Algorithmia
Case Study
Devopsbay undertook a project to enhance the Algorithmia MLOps platform by developing a custom Python installer, integrating monitoring capabilities, and creating Ansible automation. This project aimed to streamline the installation and management of Algorithmia on Kubernetes clusters, ensuring efficient deployment and configuration of necessary resources.
Key aspects of the project
Technical Challenges
Adapting the Python Algorithmia installer for Kubernetes and adding Ansible automation for installing custom components.
Project Implementation
The project involved adding features to the installer, automating deployment and configuration using Ansible, and managing the state of the installation securely.
Technologies Used
The project utilized Ansible, Python, Kubernetes, and AWS to achieve its goals.
Technical challenges
- Adapting the Python Algorithmia Installer for Kubernetes: The primary challenge was modifying the existing Python-based installer to be compatible with Kubernetes environments. This required in-depth knowledge of both Python and Kubernetes to ensure seamless integration.
- Adding Ansible Automation for Installing Custom Components: Another significant challenge was creating Ansible playbooks to automate the installation and configuration of custom components required by Algorithmia. This included deploying and managing Custom Resource Definitions (CRDs) and other Kubernetes resources.
Project implementation stages
- 1
Adding Features to the Installer
The initial phase involved enhancing the Python installer to support Kubernetes deployments. This included adding necessary features and ensuring compatibility with Kubernetes clusters.
- 2
Ansible Automation
The next phase focused on developing Ansible playbooks to automate the deployment and configuration of Algorithmia's components. This included handling CRDs and other essential resources.
- 3
Managing Installation State
To address the complexity of managing various components like Ansible, Terraform, kubectl, and Helm, a platform installer state functionality was added. This feature allowed the management of state files for the Algorithmia installation, storing them securely as JSON within an S3 bucket.
Problems and solutions
Team involvement
- Python Developers
- Machine Learning Engineers
- QA Engineers
- Front-end Developers
- MLOps Engineers
- Project Managers
Conclusion
Devopsbaysuccessfully enhanced the Algorithmia MLOps platform by developing a custom Python installer and integrating Ansible automation. The project addressed critical technical challenges, streamlined the installation process, and ensured secure state management. The use of advanced technologies and a skilled team enabled the efficient deployment and configuration of Algorithmia on Kubernetes clusters, ultimately contributing to the platform's robustness and reliability.
