This Specialization is designed for data scientists, machine learning engineers, and analytics engineers who want to strengthen their production machine learning skills. Across three courses, learners progress from tabular machine learning and feature engineering to time-series forecasting with Python and real-time ML systems with APIs, feature stores, and model-serving infrastructure. The curriculum emphasizes model deployment, practical evaluation, and the engineering techniques needed to serve machine learning models in production environments.

Applied Learning Project

Learners will apply production machine learning techniques to practical problems involving structured data, forecasting, and real-time prediction. They will develop tabular ML workflows using feature engineering, tree-based models, and hyperparameter tuning; build time-series forecasting solutions using methods such as ARIMA and Prophet; and explore deploying models through APIs. They will also examine stream processing, feature stores, model-serving infrastructure, and latency optimization to understand how end-to-end real-time ML pipelines operate. These activities connect model development and evaluation with the engineering requirements of production deployment.