From the course: Applied AI for IT Operations (AIOps)
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Model development best practices - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Model development best practices
- [Instructor] In this chapter, we will quickly review some of the recommended best practices for building and serving AI solutions. We start off with model development best practices. AI and machine learning have changed the way we build software. Data was never a key part of software development. With machine learning, training data has a crucial role to play in building AI solutions. Treat training data like software code. Keep data under version control. Use tools like DVC for this. Also, make sure that all processing done on data is repeatable by keeping track of the code that acquires, cleans, and transforms data. Machine learning models also evolve over time. Treat them also as code. Keep track of the version of models built and the associated hyper parameters. Models should be re-creatable. Track performance of machine learning models as you conduct experiments with data and hyper parameter tuning. Periodically evaluate results of experiments and improve and reassess progress.…
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