Launched stuff on ProductHunt. Open-sourced some things. Developed some nice products.
By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.
Figures on the scope of the problem vary. Forbes and Venturebeat talk about 87%, Gartner claims 85%. Less important than a precise figure is the fundamental fact behind them: Getting ML projects from research to production is hard.
There is a disconnect between machine learning being done in Jupyter notebooks on local machines and actually being served to end-users to provide some actual value. ML in production has a ways to go to catch up to the quality standards attained by more conventional software development.
Here is one take on what is broken:
In traditional software development, code is (rightly-so) a first-class citizen. In ML development, there is a further need for data to be a first-class citizen as well. Therefore, data has to be treated with the same care that most developers give to the code they write.
Right now in most organizations, data is spread everywhere and inaccessible. This is not just about raw data either-even if an organization spends a lot of money into centralizing their data into lakes, critical data is spread across the organization in Google Colabs, notebooks, scripts and pre-processed flat files. This causes, amongst other things:
Teams often find it surprising when a well-trained model starts to give spurious results in the real world. The transition from training a model to serving it is far from trivial.
Firstly, there is a skew in training and production data, that needs to be taken into account. Secondly, one has to be very careful in making sure that production data goes through the same preprocessing steps in production as in training. Lastly, while training involves running experiments and quickly iterating, serving has even further requirements on the application level, e.g. inference time and costs at scale. All these need to be taken into account to avoid unnecessary surprises when the transition from training to serving happens.
Applying DevOps principles for ML development (or MLOps) is all the rage right now. However, there is yet no gold standard for it. The field in its infancy needs to tackle:
In conventional software development, we use workflows that integrate tickets and version control to make collaboration as seamless and transparent as possible. Unfortunately, ML development still lags behind on this front. This is largely due to the fact that ML developers tend to create silos which include glue-code scripts, preprocessed data pickles, and Jupyter notebooks. While all these are useful for research and experimentation, they do not translate well into a robust, long-running, production environment.
In short, in the ML world, there is largely:
Most of the problems highlighted above can be solved by proper attention being paid to machine learning development in production, from the first training onwards. The field is catching up, slowly but surely, but it is inevitable that machine learning will catch up with traditional software engineering quickly. Will we see new, even improving, and exciting ML products in our lives at that point? Let's hope so!
Our attempt to solve these problems is ZenML, an extensible, open-source MLOps framework.
We recently launched and are now looking for practitioners to solve their problems in production use-cases! So, head over to GitHub https://github.com/maiot-io/zenml, and don't forget to leave us a star if you like what you see!
Previously published at https://blog.maiot.io/deep_learning_in_production_is_broken/
Create your free account to unlock your custom reading experience.