flagged data points.
  • 23
    Kolena

    Kolena

    Kolena

    We’ve included some common examples, but the list is far from exhaustive. Our solution engineering team will work with you to customize Kolena for your workflows and your business metrics. Aggregate metrics don't tell the full story — unexpected model behavior in production is the norm. Current testing processes are manual, error-prone, and unrepeatable. Models are evaluated on arbitrary statistical metrics that align imperfectly with product objectives. ‍ Tracking model improvement over time as the data evolves is difficult and techniques sufficient in a research environment don't meet the demands of production.
  • 24
    Ntropy

    Ntropy

    Ntropy

    Ship faster integrating with our Python SDK or Rest API in minutes. No prior setups or data formatting. You can get going straight away as soon as you have incoming data and your first customers. We have built and fine-tuned custom language models to recognize entities, automatically crawl the web in real-time and pick the best match, as well as assign labels with superhuman accuracy in a fraction of the time. Everybody has a data enrichment model that is trying to be good at one thing, US or Europe, business or consumer. These models are poor at generalizing and are not capable of human-level output. With us, you can leverage the power of the world's largest and most performant models embedded in your products, at a fraction of cost and time.
  • 25
    Cosine Genie
    Whether it’s high-level or nuanced, Cosine can understand and provide superhuman level answers. We're not just an LLM wrapper – we combine multiple heuristics including static analysis, semantic search and others. Simply ask Cosine how to add a new feature or modify existing code and we’ll generate a step by step guide. Cosine indexes and understands your codebase on multiple levels. From a graph relationship between files and functions to a deep semantic understanding of the code, Cosine can answer any question you have about your codebase. Genie is the best AI software engineer in the world by far - achieving a 30% eval score on the industry standard benchmark SWE-Bench. Genie is able to solve bugs, build features, refactor code, and everything in between either fully autonomously or paired with the user, like working with a colleague, not just a copilot.
  • 26
    Definitive

    Definitive

    Definitive

    A one-shot prompt-to-visualization API that seamlessly integrates with enterprise & public data, enabling users to instantly & accurately retrieve visually rich answers to their questions. Enables enterprises to engage in dynamic conversations with their own data, fostering efficient collaboration and informed decision-making. Supports Python code generation and joining of disparate data sets. An autonomous data science agent, providing comprehensive support in data analysis, predictive modeling, and advanced analytics. Create the enterprise AI sidekick experience that works best for your organization. Public LLMs are not currently trained on an enterprise's unique, proprietary data sets. Your sidekick unlocks workplace productivity. A more engaging interface for complex analysis is now accessible to all members of the organization, regardless of technical ability. Through API-level access, your sidekick integrates with your existing products, systems, and workflows.
  • 27
    Code to Flowchart

    Code to Flowchart

    Code to Flowchart

    Turn code into interactive flowcharts with AI, and simplify complex logic instantly.
    Starting Price: Free
  • 28
    Second State

    Second State

    Second State

    Fast, lightweight, portable, rust-powered, and OpenAI compatible. We work with cloud providers, especially edge cloud/CDN compute providers, to support microservices for web apps. Use cases include AI inference, database access, CRM, ecommerce, workflow management, and server-side rendering. We work with streaming frameworks and databases to support embedded serverless functions for data filtering and analytics. The serverless functions could be database UDFs. They could also be embedded in data ingest or query result streams. Take full advantage of the GPUs, write once, and run anywhere. Get started with the Llama 2 series of models on your own device in 5 minutes. Retrieval-argumented generation (RAG) is a very popular approach to building AI agents with external knowledge bases. Create an HTTP microservice for image classification. It runs YOLO and Mediapipe models at native GPU speed.
  • 29
    SuperDuperDB

    SuperDuperDB

    SuperDuperDB

    Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.
  • 30
    WhyLabs

    WhyLabs

    WhyLabs

    Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.