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Artificial Intelligence (AI) Platform Market

The report provides key statistics on the market status of the leading Artificial Intelligence (AI) Platform Market players and offers key trends and opportunities in the market.u00a0

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Artificial Intelligence (AI) Platform Market

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  1. THE INSIGHT PARTNERS Artificial Intelligence (AI) Platform Market Forecast by 2024 - 2031 Technology, Media and Telecommunications The report provides key statistics on the market status of the leading Artificial Intelligence (AI) Platform Market players and offers key trends and opportunities in the market.

  2. TABLE of contents • Artificial Intelligence (AI) Platform Market Landscape • Artificial Intelligence (AI) Platform Market – Key Market Dynamics • Gastroparesis – Global Market Analysis • Global Artificial Intelligence (AI) Platform Market Analysis – By Type • Artificial Intelligence (AI) Platform Market – Geographic Analysis • Artificial Intelligence (AI) Platform Market - Covid-19 Impact Analysis • Industry Landscape and Company Profiles

  3. SEGMENTS By Component: By Deployment: 1 • • Cloud-based • On-premises • Hybrid 3 Software (e.g., machine learning frameworks, development tools, AI- powered analytics) Services (e.g., consulting, integration, training) • By Application: By End-User: 4 2 • Computer Vision • Natural Language Processing (NLP) • Predictive Analytics • Robotics Process Automation (RPA) • IT & Telecom • BFSI • Retail • Manufacturing

  4. GEOGRAPHIC Analysis

  5. COMPANY Profiles 1. Amazon Web Services (AWS) 2. Microsoft Azure 3. Google Cloud 4. IBM 5. Oracle 6. Salesforce 7. DataRobot 8. H2O.ai 9. Dataiku 10. Domino Data Lab

  6. Future Outlook 1. 1. Edge AI: Edge AI: With the rise of edge computing, there is an increasing need for AI systems that can function directly on devices like smartphones and cameras. This approach enables AI models to process data quickly and efficiently right where it’s collected, avoiding reliance on distant servers. 2. 2. Explainable AI: Explainable AI: There is a strong push to make AI systems more transparent. People want to understand how AI arrives at its decisions. This demand is driving the development of platforms that can clearly explain the decision-making processes of AI models, which is crucial for building trust. 3. 3. MLOps MLOps: : Machine Learning Operations, or MLOps, is becoming a critical component in AI platforms. It combines best practices for handling the complete machine learning lifecycle — from the initial development phase through deployment and ongoing maintenance. Implementing MLOps ensures that AI projects are not only efficient but also manageable over time.

  7. THE INSIGHT PARTNERS THANK YOU CONTACT US Contact Person: Sameer Joshi +1-646-491-9876 sam@theinsightpartners.com

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