Serverless Framework Implementation with Apache OpenWhisk or OpenFaaS
The objective of implementing a serverless framework with Apache OpenWhisk or OpenFaaS is to explore serverless computing architecture and its benefits. By using these frameworks, the project aims to understand how to deploy serverless functions and leverage the scalability and cost-effectiveness of serverless computing.
Procedure and Steps:
Choose a Serverless Framework:
- Decide whether to use Apache OpenWhisk or OpenFaaS based on your project requirements and familiarity with the frameworks.
Install and Set Up the Chosen Framework:
- Install Apache OpenWhisk or set up OpenFaaS according to the framework’s documentation.
Develop Serverless Functions:
- Write serverless functions in the programming language supported by the framework (e.g., JavaScript, Python, Go).
- Define the entry points and logic for your serverless functions.
Deploy Serverless Functions:
- Use the framework’s command-line interface (CLI) or web interface to deploy your serverless functions.
- Specify any dependencies or configurations required for your functions.
Test Your Serverless Functions:
Test your serverless functions locally using the framework’s testing tools or by invoking them through the framework’s API.
Monitor and Scale Your Functions:
- Monitor the performance and usage of your serverless functions using the framework’s monitoring tools.
- Scale your functions automatically or manually based on the workload using the framework’s scaling capabilities.
Tools Used:
- Apache OpenWhisk: An open-source serverless computing platform for building and deploying serverless functions.
- OpenFaaS: An open-source serverless framework for building and deploying serverless functions, with a focus on ease of use and flexibility.
10 MLOps Projects Ideas for beginners
Machine Learning Operations (MLOps) is a practice that aims to streamline the process of deploying machine learning models into production. It combines the principles of DevOps with the specific requirements of machine learning projects, ensuring that models are deployed quickly, reliably, and efficiently.
In this article, we will explore 10 MLOps project ideas that you can implement to improve your machine learning workflow.
MLOps Projects Ideas
- 1. MLOps Project Template Builder
- 2. Exploratory Data Analysis (EDA) automation project
- 3. Enhanced Project Tracking with Data Version Control (DVC)
- 4. Interpretable AI: Enhancing Model Transparency
- 5.Efficient ML Deployment: Accelerating Deployment with Docker and FastAPI
- 6. End-to-End ML Pipeline Orchestration: Streamlining MLOps with MLflow
- 7. Scalable ML Pipelines with Model Registries and Feature Stores
- 8. Big Data Exploration with Dask for Scalable Computing
- 9. Open-Source Chatbot Development with Rasa or Dialogflow
- 10. Serverless Framework Implementation with Apache OpenWhisk or OpenFaaS