FeaturedUdemy
Developing Machine Learning Models for Production
4.8(466)8k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
While most machine learning resources focus heavily on initial model training and hyperparameter tuning, nearly 90% of experimental models fail to reach production because they lack operational durability. This intermediate course shifts your perspective from basic ML engineering to a comprehensive MLOps (Machine Learning Operations) mindset, teaching you how to build, document, scale, and maintain models that last in production environments.
Who It's For
Machine learning engineers, data scientists, and MLOps practitioners who want to transition experimental Python models into robust, automated, and scalable production systems.
Key Takeaways
- Experimentation & Documentation: Design reproducible experiments to streamline model iterations while creating comprehensive pipeline documentation for seamless team collaboration.
- Model Packaging & Serialization: Master best practices for serializing and packaging both ML models and environment dependencies for long-term production reliability.
- Pipeline Automation & Scaling: Implement continuous automation and account for data complexity to scale, deploy, and monitor production pipelines in real time.
About this course
Much of today’s machine learning-related content focuses on model training and parameter tuning, but 90% of experimental models never make it to production, mainly because they were not built to last. In this course, you will see how shifting your mindset from a machine learning engineering mindset to an MLOps (Machine Learning Operations) mindset will allow you to train, document, maintain, and scale your models to their fullest potential.
Experiment and Document with Ease
Experimenting with ML models is often enjoyable but can be time-consuming. Here, you will learn how to design reproducible experiments to expedite this process while writing documentation for yourself and your teammates, making future work on the pipeline a breeze.Build MLOps Models For Production
You will learn best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible.Scale Up and Automate your ML Pipelines
By considering model and data complexity and continuous automation, you can ensure that your models will be scaled for production use and can be monitored and deployed in the blink of an eye.Once you complete this course, you will be able to design and develop machine learning models that are ready for production and continuously improve them over time.
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