Kubeflow is a machine learning toolkit for Kubernetes, designed to simplify the deployment and management of ML workflows. It provides components for training, serving, and managing models. Kubeflow enables data scientists and engineers to build and deploy scalable ML applications on Kubernetes.

Domino Data Lab is a data science platform that centralizes infrastructure, tools, and data for collaborative research. It provides a workspace for building, deploying, and monitoring models. Domino Data Lab supports version control, reproducibility, and collaboration for data science teams in regulated industries.

Google Colaboratory is a free cloud-based Jupyter notebook environment that requires no setup. It allows users to write and execute Python code in their browser. Colaboratory provides access to GPUs and TPUs, making it suitable for machine learning and data analysis tasks.

Jupyter provides open-source tools for interactive computing across various programming languages. It offers a web-based interface for creating and sharing documents containing live code, equations, visualizations, and narrative text. Jupyter supports data cleaning, transformation, statistical modeling, data visualization, and machine learning workflows for researchers and developers.

Deepnote is a collaborative data science notebook environment designed for real-time collaboration. It allows data scientists to write, run, and share code in a browser-based interface. Deepnote supports Python, SQL, and Markdown, enabling teams to work together on data analysis and machine learning projects.

Databricks is a unified analytics platform powered by Apache Spark, providing collaborative workspaces for data science and engineering teams. It offers tools for data processing, machine learning, and real-time analytics. Databricks simplifies the development and deployment of data-intensive applications in the cloud.

Anaconda is a distribution of Python and R for scientific computing, simplifying package management and deployment. It includes a collection of data science packages, such as NumPy, pandas, and scikit-learn. Anaconda simplifies environment management, making it easier for data scientists to manage dependencies and reproduce results.

Amazon SageMaker is a fully managed machine learning service that enables data scientists and developers to build, train, and deploy ML models. It provides a suite of tools for data preparation, model building, and deployment. SageMaker simplifies the end-to-end machine learning workflow in the cloud.

Hex is a collaborative data workspace that combines notebooks, data apps, and knowledge management. It allows data teams to build and share interactive data products. Hex supports SQL, Python, and Markdown, enabling users to create data-driven stories and applications for business users.

RStudio provides tools for data science, including an integrated development environment (IDE) for R. It offers features for code editing, debugging, and visualization. RStudio supports reproducible research and collaboration for statisticians, data scientists, and analysts using the R programming language.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code, and deploying models. MLflow supports various machine learning frameworks and languages, enabling users to build and deploy models consistently.

Kaggle is a platform for data science and machine learning competitions. It provides datasets, code notebooks, and a community forum for data scientists. Kaggle allows users to collaborate, learn, and compete in solving data-related challenges, improving their skills and building portfolios.

Valohai is a machine learning platform that automates the entire ML pipeline, from data preparation to model deployment. It provides tools for experiment tracking, version control, and reproducibility. Valohai enables data scientists and engineers to collaborate and deploy models efficiently.

Amazon Web Services (AWS) offers a broad set of cloud computing services, including compute, storage, databases, analytics, and machine learning. AWS enables businesses to build and deploy applications in the cloud, scaling resources as needed. It provides infrastructure for various workloads, from web hosting to big data processing.

Google Cloud Platform (GCP) provides a suite of cloud computing services, including compute, storage, networking, and machine learning. GCP enables businesses to run applications, store data, and analyze information in Google's infrastructure. It offers tools for developers, data scientists, and IT professionals to build and deploy scalable solutions.

Microsoft Azure is a cloud computing platform offering a wide range of services, including virtual machines, databases, and AI. Azure enables businesses to build, deploy, and manage applications through a global network of data centers. It supports various programming languages, frameworks, and operating systems.

Weights & Biases provides a platform for tracking and visualizing machine learning experiments. It allows data scientists to log metrics, parameters, and artifacts during training. Weights & Biases supports collaboration and reproducibility, enabling teams to optimize models and share results effectively.

Comet is a platform for tracking, comparing, and optimizing machine learning experiments. It allows data scientists to log metrics, parameters, and code changes during training. Comet supports collaboration and reproducibility, enabling teams to improve model performance and accelerate research.

Neptune is a platform for experiment tracking and model management in machine learning. It allows data scientists to log metrics, parameters, and artifacts during training. Neptune supports collaboration and reproducibility, enabling teams to organize and optimize their machine learning workflows.

Observable is a collaborative platform for data exploration and visualization, built around JavaScript. It allows users to create interactive notebooks for data analysis and sharing. Observable supports real-time collaboration and version control, enabling teams to work together on data-driven projects.

DVC (Data Version Control) is an open-source tool for managing data and machine learning projects. It provides version control for data, models, and code. DVC enables data scientists to track changes, reproduce experiments, and collaborate effectively on data-driven projects.

Algorithmia is a platform for deploying and managing machine learning models. It allows developers to host and scale algorithms as microservices. Algorithmia supports various programming languages and frameworks, enabling users to build and deploy AI-powered applications in the cloud or on-premise.

Code Ocean is a cloud-based computational reproducibility platform for scientific research. It allows researchers to share and execute code, data, and environments. Code Ocean supports reproducible research and collaboration, enabling scientists to validate results and build upon existing work.

Paperspace provides cloud-based virtual machines for developers and data scientists, offering GPU-accelerated computing resources. It allows users to create and manage virtual desktops for development, gaming, and machine learning. Paperspace simplifies access to powerful computing resources without the need for local hardware.

Determined AI (acquired by HPE) offered a platform for accelerating deep learning training. It provided tools for distributed training, hyperparameter optimization, and experiment management. Determined AI aimed to improve the efficiency and scalability of deep learning workflows, now part of HPE's Ezmeral platform.

Mode Analytics is a data analytics platform that combines SQL, Python, and visualization tools. It allows data analysts to explore data, create reports, and share insights. Mode Analytics supports collaboration and version control, enabling teams to work together on data-driven decision-making.

FloydHub (now Valohai) provided a platform for deep learning, offering cloud-based infrastructure and tools for training and deploying models. It simplified the process of managing experiments, tracking results, and collaborating with teams. FloydHub aimed to accelerate the development of AI applications.

Dessa (acquired by Nvidia) provided an AI platform for building and deploying machine learning models. It offered tools for data preparation, model training, and deployment. Dessa focused on enabling enterprises to leverage AI for business applications, now integrated into Nvidia's AI offerings.

Papers With Code is a resource for machine learning papers, code implementations, and datasets. It provides a curated collection of research papers with associated code, enabling researchers and practitioners to reproduce results and build upon existing work. It helps track progress in machine learning.