1 / 8

ML Model Deployment_ Edge AI vs. Cloud AI

Discover the key differences between Edge AI and Cloud AI for ML model deployment. Learn which suits your needs best. Enroll in a machine learning course in Chennai today!<br>

dilip34
Download Presentation

ML Model Deployment_ Edge AI vs. Cloud AI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ML Model Deployment: Edge AI vs. Cloud AI This presentation explores the key differences between edge AI and cloud AI, providing a comprehensive guide to help you choose the right deployment strategy for your use case.

  2. Understanding the Fundamentals: Edge AI vs. Cloud AI Architecture Cloud AI Edge AI Data is sent to a remote server for processing, allowing for Models are deployed directly onto devices, enabling real-time powerful computing resources but introducing latency and decision-making with reduced latency and improved data potential privacy concerns. privacy.

  3. Key Factors Driving Deployment Decisions: Latency, Cost, and Scale Low latency is crucial for Cost considerations include 1 2 real-time applications, both hardware and requiring edge AI. infrastructure, with cloud AI potentially being more cost-effective for large deployments. Scalability is a key advantage of cloud AI, allowing for easy 3 expansion of resources.

  4. Edge AI Advantages: Real-time Processing and Data Privacy Real-time Processing Data Privacy Critical for applications like autonomous vehicles, where Ensures sensitive data remains on the device, addressing latency is a significant factor. privacy concerns in areas like healthcare or finance.

  5. Cloud AI Benefits: Scalability and Computing Power Cloud AI offers significant Easily scales up or down to processing power, ideal for accommodate fluctuating computationally intensive tasks like workloads, making it suitable for image recognition. large-scale deployments.

  6. Real-world Case Studies: Successful Deployments in Different Scenarios Smart factories use edge AI for real-time machine vision 1 and predictive maintenance. Healthcare leverages cloud AI for analyzing medical images 2 and developing personalized treatments. Financial institutions rely on both edge and cloud AI for 3 fraud detection and risk assessment.

  7. Implementation Challenges and Best Practices for Both Approaches Edge AI requires careful hardware selection and model optimization for efficient performance. Cloud AI necessitates secure data transmission and robust network infrastructure. Both approaches require rigorous testing and validation to ensure model accuracy and reliability.

  8. Decision Framework: Selecting the Right Deployment Strategy for Your Use Case Select the deployment strategy that best Evaluate Edge AI vs. Cloud AI based on latency, cost, security, scalability, and performance. Understand the pros and cons of each approach. Enroll in a machine learning course in Chennai to master AI deployment strategies! aligns with your requirements and Define your use case and identify critical constraints. factors like latency, cost, and data privacy.

More Related