- Machine Learning Model Development: Utilized Dataiku to develop a predictive model, focusing on data preprocessing, feature selection, and model training to achieve high accuracy.
- API Development and Security: Created APIs to expose the machine learning model for external queries. Implemented security best practices by storing API passwords as Kubernetes secrets, ensuring sensitive information is encrypted and isolated.
- Streamlit Web Application: Developed a user-friendly web application with Streamlit, enabling users to interact with the machine learning model, real time data analysis. This application demonstrates the practical use of the model in making predictions based on user input and the data at real time
- Kubernetes Deployment: Successfully deployed the Streamlit application and associated components on a Kubernetes cluster using Minikube. Organized resources within a dedicated namespace to maintain a clean and secure environment.
- Real-Time Data Scoring with Kafka: Integrated a Confluent Kafka cluster to facilitate real-time data scoring. This advanced feature allows the model to process streaming data, providing instant insights and enhancing the model's applicability to real-world scenarios.
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