handwriting recognition ML web app
Project Recap: Handwriting Recognition ML Web App with DevOps
Table of Contents:
Introduction
1.1. Project Overview
1.2. Use Case
Web ML app for simple and easy deployment of the whole DevOps lifecycle with integration of useful add-ons like chat bot reply for education.
1.3. Technologies
IaC tech stacks
mixed Cloud technology usage
CI/CD pipelines automatioed build
Containers and their orchestration
API dev with Python/Javascript for ML Model
Machine learning model development and integration in a web app
Monitoring and logging with Nagios and itegration tools (grafana, telegraf, prometheus, influxDB, ELK Stack)
Messaging chat bot integragration with RabbitMQ messaging and slack or discord API integration
Load balancing with AWS or GC with health checks
Future growth and improvement plans
Infrastructure Deployment
2.1. Infrastructure as Code with Terraform
Provides a declarative approach to defining infrastructure
Ensures consistency and repeatability
Allows for version control and collaboration
2.2. Configuration Management with Ansible
Agentless, easy to set up and use
Utilizes a human-readable YAML syntax
Provides idempotent and efficient deployments
2.3. AWS and GCP Integration
AWS offers a wide range of managed services and a mature ecosystem
GCP provides robust solutions for databases and machine learning
Integrating both platforms leverages the benefits of both cloud providers
CI/CD Pipeline
3.1. GitLab/Jenkins Integration
Facilitates automated building, testing, and deployment
Ensures code quality and consistency
Allows for faster development and release cycles
3.2. Automated Building, Testing, and Deployment
Increases efficiency and reduces manual intervention
Catches errors early in the development process
Ensures rapid and consistent deployment of new features and bug fixes
Containerization
4.1. Docker
Ensures consistent runtime environment across development, testing, and production
Facilitates efficient resource utilization and application isolation
Simplifies deployment and scaling of individual components
4.2. Kubernetes Orchestration
Provides robust container orchestration and management
Offers advanced features such as rolling updates, auto-scaling, and self-healing
Improves overall application reliability and resilience
API Development and Integration
5.1. Python/JavaScript API for ML Model
Enables seamless communication between the web app and the ML model
Allows for easy integration with various web frameworks and libraries
Ensures a flexible and scalable API implementation
5.2. API Best Practices
Promotes consistency, maintainability, and security
Facilitates efficient API development and integration
Ensures a high-quality user experience and performance
Machine Learning Model
6.1. Training the Model
Leverages available GPU resources for efficient training
Allows for the development of a high-quality ML model for handwriting recognition
Ensures accurate and reliable recognition of drawn letters and numbers
6.2. Jupyter Notebook and AWS SageMaker
Jupyter Notebook provides an interactive environment for developing, documenting, and sharing ML code
AWS SageMaker simplifies the process of training, tuning, and deploying ML models at scale
Facilitates easy integration with other AWS services and data sources for a seamless development experience
Monitoring and Logging
7.1. Nagios Monitoring Setup
Provides comprehensive monitoring of infrastructure and application components
Enables real-time detection and alerting of issues
Facilitates proactive management of system performance and reliability
7.2. Logging Tools Integration (Grafana, Telegraf, Prometheus, InfluxDB, ELK Stack)
Offers a flexible and extensible platform for collecting, storing, and visualizing log data
Facilitates the identification and troubleshooting of issues in the application and infrastructure
Provides insights into system performance, resource utilization, and trends
Messaging and Chatbot Integration
8.1. RabbitMQ Messaging
Enables efficient and reliable communication between application components and services
Ensures scalability and resilience in the messaging layer
Provides a foundation for chatbot integration and automation
8.2. Slack or Discord API Integration
Offers a user-friendly interface for interacting with the application and infrastructure
Allows for the creation of custom commands and functionality, such as database queries and system status checks
Facilitates collaboration and communication among team members and stakeholders
Load Balancing
9.1. AWS Application Load Balancer
Provides an efficient and reliable means of distributing traffic among application instances
Offers advanced features such as path-based routing, health checks, and SSL termination
Ensures high availability, fault tolerance, and optimal resource utilization
9.2. Configuration and Health Checks
Allows for the customization of load balancing rules and behavior
Ensures that traffic is directed only to healthy and responsive instances
Facilitates proactive management of application performance and reliability
Future Growth and Improvement Plans
10.1. Enhanced ML Model and Training Techniques
- Leverage advanced ML algorithms and techniques for improved handwriting recognition accuracy
- Explore transfer learning and other approaches to reduce training time and resource requirements
- Continuously evaluate and refine the ML model to adapt to changing user needs and expectations
10.2. Improved Scalability and Resilience
- Evaluate and implement strategies for optimizing resource utilization, such as auto-scaling and multi-region deployment
- Continuously monitor and improve application performance, reliability, and security
- Adopt emerging technologies and best practices to ensure the long-term success of the web app