MLOps eliminates manual toil and mistakes working with multiple versions of data preprocessing and feature engineering pipelines, datasets, respective train/validation/inference source code, and models by automation and high-level orchestration.
Why would you want to do MLOps?
If your team works on multiple DS/AI directions and/or approaches, it will face the chaos of versions of data preprocessing, datasets, features, and models in no time. You will also be surprised to discover that Data Scientists are not as good as you want them to be at infrastructure works – for instance, making Kubernetes deployments for training and operationalization of ML models.
MLOps is a long-term investment too.
The ROIs are the following:
- Fast and reliable iterations of experiments.
No need to rewrite Python/Java/R code on every iteration.
Set up train and validation workflows once, automate them with MLOps, and toggle hyperparameters from now on.
- Flexible scaling of computational resources.
There are times when computational resources are in high demand.
But your team doesn’t need them spinning during the night or a weekend, for example.
And there is always somebody who forgot to shut down that expensive GPU instance – if the shutdown was not automated, of course.
Do you like bills for something that wasn’t used on purpose? Hope you don’t.
- Consistent track of experiments’ results.
Hyperparameters are aligned with validation results and logged automatically.
They always forget about it, you should know.
- Seamless integration and deployment.
Your service requires iterative updates.
Believe us, you don’t want to do that manually.
- Risk mitigation.
Compare new ML models to the existing ones, detect and avoid input drift.
Eliminate human mistakes at all stages of the ML roadmap.
- Automatic monitoring and feedback loop.
The live model scoring environment and the model training environment are distinct.
As a result, test environment scores are likely to be different from those in reality.
This risk mitigation exposes the model to the actual environment as a canary deployment. Observing and recording real-world performance metrics and predictions allows detecting corner cases, iterating, and developing a better model.
Without human intervention.