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Develop and Enhance Edge AI MLOps SDK for Scalable AI Model Management

eleks.com

Information technology

Challenges in Scaling Edge AI Solutions

Latent AI faced challenges in rapidly developing, testing, and deploying AI models for edge devices to meet growing market demand. Limited resources and the complexity of managing models, datasets, and data transformations across a distributed workforce hindered their ability to deliver new features and enhance user experience quickly.

About the Client

US-based AI company specializing in Machine Learning Operations (MLOps) for edge AI, focusing on optimizing and securing AI models and runtimes.

Project Goals

Expand the capabilities of the existing MLOps SDK to support a wider range of AI models and runtimes.
Enhance the SDK's security features to protect AI models and data at the edge.
Improve the SDK's scalability to accommodate increasing customer demand and data volumes.
Accelerate the development and deployment of new product features and UX enhancements.
Improve the efficiency of AI model optimization and performance tuning for edge devices.

Functional Requirements

Automated Model Optimization: Automatically optimize AI models for performance and efficiency on edge devices.
Enhanced Security: Implement robust security measures to protect AI models and data at rest and in transit.
Scalable Deployment: Enable scalable deployment of AI models to a large number of edge devices.
Real-time Monitoring: Provide real-time monitoring of AI model performance and health.
Automated Data Transformation: Automate data transformation processes to ensure data quality and consistency.

Preferred Technologies

Cloud-native technologies

Containerization (e.g., Docker, Kubernetes)

Edge computing platforms

Python, TensorFlow, PyTorch

Required Integrations

Cloud storage services (e.g., AWS S3, Azure Blob Storage)
Device management platforms
Security tools
Monitoring and logging systems

Non-Functional Requirements

High Scalability: The SDK should be able to handle a large number of users and devices.
High Performance: The SDK should provide fast and efficient AI model processing.
Security: The SDK should be secure and protect against unauthorized access.
Reliability: The SDK should be reliable and available.
Maintainability: The SDK should be easy to maintain and update.

Expected Business Impact

This project is expected to significantly accelerate Latent AI's growth by enabling faster development and deployment of AI solutions, improving customer satisfaction, and increasing market share. Enhanced scalability and security will strengthen Latent AI's competitive advantage in the edge AI market. Faster time-to-market for new features will also improve revenue generation.