Best AI Development Platforms for Python

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

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.

Runlve is at the forefront of the AI revolution. We provide data science, MLOps and data & models management to empower our community and customers with AI capabilities that will propel their projects forward.

Nyckel makes it easy to auto-label images and text using AI. We say ‘easy’ because trying to do classification through complicated AI tools is hard. And confusing. Especially if you don't know machine learning. That’s why Nyckel built a platform that makes image and text classification easy. In just a few minutes, you can train an AI model to identify attributes of any image or text. Our goal is to help anyone spin up an image or text classification model in just minutes, regardless of technical knowledge.

SuperAGI SuperCoder combines AI-native development platform & AI agents in order to enable fully autonomous software creation starting with the python programming language & frameworks. SuperCoder 2.0 leverages Large Action Models (LAMs) and LLMs fine-tuned to python code creation leading to one-shot or few-shot python functional programming with significantly higher accuracy on SWE-bench and Codebench SuperCoder 2.0 is an autonomous system that combines software guardrails for Flask & Django development frameworks with SuperAGI's Generally Intelligent Developer Agents in order to deliver complex real-world software systems SuperCoder 2.0 integrates deeply with existing developer stacks such as Jira or Github, Jenkins, CSPs, and QA solutions like BrowserStack/Selenium Clouds, to ensure a seamless experience in software development.

Create, evaluate, deploy, refine, and improve custom natural language processing models. Your team can automate workflows without the need for an AI expert team or expensive infrastructure. Metatext makes it easy to create customized AI/NLP models without any prior knowledge of ML, data science or MLOps. Automate complex workflows in just a few steps and rely on intuitive APIs and UIs to handle the heavy lifting. Our APIs will handle all the heavy lifting. Your custom AI will be trained and deployed automatically. A set of deep learning algorithms will help you get the most out of your custom AI. You can test it in a Playground. Integrate our APIs into your existing systems, Google Spreadsheets, or other tools. Choose the AI engine that suits your needs. Each AI engine offers a variety of tools that can be used to create datasets and fine tune models. Upload text data in different file formats and use our AI-assisted data labeling tool to annotate labels.

Akira AI provides the best explainability, accuracy and scalability in their application. Responsible AI can help you create applications that are transparent, robust, reliable, and fair. Transforming enterprise work with computer vision techniques, machine learning solutions and end-to-end deployment of models. ML model problems can be solved with actionable insights. Build AI systems that are compliant and responsible with proactive bias monitoring capabilities. Open the AI blackbox to optimize and understand the correct inner workings. Intelligent automation-enabled process reduce operational hindrances, and optimize workforce productivity. Build AI-quality AI solutions that optimize, monitor, and explain ML models. Improve performance, transparency and robustness. Model velocity can improve AI outcomes and model performance.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

Experience JinaChat - a LLM service designed for professionals. JinaChat is a multimodal chat service that goes beyond text and includes images. Enjoy our free short interactions below 100 tokens. Our API allows developers to build complex applications by leveraging long conversation histories. JinaChat is the future of LLM, with multimodal conversations that are long-memory and affordable. Modern LLM applications are often based on long prompts or large memory, which can lead to high costs if the same prompts are sent repeatedly to the server. JinaChat API solves this issue by allowing you to carry forward previous conversations, without having to resend the entire prompt. This is a great way to save both time and money when developing complex applications such as AutoGPT.

LLMFuzzer will be the perfect tool for anyone who is a security enthusiast, a researcher in cybersecurity, or a pentester. They love to find and exploit AI system vulnerabilities. It's designed to make your testing process efficient and streamlined. We are currently working on a full documentation. It will include detailed information about the architecture, various fuzzing techniques, examples, as well as how to extend the tool.

Lilac is a free open-source tool that allows data and AI practitioners improve their products through better data. Understanding your data is easy with powerful filtering and search. Work together with your team to create a single dataset. Use best practices for data curation to reduce the size of your dataset and training costs and time. Our diff viewer allows you to see how your pipeline affects your data. Clustering is an automatic technique that assigns categories to documents by analyzing their text content. Similar documents are then placed in the same category. This reveals your dataset's overall structure. Lilac uses LLMs and state-of-the art algorithms to cluster the data and assign descriptive, informative titles. We can use keyword search before we do advanced searches, such as concept or semantic searching.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

Learn how to choose the right LLM based on your needs, and how you can optimize quality, speed and cost-efficiency. With a single API and standard API, you can access all LLMs from all providers. Set your own constraints for output speed, latency and cost. Define your own quality metric. Personalize your router for your requirements. Send your queries to the fastest providers based on the latest benchmark data for the region you are in, updated every 10 minutes. Unify's dedicated walkthrough will help you get started. Discover the features that you already have and our upcoming roadmap. Create a Unify Account to access all models supported by all providers using a single API Key. Our router balances output speed, quality, and cost according to user preferences. The quality of the output is predicted using a neural scoring system, which predicts each model's ability to respond to a given prompt.

Release high-quality LLM applications quickly without compromising testing. Never let the subjective and complex nature of LLM interactions hold you back. Generative AI produces subjective results. A subject matter expert must manually check a generated text to determine its quality. You probably know if you're developing an LLM application that you cannot release it without addressing numerous constraints and edge cases. Hallucinations and other issues, such as incorrect answers, bias and deviations from policy, harmful material, and others, need to be identified, investigated, and mitigated both before and after the app is released. Deepchecks allows you to automate your evaluation process. You will receive "estimated annotations", which you can only override if necessary. Our LLM product has been extensively tested and is robust. It is used by more than 1000 companies and integrated into over 300 open source projects. Validate machine-learning models and data in the research and production phases with minimal effort.

Semantic Kernel, a lightweight open-source development tool, allows you to easily build AI agents, and integrate the latest AI model into your C# or Python codebase. It is a middleware that allows rapid delivery of enterprise grade solutions. Semantic Kernel is flexible, modular and observable, which is why Microsoft and other Fortune 500 firms use it. With security-enhancing features like hooks, filters, and telemetry, you can be confident that you are delivering responsible AI at scale. It's reliable and committed to non-breaking changes. Version 1.0+ is supported across C# Python and Java. Existing chat-based APIs can be easily extended to support other modalities such as voice and video. Semantic Kernel is future-proof and connects your code with the latest AI models that evolve as technology advances.

Our open-source research efforts are focused on both the new "ware" (middleware and "software" which will wrap and integrate LLMs) as well as building high quality, automation-focused enterprise model available in Hugging Face. LLMWare is also a coherent, high quality, integrated and organized framework for developing LLM-applications in an open system. This provides the foundation for creating LLM-applications that are designed for AI Agent workflows and Retrieval Augmented Generation. Our LLM framework was built from the ground-up to handle complex enterprise use cases. We can provide pre-built LLMs tailored to your industry, or we can fine-tune and customize an LLM for specific domains and use cases. We provide an end-toend solution, from a robust AI framework to specialized models.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Diaflow is a platform that allows you to scale AI across your entire organization. It enables everyone to deploy AI workflows which drive innovation. Create powerful apps and workflows using any data source to automate manual processes or create fully automated ones. Automate manual business processes in a way that your team will enjoy. Diaflow's intuitive components and interfaces allow you to create powerful AI-driven apps for your business that you can be proud of. Diaflow AI-powered editor offers a new way to create and edit documents. Your expertise can be leveraged to provide 24/7 support. With a built-in AI spreadsheet solution, you can easily manage and transform your data. Diaflow makes it easy to create amazing products for your business. Diaflow allows you to create apps and workflows without coding in just minutes.

Agentplace is an AI platform that allows AI apps and websites to be built directly on top a AI model. No coding is required. Agentplace allows you to create AI websites and applications. ChatGPT is now your interactive, dynamic website, capable of answering all questions, selling products and delivering services. It uses AI's adaptability and common sense. It also makes use of voice. You can program the system entirely in text. The website's user interface changes depending on what users do or say. Instead of static pages, UI elements can appear, update or hide based on user needs. For example, the form can be expanded as needed or the product page can display different details depending on the user's questions. Users can speak to your website just like they would do with ChatGPT. Voice can be used to ask questions, obtain information, or complete tasks. The site is accessible while driving, cooking or even while driving.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

The best data will help you achieve peak model performance. Create and manage training data for any visual modality. Debug models, boost performance and make foundation models yours. Expert review, QA, and QC workflows will help you deliver better datasets to your artificial-intelligence teams, improving model performance. Encord's Python SDK allows you to connect your data and models, and create pipelines that automate the training of ML models. Improve model accuracy by identifying biases and errors in your data, labels, and models.

Your team can develop AI applications using models such as GPT-4, and operate them visually. You can deploy your application within 5 minutes, whether it is for internal team use or an external release. Using documents/webpages/Notion content as the context for AI, automatically complete text preprocessing, vectorization and segmentation. No need to learn embedding methods anymore. This will save you weeks of development. Dify offers a smooth user experience for model access and context embedding. It also provides cost control, data annotation, and cost control. You can easily create AI apps for internal team use, or product development. Start with a prompt but go beyond its limitations. Dify offers rich functionality in many scenarios.