This series of The Ultimate Machine Learning System will cover the fundamental concepts, the standard techniques, and the common tools for designing and developing a Machine Learning system from Problem Definition, Data Engineering, Model Development to Deployment.

This series intends for ML practitioners with at least one year of experience working with ML, and they should:

• Be able to express the intuition behind basic ML algorithms and perform basic hyperparameter optimization
• Understand the ML lifecycle and its components
• Experience with ML and deep learning frameworks
• Understand and experience in model training, deployment, and operational best practices

Please note that not all audiences are suitable for all the posts in this series. Some posts target the business decision-makers, and some posts target ML Engineers or Data Scientists. I write this series for people like me who are engineers now and in the transition to a higher responsibility position.

References

• CS 329S: Machine Learning Systems Design (opens in a new tab)
• AWS Training and Certification (opens in a new tab)