Market Definition - Artificial Intelligence Platform (AI)

The Artificial Intelligence (AI) platform is a collection of services that facilitate the machine learning process. This covers assistance with data collection and preparation, as well as training, testing, and deploying machine learning models for large-scale applications. Layers in AI platforms enable enterprises to deploy machine learning models from a range of frameworks, languages, platforms, and tools. These layers can be classified into five groups:

  1. The Data and Integration Layer
  2. The Experimentation Layer
  3. The Operations and Deployment Layer
  4. The Intelligence Layer
  5. The Experience Layer

The AI platform gives developers the tools and technology they need to build AI-powered apps. Many AI apps are now designed specifically for cloud-based platforms, allowing for quick and easy deployment. The algorithms in the AI platform provide logical models for application developers to construct a variety of unique applications, such as speech and voice recognition, text recognition, and predictive analytics.

Market Overview

Due to the development of massive amounts of data that require analysis to enhance service providers' decision-making processes, the Artificial Intelligence (AI) platform industry is expected to increase in the coming years. The worldwide Artificial Intelligence (AI) platform market is predicted to increase at a 30.5% compound annual growth rate (CAGR) from USD 2.61 billion in 2017 to USD 9.88 billion by 2022. Some of the primary driving factors for the market are the rising need for AI-based solutions and the expansion of data creation.

Machine Learning (ML) software is predicted to have a bigger market share and expand at a faster rate. The AI managed service is predicted to expand at a faster rate than the other services, and it will be a bigger contributor overall. Because there are two ways to implement AI platforms: cloud and on-premises, the managed service is considered to be increasing quicker. Cloud-based solutions provide a wide range of flexible AI platform options. Forecasts and prescriptive models are predicted to have the biggest market share in the application sector, while text recognition is expected to expand at the fastest CAGR throughout the forecast period.


The competitive leadership mapping section provides information regarding key vendors offering best Artificial Intelligence (AI) platform and outlines the findings and analysis as well as rates them accordingly based on vendor performance within each evaluation criteria. The evaluation criteria are based on 2 broad categories, namely, Product Maturity and Company Maturity. Each category encompasses various parameters on the basis of which Artificial Intelligence platform vendors are evaluated.

Parameters considered under Product Maturity include breadth and depth of product offering, product features & functionality, focus on product innovation, product differentiation and impact on customer value, and product quality & reliability.

Parameters considered under Company Maturity include geographic footprint/presence in emerging markets, breadth of applications served, channel strategy and fit, the effectiveness of organic growth strategy, and mergers & acquisitions strategy.


Top Visionary Leaders in Artificial Intelligence platform 


Microsoft offers AI platform services and tools to aid developers in the development of AI-powered models. Data science tools for rapidly building and operationalizing AI products and services, as well as ready-to-use AI services, are part of the Microsoft AI platform. The Microsoft AI platform provides developers and data scientists with a full range of customized AI Services, enterprise-grade AI Infrastructure, and contemporary AI Tools to help them build future-ready applications.


Machine learning, data analytics, and data storage are among the cloud computing capabilities offered by Google Cloud Platform. The platform delivers a secure and cost-effective infrastructure as well as data and analytics capabilities to help companies produce better products. Google's Cloud ML Engine allows developers to design machine learning models for any quantity of data. The Google Cloud ML Engine provides managed services that serve as a platform for model building and prediction, as well as infrastructure replacement.


IBM is a key player in deep learning research and development, having spent much in the field. Businesses may use the IBM Watson bundle to integrate AI into their apps while also aiding with cloud data management. To have successful conversional partners, IBM Watson has a wide variety of language processing tools. Watson provides an automated predictive analytic solution that reveals the driving results automatically.


The SAP Leonardo Machine Learning platform provides a centralized infrastructure for building and maintaining intelligent applications. It is built on the SAP Cloud Platform and features machine learning capabilities to assist companies in discovering connections and patterns in their data. It consists of services that allow people to access and learn from data. It also enables the creation of corporate apps with intelligent features without the requirement for data science expertise.


Qualcomm AI Platform was designed with the goal of bringing machine learning to mobile devices. The platform offers a variety of smartphone, camera, and virtual and augmented reality (VR/AR) activities. To eliminate the requirement for a network or Wi-Fi connection, Qualcomm is working on integrating AI technology into a range of devices, including robots and automobiles. Furthermore, Qualcomm Research was formed in the Netherlands, demonstrating the company's continuing efforts in AI R&D.


The Amazon machine learning service reduces the need to grasp challenging technology and machine learning methodologies, as well as offers visualization tools and wizards to help with model construction. By offering visualization tools and wizards to help in the creation of machine learning models, it eliminates the need to understand difficult technology and machine learning methodology.


The Salesforce Einstein package contains data modelling, preparation, and infrastructure activities that may be leveraged to enhance predictive models and applications. By making picture identification and natural language processing capabilities available to customers, the Einstein platform services lay the groundwork for creating AI-driven apps. Marketers may examine the target audience, content, and channels while developing campaigns with Marketing Cloud Einstein capabilities like Predictive Scoring, Predictive Audiences, and Automated Send-time Optimization.


Intel has made investments in startups in order to boost its AI platform's technological capabilities. Apart from investments, Intel has bolstered its Artificial Intelligence Platform through acquisitions. By utilizing such methodologies, Intel is seeking to retain a competitive position in AI platform technologies such as deep learning, neural networks, and machine learning (ML). The Intel Nervana Platform is designed to serve as the foundation for AI systems that are highly efficient. The platform is intended to be easy and quick to use.


Top Dynamic Differentiators in Artificial Intelligence Platform


HPE unveiled hardware, services, and software focused on assisting research institutes and businesses in creating insights from huge volumes of data using high-performance computing and AI technology. The HPE C3 AI Suite is a collection of services and capabilities that enables AI applications to be delivered more quickly than using previous methods. Data scientists and application developers may use the C3 model-driven architecture, as well as a set of data integration, management, and processing capabilities, time-series services, AI and model management, and a security framework, to deliver AI at scale.


To boost the productivity of its projects, Infosys is focusing on implementing the software automation platform, as well as AI-based automation approaches. Many of Infosys' clients benefit from the Mana AI platform, which helps them achieve efficiency and automation. The Infosys AI platform Nia is made up of three main components: a data platform, and automation platform, and a knowledge platform, as well as AI capabilities including machine learning, data analytics, and robotic process automation.


Through innovation, research, and investments, Wipro is concentrating on AI platform technology. To differentiate oneself in the IT services industry, Wipro is focusing on AI initiatives. Wipro HOLMES aims to create the framework for cognitive process automation, knowledge virtualization, predictive systems, digital virtual assistants, robots, drones, and digital virtual agents. Natural language processing, machine learning, semantic ontologies, deep learning, genetic algorithms, knowledge modelling, and pattern recognition are among the technologies included in the platform, which cut costs while improving the experience, efficiency, and automation.


Top Innovators in Artificial Intelligence Platform


SAS combines artificial intelligence into its software to give organizations more intelligent, automated solutions that help them boost productivity and explore new prospects. SAS Visual Data Mining and Machine Learning is a cutting-edge technology that combines complex analytics, data preparation, visualization, model assessment, and model deployment in one convenient location. SAS AI technologies provide smarter, automated solutions. It boosts your imagination and opens doors to new possibilities.


Oracle's ready-to-build AI platform provides a set of cloud services enabling data scientists and application developers to quickly design, train, deploy, and manage AI-powered applications. Its ready-to-use AI cloud apps with cognitive capabilities aid in achieving better business outcomes. It provides a comprehensive set of cloud services for developing, deploying, and managing AI-powered systems. Machine learning is working behind the scenes to automate security patches and backups, as well as enhance database query speed, with ready-to-use Oracle Autonomous Database solutions.


Absolutdata provides the NAVIK AI platform, which serves as a foundation for developing AI-enabled products and services. It is a platform that allows improved decision-making through the combination of analytics, data, and technology. The service provides superior decision-making skills to the marketing and sales teams. NAVIK Converter 2. 0 is an improved version of Absolutdata's NAVIK Converter that includes enhanced analytics and machine learning to assist marketers with campaign creation.


Ayasdi's cutting-edge machine intelligence platform combines automation, machine learning, and topological data analysis to make extracting knowledge from even the largest and most complex data sets as simple as possible, while also enabling the deployment of intelligent, AI-based applications across the enterprise. Ayasdi is striving to extend the application of artificial intelligence in a range of businesses. In order to introduce its AI platform in the healthcare and finance industries, Ayasdi teamed with Deloitte.


Top Emerging Companies in Artificial Intelligence Platform


To improve your decision-making and client experiences, the Rainbird platform uses AI-powered automation. In a number of industries, including healthcare, financial services, retail, and manufacturing, the Rainbird platform allows improved decision-making. As with human conversations, the Rainbird artificial intelligence engine guarantees that future queries are evaluated in context.


Kasisto is a conversational AI platform that allows users to build virtual assistants and bots that provide personalized, pleasant, and intelligent experiences across different channels. On the mobile, web, messaging platforms, and voice-enabled devices, the KAI conversational AI platform enables virtual assistants and bots. It creates intelligent discussions with clients that fulfill requests, solve issues, and anticipate their requirements.


The DataRobot automated machine learning platform combines knowledge, experience, and best practices to give machine learning projects unrivaled levels of automation and ease of use. In a fraction of the time, the DataRobot platform provides extremely accurate prediction models. It analyses the training data and recommends the best model type automatically. DataRobot transforms characteristics for best outcomes by executing operations such as one-hot encoding, missing value imputation, text mining, and standardization.


BigML's cloud-based Machine Learning solution is easy to use, integrate, and put into action right now. BigML's Machine Learning algorithms handle real-world challenges throughout your business utilizing a single, standardized framework. In a number of sectors, it offers a wide range of predictive applications. It adjusts resources automatically to meet computational demands in a cost-effective way while protecting end-users from infrastructure issues.

Vital AI

Vital AI builds AI applications that intelligently automate business processes for greater efficiency and create innovative communication for people, devices, and data using the Haley Software Development Kit (SDK), an intelligent agent platform with various AI algorithms. This set of tools is capable of maintaining and deploying intelligent data models across application architectures.

Top 10 Artificial Intelligence Platform:

  2. GOOGLE Cloud Machine Learning Engine
  3. IBM Watson
  4.  AMAZON ML platform services
  5. SAP Leonardo Machine Learning
  6. INTEL Nervana Platform
  7. Salesforce Einstein suite
  8. Qualcomm 
  9. HPE C3 AI Suite
  10. SAS 

Benefits of Artificial Intelligence Platform

Gains in efficiency and productivity

Two of the most frequently mentioned advantages of using AI in the workplace are increased efficiency and productivity. The Artificial Intelligence Platform can complete jobs at a speed and scale that people cannot. Simultaneously, by eliminating such activities from the obligations of human employees, AI empowers those individuals to focus on higher-value jobs that technology cannot. This enables businesses to reduce the expenses of completing monotonous, repeated operations that can be automated while still utilizing the talent of their human resources.

Improved business speed

Artificial Intelligence promotes faster development cycles and reduces the time it takes to get from design to commercialization, resulting in a higher, and more rapid, return on investment in research funds.

New competencies and a broader business model

Executives may utilize AI to expand their company models. Data and analytics deployment in the company gives up new options for organizations to compete in many fields.

Customer service that is better

The AI-Powered Enterprise can learn more about the demands of its customers, resulting in more tailored and individualized interactions between businesses and their customers.

Improved surveillance

Organizations may adopt near-instantaneous monitoring capabilities that can alert them to concerns, propose action, and, in some circumstances, even start a reaction, thanks to AI's ability to take in and interpret huge volumes of data in real-time.

A few uses of Artificial Intelligence platform -

  • Speech to text conversion
  • Spam filtering
  • Image classification and tagging, for example, for parental advise
  • Media object detection
  • Customer behavior analysis
  • Network intrusion detection
  • Recommendation systems (shopping, music, friends, etc.)
  • Detecting terrorism funding (among millions of transactions every day)
  • Detecting faces in a photo, video, or other images
  • Stock price movement forecast


Market Dynamics


The proliferation of data generation, increasing demand for AI-based solutions, increasing operational efficiency and lowering costs, and the growing desire to improve customer experience are the main driving drivers for the artificial intelligence platform market.

The following are some significant drivers for the artificial intelligence platform:

  • The need for AI-based solutions is increasing. As the need for automation and optimization of corporate processes grows, more companies are turning to AI-based solutions.
  • The automobile and IT industries are driving development. The artificial intelligence platform market is expected to grow in the coming years due to the trend of industries moving toward automation to boost productivity.
  • In addition, the market for artificial intelligence platforms is being driven by the proliferation of big data and the desire for intelligent virtual assistants.


The Artificial Intelligence Platforms Market is hampered by a number of factors. Some of the barriers to the growth of the AI platform market include a lack of skilled employees and data privacy concerns in emerging economies. These variables contribute to the danger of failing to perform in a competitive business environment, necessitating the use of AI platform solutions. In addition, the artificial intelligence platform industry will be challenged by its inability to guard against new types of threats.


The artificial intelligence platform industry has risen in response to a shift in client preferences toward convenience and luxury. Moreover, numerous firms are predicted to utilize the artificial intelligence platform market to boost their consumer services. Furthermore, the growing demand for intelligent business processes and end-user innovations will provide ample opportunity for the artificial intelligence platform market to grow.


The emergence of data privacy concerns will pose a significant threat to the artificial intelligence platform business. AI applications raise a number of safety and security concerns. In AI solutions, there is a high risk of information leakage, which can lead to data misuse or manipulation. Also, customers are concerned about the lack of openness about the usage and processing of data. Access control, identity management, risk management, regulatory and legislative compliance, auditing, and logging are all areas where these concerns might have legal and security repercussions.

360 Quadrants Research Methodology

Top Companies in Artificial Intelligence Platform will be rated using the following methodology -

  1. A highly experienced team of researchers and senior analysts conduct extensive research to generate a list of vendors (competitors).
  2. A patent-pending algorithm is used to collect inputs from key stakeholders—industry experts, buyers, vendors, and 360Quadrants analysts—based on criteria for Product Maturity and Company Maturity.
  1. Criteria under Product Maturity include breadth and depth of product/service offering, product features and functionalities, product differentiation, and their impact on customer value.
  2. Criteria under Company Maturity include geographical footprint, partner ecosystem, financial stability, and client coverage or sector footprint.
  1. Approximately 25+ in-depth parameters will be considered for research for the Artificial Intelligence Platform market. These parameters will be updated every 6 months to ensure the latest developments are taken into consideration.
  2. A weight is assigned to each stakeholder based on information gathered pertaining to the above criteria as well as inputs from stakeholders. These inputs follow the order of priority given below:
  1. Buyers
  2. Industry Experts
  3. 360Quadrants Analysts
  4. Vendors (Competitors)
  1. The inputs are analyzed, and a final rating is assigned to each vendor (competitor).
  2. After the finalization of ratings, each vendor is placed in the most relevant quadrant based on their score.

Best Artificial Intelligence (AI) Platforms in 2022

Comparing 28 vendors in Artificial Intelligence Platform across 254 criteria.

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Microsoft provides AI platform services and resources to assist developers in the development of AI-enabled models. It is also believed to be aggressively investing in services, tools, and platforms to assist in the integration of AI and data-driven intelligence into all applications. It also launched the Microsoft Research AI lab, which focuses on overcoming AI challenges. ML, NLP, decision-making, and visual perception are among the technologies used in the study.

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Google Cloud Platform provides cloud computing services that include machine learning, data analytics, and data storage. TensorFlow, an open-source toolkit from Google, may be used to do numerical calculations using data flow graphs. TensorFlow assists users from a variety of industries in using a variety of applications, ranging from language translation to illness early detection. Aside from that, Google's ML Engine allows developers to design machine learning models on any size of data. In addition, the company's Cloud Machine Learning Engine provides managed services that eliminate the need for infrastructure and serve as a foundation for model creation and prediction.

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The IBM Watson package allows businesses to integrate AI into their applications while also assisting with cloud data management. It has the PowerAI platform, which has a variety of AI capabilities. These qualities negate the need for AI solutions to be developed. Furthermore, the PowerAI platform includes AI-rich features such as deep learning, allowing businesses to meet technological demands. Enterprises can use IBM Power Systems software in conjunction with the PowerAI platform to deploy PowerAI with deep learning capabilities for improved performance.

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Qualcomm assists in the development of AI-enabled applications for a variety of industries. The company has been concentrating on applying machine learning and artificial intelligence (AI) to a variety of industrial applications. For example, the business bought Scyfer B.V., which has established capabilities in machine learning, to increase the capabilities of the Artificial Intelligence Platform technology across numerous industries, such as manufacturing and finance. Qualcomm is focusing on integrating AI technology into a variety of devices, including robots and autos, to eliminate the need for a network or Wi-Fi connection. Furthermore, the company's consistent efforts in AI R&D may be seen in the fact that Qualcomm Research was founded in the Netherlands. The company collaborated with Google to speed up TensorFlow, Google’s open-source library. Qualcomm established a research lab with the assistance of the University of Amsterdam. In addition, the company revealed that deep learning frameworks such as Caffe2 and TensorFlow will be supported.

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Intel has used acquisitions to bolster its Artificial Intelligence Platform products; for example, it recently bought Nervana Systems, a company that specializes in machine learning skills. Apart from the acquisition approach, the corporation is claimed to be making capital investments in companies like AEye, Element AI, and CognitiveScale to drive AI innovation. Furthermore, the company has made investments in startups to improve the technological capabilities of its AI platform. Apart from acquisitions and investments, the company partnered with Tata Consultancy Services to develop AI, IoT, cloud, and 5G architecture. Intel is attempting to maintain a competitive position in AI platform technologies such as deep learning, neural networks, and machine learning (ML) by employing such techniques.

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The SAP Leonardo Machine Learning platform was built on the SAP Cloud Platform and has ML features to assist enterprises in discovering connections and patterns in data. It consists of services that allow users to learn from and get information from data. Furthermore, it enables the use of intelligent capabilities for the development of enterprise applications while obviating the need for data science expertise. The SAP Leonardo ML platform provides a foundation for developing and managing intelligent applications across a shared infrastructure.

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SAS Visual Data Mining and Machine Learning is a cutting-edge system that brings together sophisticated analytics, data preparation, visualization, model assessment, and model deployment in one place. It also accepts code written in major open source languages. This dependable, collaborative environment yields the necessary results, assisting in the improvement of organizational operations and the discovery of new growth chances.

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HPE focuses on the launch of AI-enabled platforms, as seen by the fact that it just announced Investigate Analytics, a software solution that combines big data and AI technology to assist financial firms in spotting risk and fraudulent behaviors. By analyzing massive amounts of data, this solution aids in the detection of such frauds and hazards. Furthermore, the company introduced hardware, services, and software aimed at providing high-performance computing and AI technologies to aid scientific institutes and enterprises in generating insights from massive amounts of data. Furthermore, these solutions are geared toward assisting enterprises with security and economic concerns.

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The company is focused on adopting the software automation platform, along with AI-based automation techniques, to increase the efficiency of its projects. Infosys’ Mana platform of AI was adopted by many of its clients to achieve efficiency and automation. Moreover, the company invested in the Danish AI startup, UNSILO, which leverages NLP and ML to gain insights into numerous texts for increasing the efficiency and the speed of workers. Additionally, to help clients take advantage of the AI-based technologies, Infosys launched the AI platform, Nia.

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Data scientists and developers may use AWS Managed ML Platforms to generate models without having to worry about infrastructure administration. Amazon ML eliminates the need to master difficult technologies and machine learning methods, as well as provide visualization tools and wizards to assist in the creation of machine learning models. Apache Spark on Amazon EMR is a distributed processing solution that focuses on large data workloads and is a free source. It has a number of features, including performance enhancements and the ability to quickly construct applications, such as libraries, to aid in the development of apps for diverse use cases.

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The Salesforce Einstein package includes data modelling, preparation, and infrastructure operations that may be used into predictive models and applications to gain benefits. The Einstein platform services provide the foundation for building AI-driven apps by making image recognition and natural language processing capabilities available to users. Marketers may use technologies like Predictive Scoring, Predictive Audiences, and Automated Send-time Optimization in the Marketing Cloud Einstein to assess the target audience, content, and channels while building campaigns. In addition, Analytics Cloud Einstein assists in the discovery of future patterns for business processes and delivers insights from a vast amount of data. These platforms eliminate the need for algorithms and mathematical models to be created. Enterprises can also establish an intelligent, automated, and predictive client engagement experience by leveraging Service Cloud Einstein. Customers can use the Community Cloud Einstein to locate information and get recommendations on what to read.

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Oracle provides readymade AI cloud applications with intellectual features that drive better business outcomes. It offers a full suite of cloud services to build, deploy, and manage AI-powered solutions. It automates security patching, backups, and improve database query performance, which eliminates human error and repetitive manual tasks so organizations can focus on higher-value activities.

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Wipro is focusing on AI platform technology innovation and development, as evidenced by its partnership with Ramot, Tel Aviv University's (TAU) business engagement center, in July 2017. The collaboration was centered on AI research, which is constantly evolving. Furthermore, Wipro has made investments in AI platform technologies through the Horizon Program (an intrapreneurship program). Apart from that, the company is concentrating on its AI investments in order to differentiate itself in the IT services market. It also launched the SAP software automation solution, which automates corporate activities using SAP's Enterprise Resource Planning (ERP) software. Wipro's AI platform, HOLMES, is integrated into the automation service.
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Absolutdata, acquired by Infogain has been updating its product offering in the market; for instance, the company launched the upgraded version NAVIK Converter 2. 0, which comprises technologies, such as advanced analytics and ML, to help brands in designing campaigns. Furthermore, the company launched its AI platform, NAIVIK, as well as, AI-based tools for helping the sales and marketing team in achieving efficiency. Apart from that, to increase its expertise around analytics and achieve steady growth, the company added more staff. It is believed that the company is attempting to develop more innovative AI platforms to offer better experiences to its clients.

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Ayasdi partnered with Deloitte to launch its AI platform in the healthcare and financial sectors. Both firms worked to expand AI use across a variety of industries. Ayasdi also takes advantage of Deloitte's skills in cognitive and other new technologies for business application discovery. Furthermore, Ayasdi is believed to be seeing tremendous growth in its healthcare vertical, and the company has added to its healthcare employees to meet the increased demand. The new hires will help the company improve its position in critical analytical issues for its products, such as patient risk stratification and fraud, clinical variation, and denials management.

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Kasisto offers a conversational AI platform to create virtual assistants and bots that deliver personalized, delightful, and intelligent experiences across multiple channels. It enables a network of intents wired at runtime to enable human-like, cross-intent conversational experiences. KAI includes a deep-learning analytical toolset for data collection and analysis, model training, testing, and deployment.

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The DataRobot automated machine learning platform combines expertise, experience, and best practices to provide unrivaled levels of automation and ease of use for machine learning projects. Users of all skill levels, from businesspeople to analysts to data scientists, may use DataRobot to construct and deploy highly accurate predictive models in a fraction of the time it takes to do so using traditional modelling methods. DataRobot analyzes training dataset and automatically recommend the optimal type of model: regression, classification, or time series.

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Rainbird is a platform for automated decision-making powered by artificial intelligence. It goes beyond simple decision-making rules engines and limited 'black box' machine learning that can't explain itself. By allowing semantic linkages between multiple Rainbird knowledge maps, the platform enables smarter decision-making. It allows for the 'joining up' of previously compartmentalized knowledge, resulting in a more holistic and strategic system capable of automating complicated decisions.

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BigML provides well-engineered Machine Learning algorithms that have been proved to solve problems using a single, consistent framework across the organization. It eliminates a project's reliance on a large number of independent libraries, which adds to the project's complexity, maintenance expenses, and technical debt. BigML provides a wide range of predictive applications in a variety of industries. It automatically changes resources to suit computing needs in a cost-effective manner while shielding end-users from infrastructure problems.

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Vital AI Development Kit (VDK) offers a suite of software to reorganize the flow of data across application architecture and integrate with analytical frameworks using the Vital Service API. The key tool is VitalSigns, which provides a consistent data model used by all software modules.

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Teneo is a SaaS Platform for Conversational AI. The platform enables individuals to have intelligent, human-like conversations with electronic device applications and services.

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Faculty Platform allows to manage and schedule model training and execution pipelines natively, and deploy models into staging and production with one simple workflow. It uses a browser or command-line interface that integrates with favorite IDE and version control systems for easy interface customization.

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Figure Eight generates high-quality customized training data and automates the business processes with easy-to-deploy models. It offers products and services like self-driving cars, intelligent personal assistants, medical image labeling, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.

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Netomi, formerly Artificial Intelligence responds quickly to issues that are repeatable while enabling human agents to focus on high-impact work. It ensures the best experience to customers whenever there is an issue, a question, or a need. It can collaborate with human agents to offer high-quality resolutions to customer queries on email, mobile, and chat.     

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RapidMiner offers a complete solution on a single platform that supports the whole Machine Learning workflow, from data preparation to model deployment and ongoing model management. It is easy-to-understand and utilizes workflow designer speeds up end-to-end data science for increased productivity.

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Face Recognition Surveillance Platform, SenseFace Face Recognition technology is based on a deep learning algorithm is used in this service. It provides real-time facial recognition to protect the public. SenseFace is a leader in integrating intelligent video analysis technologies. It can be used for target surveillance, monitoring of a person's trajectory, population management, and data analysis, among other things.

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Frequently Asked Questions (FAQs)
Natural language generation, speech recognition, virtual agents, text analysis, machine learning platforms, AI optimized hardware, emotion recognition, image decision management, deep learning platforms, biometrics, robotics automation, cyber defense, compliance, knowledge worker assistance, content creation, recognition, and marketing automation are just a few of the technologies that are being developed.
Chatbots, speech recognition, text recognition, face identification, and sentiment analysis are all examples of predictive and prescriptive models.
Accessibility of AI and analytics tools is critical with the development of citizen data scientists. By offering tools for managing the end-to-end machine learning life cycle, AI platforms assist to democratize and productize ML models. They do it by using a SaaS interface that is meant to make user interactions easier for non-technical individuals. Without these platforms, AI's impact would be limited because a larger portion of resources would be spent on developing and maintaining models.
Environment monitoring and reaction for climate change objectives, automated transportation, taking over risky occupations, robots working alongside people, enhanced senior care, cyborg (organic/bio-mechanic) creatures.
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