This course bridges the gap between raw data and production-ready AI systems. In 2026, the value of a machine learning model is defined by the reliability of the data pipelines that feed it. This program transforms you into an MLOps-ready engineer capable of building automated, scalable, and observable data architectures.
Data Engineering Essentials
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Beginner level
Recommended experience
5 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Build scalable data pipelines using Pandas Polars and Apache Spark for diverse dataset sizes
Architect real time streaming solutions with Apache Kafka and feature stores for live ML inference
Automate complex ML workflows using Airflow and Prefect to ensure reliable continuous training
Details to know
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Recently updated!
March 2026
Assessments
4 assignments
Taught in English
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There are 4 modules in this course
Explore the foundational shift from traditional software development to data-centric machine learning operations. You will compare DevOps and MLOps workflows while mastering the core pillars of CI, CD, CT, and CM. This section establishes the architectural blueprint for building reliable and automated machine learning systems.
What's included
10 videos3 readings1 assignment
Master the essential techniques for collecting and preparing high-quality data for machine learning models. You will implement robust ETL processes and explore the strategic role of Data Lakes in modern ML stacks. Hands-on labs with Pandas and Polars will provide practical experience in transforming raw datasets into clean features.
What's included
7 videos2 readings1 assignment
Scale your engineering capabilities to handle massive datasets and real-time information flows. This module introduces distributed computing with Apache Spark and Dask alongside high-velocity streaming via Apache Kafka. You will also evaluate the critical role of Feature Stores in maintaining consistency between training and serving.
What's included
7 videos1 reading1 assignment
Connect individual data tasks into a seamless and automated production pipeline using Airflow and Prefect. You will learn to manage complex dependencies and schedule automated training triggers to ensure model performance over time. This section focuses on making your data workflows resilient through advanced monitoring and error handling.
What's included
4 videos2 readings1 assignment
Instructor
Offered by
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Frequently asked questions
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