I am pretty well aware of the mathematics & intuition behind many machine learning algorithms and I also became quite good at building / implementing them to solve real world problems. However, I always knew that building ML models is only a small part of the whole ML system (as you can see in the image below).
This is why I decided, as an initial dive to the field of MLOPs, to take the "Machine Learning Engineering for Production" specialization offered by DeepLearning.AI on Coursera. Generally, the specialization covered how to conceptualize, build, and maintain integrated systems that continuously operate in production. It offered the following 4 courses:
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deplyoing Machine Learning Models in Production
By completing all of those 4 courses, I was able to learned and acquire some practice in the following things:
- Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
- Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
- Build data pipelines by gathering, cleaning, and validating datasets
- Implement feature engineering, transformation, and selection with TensorFlow Extended
- Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
- Apply techniques to manage modeling resources and best serve offline/online inference requests
- Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
- Deliver deployment pipelines for model serving that require different infrastructures
- Apply best practices and progressive delivery techniques to maintain a continuously operating production system
Most importanly, I got a very good general understanding of the field and what I should learn in detail to excel at it.
Definitely recommend taking this specialization if you want to build a career in ML Engineering and Data Science!