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Xavier Amatriain (Curai)
Co-founder and CTO at Curai. Previously: VP of Engineering at Quora, led Algorithms Engineering at Netflix.
Lessons Learned From Building Practical Deep Learning Systems
- More data is preferred when we have access to more features and our models have low-bias.
- Better models is preferred when the space of our feature set has low dimensions.
- Transfer learning lowers the need for access to data. In order to use this method effectively, we want to fine-tune the pre-trained models on better data.
- Occam's Razor: Given two models that perform more or less equally, you should always prefer the less complex.
- Deep learning might not be preferred, even if it squeezes an increase of 1% accuracy.
- Reasons to use simple models include scalability, system complexity, maintenance, explainability, etc.
- More complex features may require a more complex model.
- A more complex model may not show improvements with a feature set that is too simple.
- A well-behaved Machine Learning feature should be reusable, transformable, interpretable, and reliable.
- In deep learning, architecture engineering is the new feature engineering.
- Most fascinating results in recent years come from a combination of the two approaches (stacked autoencoders, unsupervised pre-training, etc.).
- Self-supervised learning is a learning paradigm where we train a model using labels that are naturally part of the input data, rather than requiring separate external labels.
- Most practical applications of machine learning run an ensemble. You can use completely different approaches at the ensemble layer.
- Ensemble resembles the way to turn any model into a feature!
- Biases can happen in the data labels, or even in the presentation to end-users.
- Introducing biases leads to a lack of fairness in machine learning.
- Two desired properties of models in the wild are:
- Easily extensible: incrementally/iteratively learn from "human-in-the-loop" or from additional data.
- Knows what it does not know: model uncertainty in prediction and enable fall-back to manual.
- Evaluation metrics used during offline and online experiments must match!
- A/B tests help measure differences in metrics across statistically identical populations that each experience a different algorithm.
- Use long-term metrics whenever possible.
- Short-term metrics can be informative and allow faster decisions.
- You should apply the best software engineering practices during the design of machine learning systems (encapsulation, abstraction, cohesion, low coupling, etc.).
- However, design patterns for machine learning software are not well-known or documented.
- Whenever you develop any ML infrastructure, you need to target two different modes:
- ML experimentation that emphasizes flexibility, reusability, and ease of use.
- ML production that adds on a new layer of performance and scalability.
- In order to combine them:
- Research should be done using tools that are the same in production.
- Abstraction layers should be implemented on top of the optimized research code so they can be accessed from friendly experimentation tools.
- Examples of other ML approaches include XGBoost, tensor methods, factorization machines, non-parametric Bayesian methods, etc.
- Sometimes, deep learning methods do not outperform these simpler approaches.