Scalable Machine Learning Architecture

QDT’s platform architecture, powered by Kubernetes, highlights a comprehensive, adaptable solution for deploying and managing machine learning workloads. The platform is capable of supporting a wide range of machine learning applications and business needs across different sectors.

Core Components

• QDT Data Lake

‍Serves as the central hub for data ingestion and storage, boasting over 150,000 globally available data sources updated daily. This repository is critical for feeding diverse and current data into the machine learning algorithms.

• Ingestion Processes

‍These are responsible for the efficient intake and preliminary processing of data from various sources, ensuring data is properly formatted, cleaned, and primed for analysis.

• Worker Nodes‍

Managed by Kubernetes and housed in Docker containers, these nodes execute the machine learning tasks. They can be dynamically adjusted in response to computational demand, demonstrating the platform's scalability.

• Frontend Processes

‍ Encompass the user interface and application logic, managing user interactions, requests, and the presentation of results. This layer is crucial for a seamless user experience.

• Redis In-memory Database

‍Known for rapid data retrieval, Redis supports fast access to processed data and intermediate results, which is essential for quick model iterations and reduced response times.

• Quantum ML

‍At the heart of the platform, this machine learning engine uses a comprehensive library of leading algorithms to analyze data, generate predictions, and provide insights, automating the model-building process across various business scenarios.

• User Authentication

‍Implements robust security measures for user verification, safeguarding access to the platform and its data resources.

• External API Integrations

‍Facilitates interactions with external systems and services, broadening the platform's capabilities with additional data sources, analytics tools, and other functionalities.

• Cassandra

‍A scalable and resilient NoSQL database that backs the platform's data storage needs, ensuring fast access to vast amounts of data with durability.

Kubernetes Integration

• Automated Deployment and Scaling

‍The platform uses Kubernetes to orchestrate containerized applications' deployment, scaling, and management, allowing for efficient computational resource utilization, including automated rollouts, rollbacks, and self-healing features.

• Load Balancing and Service Discovery

‍Through Kubernetes, the platform can evenly distribute network traffic among containers for enhanced stability and load management, contributing to improved platform responsiveness and reliability.

• Cost Optimization

‍Kubernetes dynamically allocates resources based on actual demand, significantly reducing costs by avoiding payment for idle resources. Resources are allocated only when models are queued for execution.

• Disaster Recovery

‍Leveraging Kubernetes' self-healing mechanisms, the platform ensures high availability and resilience, minimizing potential downtime and maintaining continuous service.

‍