Master modern data engineering tools by building web apps, microservices, and CLI tools using Python, FastAPI, and Rust with cloud integration.
Master modern data engineering tools by building web apps, microservices, and CLI tools using Python, FastAPI, and Rust with cloud integration.
This practical course equips data professionals with essential skills for building modern data engineering solutions. Students learn to create and deploy interactive Jupyter notebooks on cloud platforms, develop scalable Python microservices using FastAPI, and build robust command-line tools in both Python and Rust. The curriculum covers containerization of machine learning microservices, automated testing, and project publishing. Through hands-on experience with cutting-edge tools and techniques, participants gain the ability to create efficient, production-ready data solutions.
Instructors:
English
English
What you'll learn
Build and deploy interactive Jupyter notebooks for data analysis
Develop scalable Python microservices using FastAPI
Create and containerize machine learning microservices
Implement robust command-line tools in Python and Rust
Deploy notebooks on cloud platforms like Google Colab and AWS SageMaker
Automate testing and publishing of data engineering projects
Skills you'll gain
This course includes:
40 Hours PreRecorded video
Graded assignments,Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
The course focuses on practical implementation of data engineering tools and techniques. Students learn to work with Jupyter notebooks, deploy applications on cloud platforms, develop microservices using FastAPI, and create command-line tools in Python and Rust. The curriculum emphasizes hands-on experience through project-based learning, covering everything from basic notebook usage to advanced containerization and deployment strategies.
Jupyter Notebooks
Module 1
Cloud-Hosted Notebooks
Module 2
Python Microservices
Module 3
Python Packaging and Rust Command-Line Tools
Module 4
Fee Structure
Instructors

30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.

20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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