Releases: prathikanand7/dev-ops
v3.0
Release v3.0
Overview
Version 3.0 represents a major step towards production readiness, focusing on robust DevOps automation, system reliability, and infrastructure maturity. This release strengthens the platform’s testing strategy, improves deployment consistency, enhances configurability, and refines both developer and user experience.
The primary focus of this release is end-to-end validation, infrastructure reliability, and developer productivity, ensuring that the system behaves predictably across the full workflow lifecycle.
Key Features
End-to-End Workflow Validation
A complete end-to-end (E2E) testing pipeline has been introduced to validate the full system lifecycle.
This includes:
- Automated triggering of notebook execution workflows
- Validation of job submission, execution, and result retrieval
- Integration testing across API Gateway, Lambda, AWS Batch, and S3
This ensures that the entire system works cohesively and reduces the risk of integration failures.
Expanded Testing Coverage
Testing capabilities have been significantly improved across both infrastructure and application layers.
Enhancements include:
- Dedicated unit tests for Lambda functions
- Extended Terraform test coverage for infrastructure modules
- Improved validation of configuration and deployment logic
These additions strengthen system reliability and ensure safer deployments.
Infrastructure Optimization and Cost Improvements
Infrastructure has been further optimized to reduce unnecessary resource usage and improve cost efficiency.
Key changes:
- Removal of redundant VPC interface endpoints (ECS and ECS agent)
- Streamlined networking configuration
These optimizations reduce operational overhead while maintaining system functionality.
Configurable Job Execution Parameters
Job execution behavior is now more flexible through JSON-driven configuration.
This enables:
- Dynamic control over job attempt duration
- Easier tuning of execution parameters without code changes
- Centralized configuration for compute behavior
This improves adaptability for different workload types.
API Gateway and OpenAPI Enhancements
The API layer has been further standardized and automated using OpenAPI-driven Terraform templating.
Enhancements include:
- Improved API Gateway configuration via OpenAPI specification
- CORS support for better frontend integration
- Enhanced handling of S3-based results and artifact tracking
This results in a more maintainable and scalable API layer.
Developer Experience and Code Quality Improvements
The development workflow has been strengthened to enforce higher code quality and consistency.
Updates include:
- Introduction of pre-commit hooks
- Linting and formatting checks
- Secret detection
- Code quality enforcement
- Improved documentation across:
- Terraform modules
- API Gateway configuration
- Infrastructure bootstrap process
These changes improve developer productivity and reduce errors during development.
User Interface Enhancements
The frontend has been refined to improve usability and alignment with LifeWatch branding.
Improvements include:
- Updated UI styling consistent with LifeWatch design
- Enhanced parameter editing experience
- More intuitive interaction with job configuration and submission
This results in a more polished and user-friendly interface.
Architecture Impact
The core serverless and batch-based architecture remains unchanged, but this release significantly strengthens its reliability, testability, and configurability.
Key improvements include:
- Full end-to-end validation integrated into the CI/CD pipeline
- Stronger infrastructure testing via Terraform test suites
- Enhanced API standardization using OpenAPI
- Improved configuration management through JSON-driven design
These changes move the system closer to a production-grade DevOps platform.
Future Improvements
Future work will focus on:
- Multi-environment deployment (dev, staging, production)
- Enhanced authentication and authorization (e.g., Cognito or OAuth)
- Advanced observability and alerting (CloudWatch dashboards and alarms)
- Additional compute profiles (e.g., GPU support)
Contributors
- Kayle Verhiel
- Giorgos Nikolaou
- Eneko Retolaza Ardanaz
- Prathik Anand Krishnan
MVP2.0
Release v2.0
Overview
Version 2.0 builds on the serverless foundation introduced in v1.0 by improving user experience, enhancing cost transparency, and expanding system functionality. This release focuses on usability, observability of user workflows, and cost optimization across the infrastructure.
Key Features
Job History and Workflow Tracking
The platform now provides persistent tracking of previously executed jobs.
A dedicated Lambda function has been introduced to manage job history, allowing users to:
- View previously submitted jobs
- Track execution outcomes
- Access past results
This improves transparency and enables users to better manage and revisit their workflows.
Cost Estimation for Workflows
Users can now view estimated costs before executing workloads.
The system provides predictive cost estimation based on selected compute resources and workload characteristics. This allows users to:
- Make informed decisions before running jobs
- Optimize compute selection (EC2 vs Fargate)
- Avoid unexpected cloud costs
S3-Based Notebook Integration
Support for S3-backed notebook workflows has been introduced.
This enables:
- Storage and retrieval of notebook-related artifacts in Amazon S3
- Improved data persistence for interactive or batch-based workflows
- Better integration between compute jobs and stored data
User Interface and Experience Improvements
The frontend has been significantly redesigned to improve usability and accessibility.
Enhancements include:
- Updated layout and styling
- Improved workflow navigation
- Integrated job history view
- More intuitive interaction with backend services
These changes provide a more streamlined and user-friendly experience.
Infrastructure Cost Optimization
Infrastructure efficiency has been improved by removing unnecessary AWS resources.
Specifically:
- Redundant VPC interface endpoints have been removed
- Overall cloud resource usage has been reduced
This results in lower operational costs without impacting system functionality.
Development Workflow Improvements
The development and collaboration workflow has been improved.
Updates include:
- Introduction of a standardized pull request template
- More consistent contribution and review process
This helps maintain code quality and improves team collaboration.
Architecture Impact
The core serverless architecture introduced in v1.0 remains intact, with enhancements focused on usability and cost-awareness.
New components introduced in this release:
- Job History Lambda for tracking and retrieval
- Cost estimation logic integrated into the backend
- Extended S3 usage for notebook and workflow storage
These additions enhance the existing architecture without increasing operational complexity.
Future Improvements
Future work will focus on deeper cost optimization insights, improved monitoring and observability, and further enhancements to workflow orchestration and user experience.
Contributors
Georgios Nikolaou
Kayle Verhiel
Eneko Retolaza Ardanaz
Prathik Anand Krishnan
MVP
Release v1.0
Overview
Version 1.0 introduces a major architectural transition for the platform. The system has moved from an initial monolithic backend implementation to a fully cloud-native serverless architecture deployed on AWS. The infrastructure is now entirely defined through modular Terraform configurations, and deployment pipelines have been improved to support automated container builds and infrastructure validation.
This release focuses on improving scalability, infrastructure reproducibility, and operational flexibility while simplifying backend management through managed cloud services.
Architecture Overview
The platform now follows a serverless and container-oriented cloud architecture built on AWS managed services.
The system workflow is structured as follows:
- Users interact with the frontend application which sends requests to the backend API.
- Amazon API Gateway receives incoming HTTP requests and routes them to the appropriate backend Lambda functions.
- AWS Lambda functions handle request processing, job orchestration, and task submission.
- Worker tasks are executed using containerized workloads through AWS Batch running on Fargate or optionally through EC2 compute instances.
- Once processing is completed, result artifacts are packaged and uploaded to Amazon S3.
- Users receive secure access to the results through dynamically generated pre-signed S3 URLs.
This architecture allows the system to scale automatically with demand while minimizing operational overhead and infrastructure management.
Serverless Backend Migration
The backend architecture has been fully migrated from a traditional Django and Celery deployment to a serverless model based on AWS Lambda. API Gateway now acts as the entry point for all backend requests and routes them to Lambda functions that perform orchestration and processing.
This change significantly reduces infrastructure management complexity while enabling automatic scaling and improved resource efficiency.
Flexible Compute and Job Processing
The platform now supports multiple compute environments for executing workloads.
Two compute options are currently supported:
- AWS Batch with Fargate, which provides serverless container execution for scalable batch processing.
- EC2-based compute resources, allowing more control over compute environments for workloads that require specific instance configurations.
This flexibility allows the system to optimize execution depending on workload characteristics and performance requirements.
Secure Data Storage and Delivery
Result management and distribution have been enhanced to improve security and efficiency.
Processing outputs are automatically compressed into ZIP archives before being stored in Amazon S3. Once processing completes, users receive dynamically generated pre-signed URLs that allow them to securely download results without exposing the underlying storage infrastructure.
This approach ensures both secure data access and efficient transfer of result artifacts.
Frontend Serverless Integration
The frontend application has been refactored to communicate directly with the serverless backend APIs.
Key updates include:
- Direct communication with API Gateway endpoints
- CORS configuration to enable browser-based requests
- Simplified interaction between frontend services and Lambda-based APIs
These updates allow the frontend to operate seamlessly with the new serverless backend architecture.
CI/CD and Container Deployment Improvements
Deployment pipelines have been enhanced to improve reliability and automation.
Worker containers are now built and validated automatically before being pushed to Amazon Elastic Container Registry (ECR). The deployment pipeline includes verification steps to ensure that only valid and tested container images are deployed to the cloud environment.
These improvements increase the stability of compute workloads and reduce deployment errors.
Infrastructure as Code with Terraform
The entire cloud infrastructure is managed using Terraform.
Significant improvements were made to the Terraform codebase:
- Reorganization of the infrastructure configuration into modular child modules
- Removal of legacy Terraform environments
- Improved Terraform formatting and workflow automation
- Introduction of Terraform testing frameworks to validate infrastructure changes
The new modular structure improves maintainability while ensuring that the entire infrastructure can be reproducibly deployed from version-controlled configuration files.
Logging and Monitoring
System execution logs and infrastructure events are available through AWS CloudWatch. Logging provides visibility into Lambda execution, API activity, and compute job processing, enabling easier debugging and monitoring of system behavior.
Breaking Changes
This release removes the previous Django and Celery-based backend services. All backend functionality has been migrated to AWS Lambda and API Gateway.
Existing deployments based on the previous architecture will require migration to the new serverless infrastructure configuration defined through Terraform.
Future Improvements
Future development will focus on expanding compute orchestration capabilities, improving observability, and introducing additional workload management and monitoring features to support larger-scale distributed processing.
Contributors
Georgios Nikolaou
Kayle Verhiel
Eneko Retolaza Ardanaz
Prathik Anand Krishnan