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

From
Revision as of 16:53, 31 March 2019 by BPeat (talk | contribs)
Jump to: navigation, search

Youtube search... ...Google search

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

________________________________________________________

  • Amazon AWS two levels: predictive analytics with Amazon ML and the SageMaker tool for data scientists
  • 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.
  • Algorithmia provides a could based platform for algorithm developers to share their work, and for application developers to incorporate algorithms into their applications.
  • 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.
  • 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.
  • 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.
  • Google Cloud Platform (GCP) allow people without data science expertise to train models on their data in an automated manner.
  • 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
  • Microsoft Azure two main categories: Azure Machine Learning Studio and Bot Service.
  • 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.
  • 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
  • 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
  • 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.
Retrieved from "https://primo.ai/index.php?title=Platforms:_AI/Machine_Learning_as_a_Service_(AIaaS/MLaaS)&oldid=7030"