Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)

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Machine learning as a service (MLaaS) is an umbrella definition of various cloud-based platforms that cover most infrastructure issues such as data pre-processing, model training, and model evaluation, with further prediction. Prediction results can be bridged with your internal IT infrastructure through REST APIs. Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson | Bradford Cross

Platforms

  • Amazon AWS
  • Apple ...Turi lets developers build apps with ML and AI capabilities that automatically scale and tune -- includes the Turi Machine Learning Platform, GraphLab Create, Turi Distributed, and Turi Predictive Services
  • Google, DeepMind & Google Cloud Platform (GCP) ...AI with TensorFlow ... Vertex AI
  • Hugging Face, allows users to share machine learning models and datasets
  • IBM Watsonx
  • Intel ...AI Opportunities for Cloud Service Providers
  • Kaggle ...is the place to do data science projects
  • Microsoft Azure ...two main categories: Azure Machine Learning and Bot Service.
  • Modal
  • NVIDIA ...dominant player in datacenter artificial intelligence (AI) acceleration
  • OpenAI
  • Palantir


_____________________ Others ___________________________

  • Anaconda Cloud You can search and download popular Python and R packages and notebooks, store your packages, notebooks and environments, and share them with your team. ... see Anaconda
  • Algorithmia provides a could based platform for algorithm developers to share their work, and for application developers to incorporate algorithms into their applications.
  • Automatic Business Modeler (ABM) a tool for automatic prediction of customers behavior. It provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection.
  • Ayasdi client, product, and market-related data
  • BigML a cloud based machine learning platform with an easy to use graphical interface. It also provides simple mechanisms to incorporate predictive models into production applications through its REST API.
  • Civis Analytics provides a machine learning platform that also includes large demographic databases and consulting services. Workflows that address various business problems are made available to users of the platform.
  • CloudFactory subscription-based model to access skilled workers who moderate and categorize data for machine learning algorithms and other processes.
  • Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects. Comet lets you compare different experiments and see the differences in code, hyper-params, and many other data points.
  • Databricks handle all analytic processes — from ETL to models training and deployment — leveraging familiar tools, languages, and skills, via interactive notebooks or APIs. From the original creators of Apache Spark
  • Dataflow build natural and rich conversational experiences
  • Dataiku a collaborative data science platform. It comes with all the tools most data scientists would need, along with a visual interface.
  • DataRobot (with Alteryx) comes as a cloud service and on-premises installation. It provides methods for the automation of many repetitive data science tasks.
  • DataRPM | Progress uses machine first approach to connect to the customer’s internal data, fuses it with external data sources and runs a series of automatics machine learning algorithms to build Predictive Analytics and Recommender Systems
  • DataScience offer a cloud based platform supporting most of the languages and libraries used in data science. It also comes with advisory services and some common data science solutions (e.g. recommendation engines, churn).
  • Datoin Build your Intelligent App Strategy - When computers were invented, the industry exploited them to automate stuff. Stuff that was quantifiable and unambiguous. The ambiguous, the qualitative, and the predictive were left to humans. Now, the paradigm shift is happening industry wide, to automate--something which was not possible yesterday.
  • Deep Cognition Deep Learning Studio simplifies AI development and deployment. single-user solution for creating and deploying AI. The simple drag & drop interface helps you design deep learning models with ease. Pre-trained models as well as use built-in assistive features simplify and accelerate the model development process. You can import model code and edit the model with the visual interface.
  • DeepAI Enterprise machine learning as a service. Leverage DeepAI's data science team to have your structured or unstrucutred data explored for patterns, correlations, and causation. We then use this information to develop machine learning algorithms that spot these patterns across your datasets as they grow.
  • Dialogflow Give users new ways to interact with your product by building engaging voice and text-based conversational interfaces, such as voice apps and chatbots, powered by AI. Connect with users on your website, mobile app, the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices
  • Domino can be implemented on-site or in the cloud. It uses all the commonly used tools and provides a working environment with features such as version control, collaboration and deployment tools.
  • FICO Analytic Cloud embraces machine learning, statistics, optimization and business rules management, in the context of a well managed environment. It also serves as a marketplace for developers of analytic solutions and users who have a need for them.
  • Figure Eight combines machine learning with crowd based services to collect, clean and label data sets (under NDA if needed). Provides a unique combination of machine and human skills.
  • FloydHub a private, secure home for your team. Help your team focus on a goal by organizing your data, jobs, workspaces and model deployments into projects.
  • H20 Driverless AI data visualisation, feature engineering, model interpretability and low-latency deployment
  • Haley.ai run bots, dialog system, databases, scripts/webservices, and predictive models
  • HPE Haven OnDemand provides more than 60 APIs and services that deliver deep learning analytics on a wide range of data, including text, audio, image, social, web and video.
  • Infosys Nia discovery of new opportunities to optimize, simplify, and automate complex business processes
  • KAI a conversational AI platform
  • LumenData provide a cloud hosted service to collect data, generate classification models and score new data. Code is added to web and portable device applications which stream data to the algorithms.io service, where it is captured and processed.
  • Meya.ai Mey Bot Studio is a web-based IDE to make bots
  • MLJAR.com human-first machine learning platform. It makes machine learning algorithms search and tuning painless. User need to upload a dataset, select input and target attributes and mljar will find best matching ML algorithm.
  • NAVIK AI the intelligence layer in emerging enterprise architectures.
  • The Open Machine Learning project is an inclusive movement to build an open, organized, online ecosystem for machine learning. We build open source tools to discover (and share) open data from any domain, easily draw them into your favorite machine learning environments, quickly build models alongside (and together with) thousands of other data scientists, analyse your results against the state of the art, and even get automatic advice on how to build better models. Stand on the shoulders of giants and make the world a better place.
  • OpenMined an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence.
  • Opera Solutions uses Signal Hub to extract thousands of ‘signals’ from large amounts of data. The signals (predictive relationships) can then be used by anyone who wants to build models or understand business dynamics.
  • Paperspace The first cloud built for the future. Powering next-generation applications and cloud ML/AI pipelines
  • Peltarion Start right away with your own data or with our data library
  • PurePredictive uses AI to automate the machine learning process. The platform automates the discovery of data transformations and higher order relationships between data features and automatically accommodates data drift.
  • RapidMiner unites data prep, machine learning, and predictive model deployment.
  • Receptiviti analyzes communication, helping you better understand
  • Scale high quality training and validation data for AI applications; API provides access to human-powered data for hundreds of use cases
  • Softweb Solutions - SIA efficiently carry out data preparation task. SIA enhances the predictive power of ML models allowing data scientists to focus on deriving business insights
  • Spell - Start-ups and big businesses alike use Spell to accelerate discovery and manage their end-to-end machine learning pipeline.
  • Squirro A.I.-driven Actionable Recommendations; a modern Cognitive Insights Engine
  • Twilio Autopilot Build, train, and deploy artificially intelligent bots, IVRs, and Alexa apps using natural language understanding and machine learning frameworks
  • Wipro HOLMES get past the adoption hurdles and achieve non-linear growth in speed, scale and agility.
  • Wise GE Digital's Machine Learning offering for industrial Internet of Things (IoT).
  • wit.ai natural language for developers to build applications and devices that you can talk or text
  • Xilinx AI Inference Acceleration
  • Yottamine Predictive Service allows for building models or making predictions in two simple steps. Via integration with scalable cloud computing it provides high speed and efficiency. It also conforms with the SSL industry security standard and can exports to PMML those models that are supported by the standard. Data scientists can connect and control Yottamine Predictive Web Services using R programming language via YottamineR package.


Software Factory

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A software factory is a structured collection of related software assets that aids in producing computer software applications or software components according to specific, externally defined end-user requirements through an assembly process. A software factory applies manufacturing techniques and principles to software development to mimic the benefits of traditional manufacturing. Software factories are generally involved with outsourced software creation.... Software factory–based application development addresses the problem of traditional application development where applications are developed and delivered without taking advantage of the knowledge gained and the assets produced from developing similar applications. Many approaches, such as training, documentation, and frameworks, are used to address this problem; however, using these approaches to consistently apply the valuable knowledge previously gained during development of multiple applications can be an inefficient and error-prone process. Software factories address this problem by encoding proven practices for developing a specific style of application within a package of integrated guidance that is easy for project teams to adopt. Developing applications using a suitable software factory can provide many benefits, such as improved productivity, quality and evolution capability. Software Factory | Wikipedia

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