MLOps is a set of practices that combines Data Engineering, Machine Learning and DevOps. The main aim of this union is reliable and efficient deployment and maintenance of ML systems in production.
MLOps practices and its benefits.
In this article, we will review Machine Learning features that automate and shorten the machine learning cycle.
- Rapid innovation through robust machine learning lifecycle management.
MLOps, or DevOps for machine learning, allows teams of data processing, analysis professionals and IT professionals to collaborate, as well as increase the pace of model development and deployment through monitoring, validation and management systems for machine learning models.
- Create reproducible workflows and models.
Reduce variation in model iterations and provide resiliency for enterprise-level scenarios with reproducible learning and models. Use dataset registries and advanced model registries to track resources. Provide improved traceability by tracking code, data, and metrics in the execution log. Create machine learning pipelines to design, deploy, and administer reproducible model workflows for consistent model delivery.
- Easy deployment of high precision models in any location.
Deploy quickly and confidently. Use automatic scaling, managed clusters of CPUs and GPUs with distributed learning in the cloud. Pack models quickly, ensuring high quality at every step through the use of profiling and model validation. Use managed deployment to migrate models to the production environment.
- Effective management of the entire machine learning life cycle.
Use the built-in integration with Azure DevOps and GitHub actions to plan, automate, and manage workflows efficiently. Streamline training and model deployment pipelines, use CI / CD to simplify retraining, and integrate machine learning easily into existing release processes. Use advanced data bias analysis to improve model performance over time.
- Machine Learning Resource Management System and Control.
Keep track of version history and model origin to enable auditing. The transparency of the model will allow you to evaluate the importance of features and create more advanced models with minimal bias using uniform distribution metrics. Set calculation quotas for resources and enforce policies to ensure compliance with security, privacy, and compliance standards. Create audit trails to meet regulatory requirements as you mark machine learning resources and automatically trace experiments.
- ML Pipelines.
One of the main concepts of Data Engineering is the data pipeline. A data pipeline is a cycle of transformations that are applied to data between its source and an end destination. They are usually explained as a graph in which each node is a transformation and edges represent dependencies or execution order.
- Hybrid Teams
We’ve already established that in order to be successful we need a mixed team that has that skill set. Most likely it would consist of a Data Scientist (ML Engineer), Data Engineer and DevOps Engineer.
It is important to understand that a Data Scientist alone can’t achieve the goals of MLOps.
- Model and Data Versioning
In a traditional software world you need only versioning code, because all behavior is determined by it. In ML things are a little different. In addition to the familiar versioning code we also need to track model versions, the data used to train it, and some meta-information like training hyperparameters.
- Model validation
ML models are harder to test than DevOps one, because no model gives absolutely accurate results. This means that model validation tests need to be surely statistical in nature, rather than having a binary pass/fail status.
It’s also not enough to track a one metric for the entirety of the validation set.
- Data validation
A good data pipeline usually starts by validating the input data. In addition to basic validations that any data pipeline performs, ML pipelines need higher level validation statistical properties of the input. For example, if the average diversion of a feature changes considerably from one training dataset to another, it will likely affect the trained model and its predictions.
For ML systems, monitoring becomes even more important than monitoring production systems. It is because their performance depends not just on factors that we have some control over, like infrastructure and our own software, but also on data, which we have less control over. Therefore, in addition to monitoring standard metrics like latency, traffic, errors and saturation, we also need to monitor model prediction performance.
As ML matures from research to applied business solutions, so do we need to improve the understanding of its operation processes.
The following table summarizes MLOps’ main practices and how they relate to DevOps and Data Engineering practices: