Kaggle

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.

It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.

Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables

Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.

It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.

It is an open source, visual language for data science that lets you design, prototype and develop any application by connecting visual elements together. Build dashboards, RPA workflows, and apps. No coding required.

It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

It is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way.

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

It lets you run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without your own infrastructure.

It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

It is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning. You can choose from over 250 pre-built transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats, and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and machine learning projects. You only pay for what you use - no upfront commitment.

Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior.

Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

It is the machine learning platform for developers to build better models faster. Use W&B's lightweight, interoperable tools to quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues.

It is a Google Chrome extension that helps you scrape data from web pages and into a CSV file or Excel spreadsheet.

It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).

It is data modeling tool used to find, visualize, design, deploy and standardize high-quality enterprise data assets. Discover and document any data from anywhere for consistency, clarity and artifact reuse across large-scale data integration, master data management, metadata management, Big Data, business intelligence and analytics initiatives – all while supporting data governance and intelligence efforts.

Lamina helps you integrate Deep Learning models like Sentiment Analysis and Entity Extraction into your products with a simple API call. Relieving you of getting data, creating a model and training them which would be compute-intensive.

It is a Recommender as a Service with easy integration and powerful Admin UI. The Recombee recommendation engine can be applied to any domain that has a catalog of items and is interacted by a large number of users. Applicable to web and mobile apps, It improves user experience by showing the most relevant content for individual users.

It provides all you need to build and deploy computer vision models, from data annotation and organization tools to scalable deployment solutions that work across devices.

It is a tool that lets you and your team easily share knowledge and explore data. With the API you can send timeseries data to Clarify and use timelines to visualize and collaborate around this data.

Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows.

It is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories.

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

It is a data science platform for tracking experiments, versioning data, models, and pipelines, using Git. It allows your whole team to compare, reproduce, and contribute to each other's work. It allows your whole team to compare, reproduce, and contribute to each other's work.

It is an advanced data warehouse and analytics platform available both on premises and on cloud. With enhancements to in-database analytics capabilities, this next generation of Netezza enables you to do data science and machine learning with data volumes scaling into the petabytes.

Dasha is a conversational AI as a Service platform. Dasha lets you create conversational apps that are more human-like than ever before, quicker than ever before and quickly integrate them into your products.

It is a better place for your data science projects, Jupyter notebooks, machine learning models, experiment logs, results, and more.

Delight your users with personalised content recommendations. It's easy to set up and works with or without collaborative data. The Lateral API is trained on 10s of millions of high quality documents from law, academia and journalism. It can understand any document and provide intelligent recommendations.

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

It is a next-generation data analytics and business intelligence platform that excels at rapidly delivering business value from transactional data and is the first real breakthrough in data analytics in 20 years. It provides an integrated end-to-end data experience, from data acquisition and enrichment to visualizing and sharing results. It cuts project implementation time from months to weeks, provides revolutionary query speed, and maintains a unified, single-source of truth for multiple workloads including business intelligence, analytics, and machine learning.