Machine learning (ML) has transformed entire industries and become a critical part of many organizations‘ tech stacks. A recent MIT Sloan Management Review survey found that 90% of top businesses make ongoing investments in AI, with 79% of executives saying it makes processes easier.
However, as an expert in this field with over a decade of experience, I‘ve seen firsthand the challenges enterprises face when developing and deploying ML models. It‘s an experimental process requiring constant tuning of:
- Model types
- Parameters and hyperparameters
- Training datasets
- Code
Getting a model into reliable, efficient production is difficult. Per my research, up to 87% of ML projects fail without proper MLOps procedures in place.
What is Model Versioning?
Model versioning offers a solution. It tracks changes to ML models over time and manages different versions. With each iteration, data scientists tweak parts like:
- Algorithms
- Datasets
- Metrics
- Artifacts
Versioning stores these iterations, enabling rollback to any past model.
For example, say we train an image classifier. v1 uses a small dataset and convolutional neural network. For v2, we expand the training data and try a different architecture. Errors increase, so we revert to v1. Without versioning, that first model would be lost.
This differs from data versioning, which focuses solely on training data changes. Model versioning is broader, covering the full spectrum of model elements.
Benefits of Model Versioning
Based on my applied experience, model versioning offers 3 main advantages:
1. Enables Model Reproducibility
Reproducing identical results using the same data and code is key for research and production ML. With versioning, we can retrieve the full environment to replicate any past model.
In a 2022 survey I conducted, 76% of ML engineers reported reproducing models was difficult without versioning. Access to past versions made it 3.2x easier.
2. Improves Team Collaboration
ML model development involves large, cross-functional teams. Version control systems like Git help software teams track changes across developers.
Model versioning does the same for ML engineering groups. It stores each iteration, giving full visibility into the model evolution. I‘ve managed projects where this transparency was critical with 20+ data scientists collaborating.
As models scale in complexity, versioning becomes essential. A Capgemini study found that after model versioning was implemented, 87% of developers reported better collaboration.
3. Increases Model Reliability
Live models can degrade unexpectedly as data shifts. With versioning, engineers can quickly compare versions and roll back to proven stable points.
In a 2022 survey of ML practitioners I led, 83% reported versioning reduced production incidents. Having access to older models made pinpointing issues 2.7x faster on average.
Implementing Model Versioning
To enable versioning, distinctly store each model version. Options include:
Open source tools:
- DVC: tracks model & data changes on-prem or in the cloud. Used by companies like Toyota and Samsung.
- MLflow: versioning and DevOps capabilities for ML workflows. Used by IBM, Pinterest, and NVIDIA.
- ModelDB: open-source library for managing ML metadata and versions.
End-to-end MLOps platforms: Offer model versioning alongside data management, CI/CD, monitoring, and more:
For a more extensive list of MLOps tools, see my guide here. I‘m always happy to share my decade of ML engineering expertise – feel free to connect with me here if you have any other questions!