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 AI in OTT Overview:
 AI integration in OTT offers opportunities and challenges.
 AI algorithms personalize content, improve engagement, and optimize delivery.
 Challenges:
 Data Privacy and Security:
 Compliance with regulations like GDPR and CCPA.
 Implement robust data protection measures.
 Content Curation:
 Ensuring accuracy and relevance of recommendations.
 Mitigating biases in AI algorithms.
 Infrastructure:
 Need for scalable architecture.
 Real-time data processing and predictive analytics.
 Talent Acquisition:
 Shortage of skilled professionals.
 Importance of continuous training and upskilling.
 Best Practices:
 Ethical AI Principles:
 Prioritize fairness, transparency, and accountability.
 Conduct regular audits of AI systems.
 Collaboration:
 Foster cross-functional collaboration.
 Harness collective expertise for innovation.
 Continuous Learning:
 Encourage experimentation and iterative development.
 Optimize AI-driven features and functionalities.
 User Education and Transparency:
 Explain AI algorithms to users.
 Empower users with control over their data.
ANIn Gurugram April 2024 |Can Agile and AI work together? by Pramodkumar Shrivastav
ANIn Gurugram April 2024 |Can Agile and AI work together? by Pramodkumar Shrivastav
ANIn Gurugram April 2024 |Can Agile and AI work together? by Pramodkumar Shrivastav
ANIn Gurugram April 2024 |Can Agile and AI work together? by Pramodkumar Shrivastav
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ANIn Gurugram April 2024 |Can Agile and AI work together? by Pramodkumar Shrivastav

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  • 5.  AI in OTT Overview:  AI integration in OTT offers opportunities and challenges.  AI algorithms personalize content, improve engagement, and optimize delivery.
  • 6.  Challenges:  Data Privacy and Security:  Compliance with regulations like GDPR and CCPA.  Implement robust data protection measures.  Content Curation:  Ensuring accuracy and relevance of recommendations.  Mitigating biases in AI algorithms.  Infrastructure:  Need for scalable architecture.  Real-time data processing and predictive analytics.  Talent Acquisition:  Shortage of skilled professionals.  Importance of continuous training and upskilling.
  • 7.  Best Practices:  Ethical AI Principles:  Prioritize fairness, transparency, and accountability.  Conduct regular audits of AI systems.  Collaboration:  Foster cross-functional collaboration.  Harness collective expertise for innovation.  Continuous Learning:  Encourage experimentation and iterative development.  Optimize AI-driven features and functionalities.  User Education and Transparency:  Explain AI algorithms to users.  Empower users with control over their data.