MLOps includes model management. At scale, models based on machine learning should be robust and fulfill all business needs. A rational, convenient model management policy is required to make this happen. The construction, learning, versioning, and distribution of machine learning models are all handled by ML model management.
When working on new machine learning models or applying them to new domains, researchers do a lot of tests using different optimizers, model architectures, loss functions, variables, parameters, and inputs. These experiments are used to find the optimum model configuration.
However, if you don’t have a mechanism to monitor model configurations and performance between tests, all hell may break loose because you won’t be able to tell and pick the best answer. Keeping tabs on all trials and findings, even if it’s only one researcher researching separately, is difficult.
That is why you manage models. It enables you, your team, and your company to:
- Address typical company challenges in a proactive manner.
- Track data, code, metrics, and model versioning to enable repeatable experimentation.
- To facilitate reusability, package and provide models in recurring configurations.
Parts of ML Model Management
ML model architecture consists of following parts:
- Version control systems – assist machine learning model development in managing changes to source code. Data version management, on the other hand, is a collection of model management tools and methods that aims to adapt the version control process to the data world in order to manage model changes in connection to datasets and vice versa.
- Code checkpointing – changes to the model’s source code are managed here
- Experiment Locator – It tracks, collects, and organizes model training/validation data/performance over several runs with various configurations and datasets.
- Model Registry – is a comprehensive model monitoring system for ML models that have been trained, staged, and deployed.
- Model Observation – It’s used to keep track of the model’s inference performance and spot any symptoms of Serving Skew, which occurs when data changes lead the deployed model’s performance to fall below the score/accuracy it had in the training environment.
Importance of ML Model Management
Model management is an essential component of any machine learning workflow. It makes it easy to oversee the ML model lifecycle management from model generation through model deployment, including configuration, experimentation, and tracking of different experiments.
It’s vital to note that we handle two things in Model Management:
- Models – packaging, deployment & tactics, monitoring, and retraining are all handled here.
- Experiments – This is where we keep track of loss, training metrics, photos, text, and any other information you have, as well as pipeline versioning, data, and code.
Data teams would have a difficult time generating, tracking, comparing, deploying, and replicating models without model management.
Ad-hoc approaches are an alternative to model management and can lead to researchers creating ML projects that are not repeatable, sustainable, scalable, or coordinated.
Here are some more considerations on importance of model management:
- Provides for a single source of information to be established;
- Allows for model versioning for standards and consistency;
- Problems (underfitting, overfitting, bias, or performance) are easier to mitigate, making the solution more traceable and compatible with laws.
- You can do research and development more quickly and effectively.
- Teams become more productive and have a strong sense of purpose.
- ML Through the use of various best practices and technologies, model management may promote cooperation around code, data, and documentation.