How can you manage model versioning and governance in machine learning deployment?

Governance is key from an organizational view to safeguard IP and digital assets. Increasingly, ML models become strategic assets, reflecting substantial investments and it's too risky to let knowledge of them hinge on a few staff members. Documenting model evolution and final forms helps reduce this vulnerability. At the same time, thorough documentation, standardized development processes and protocols for sharing of models help leverage the investment for other purposes throughout the organization. Instead of seeing ML models as technical tools to automate a particular function, see them as repositories that represent knowledge about the company's core business.

Reproducibility of model behaviour is key in analysing and debugging model performance. That requires data and model lineage. Dataset and model versioning facilitate that. You can compare how the training data and ML model changed to perform differently for a slice of data

Model versioning and governance are essential for ensuring the reliability, transparency, and accountability of machine learning models. By implementing robust model versioning and governance practices, organizations can minimize the risk of errors, bias, and misuse of their models.

Replicability relies on having a strict lineage of data and code back to the reference data. This allows you to pinpoint the cause for different behaviour due to the latest changes in the pipeline.

Proving that AI systems are effective differs from the typical software quality assurance. Model versioning allows you to test your AI system at multiple stages - during training, validation and continuously through its life. Governance is critical when automating the tests.

Machine learning models may experience performance drops due to data drift and other factors. To maintain model performance and keep up with changing data, it's essential to retrain new models with recent data. It also critical to ensure the performance of latest model and transition them from development to production environment gradually using proper MLOps pipeline with testing procedure. Utilizing frameworks like MLFlow is a streamlined approach to versioning and governance which saves time and resources for organizations while reducing the overhead of building a solution from scratch.

Based on my experience git is useful when code versioning is required but in case of model binaries it becomes difficult to manage such large files. Mlflow is quite popular to version model binaries, parameters, accuracy matrices, datasets etc.

Fundamentally, you can version artifacts by organizing them in different folders and tagging them with appropriate names but it can be cumbersome and inefficient. 1. Dependency Versioning using Pip, Conda, and Poetry: Pinning versions is important so that the code doesn’t break with installing the latest packages. 2. Code Versioning using GitHub 3. Data/Model/Config Versioning using DVC: DVC connects Git with cloud storage to version data. A model is a combination of weights and their hyperparameters, so we store both using DVC. 4. Config Management using Hydra: Create nested and distributed configuration instead of having a single YAML file defining parameters. 5. Experiment Versioning with ML Flow, Weights & Biases, Tensorboard

Life of a data scientist is very much similar to how is model's weights are updated and that is iteration 😊.Iteration of experiments takes up a significant portion of a data scientists time .To this effective model versioning is important .And not just for iternation ,but even for production MLOPS mannagemt model version is crucial .MLOPS framework such as MLFLOW ,KUBEFLOW, AIRFLOW ,Triton from NVIDIA and MLOPS frameworks of hyperscalers provides for it .You can use them as per your needs and environment.

Save everything into version control: - Queries that generated the data - References to _immutable_ file based datasources - Any code that you use either in training or inference Tag every inference log with model commit hash (or the branch name) for monitoring purposes. This way you will know every information about a model and its use at evaluation time.

To govern ML models effectively these are the best approach to follow I can suggest: 1. Document model details. 2. Use version control for code and data. 3. Implement a model registry. 4. Continuously monitor model performance. 5. Ensure data quality and privacy. 6. Rigorously validate models. 7. Record validation results. 8. Address bias and fairness. 9. Prioritize model security. 10. Regularly retrain models. 11. Comply with regulations. 12. Ensure model explainability. 13. Define a clear model lifecycle. 14. Document decision-making. 15. Foster collaboration. 16. Prioritize transparency. 17. Prepare for disaster recovery. These practices enhance transparency, reliability, and accountability in ML model development and deployment.

Before your solution goes to production, legal teams at organizations check if your solution is fair. For a person detection model, you should prove that the model is unbiased towards say different genders (Male/Female). Likewise, for Credit Scoring model, you should prove that the model is unbiased towards different age groups i.e., the model should not indicate, for example, that elderly people are more likely to default. To assess fairness in the first example, we require labelled data (person bounding boxes with Gender attributes). Then we can compare the following for Male and Female annotations: 1. Model confidence 2. Average Recall 3. TPR, FNR etc. Note that there are no FPs in this case as Ground Truth can either be TP or FN.

Strice data and code lineage allow accurate observability. Model evaluation is just another controlled step in the model workflow. Work together with stakeholders to identify risk factors, create test data for these and record the results in a controlled environment. Replicate the above workflow for deployed models in production and record the results in your data warehouse for further analysis.

Effective governance of machine learning models involves establishing clear policies for ethical AI use, maintaining compliance with legal standards, implementing robust model monitoring for performance and bias, and ensuring accountability with detailed documentation and decision logs. It's crucial to integrate these practices with organizational processes and regularly review them to adapt to evolving regulatory landscapes and stakeholder needs, fostering transparency and trust in AI applications.

Model versioning and governance is a discipline rather than a set of tools. You can do perfectly good model versioning and governance with a GitHub account and an SQLite database.

**Versioning Tools:** - Git: For code versioning. - DVC: For data and pipeline versioning. - MLflow: For model tracking. - Weights & Biases: For experiment tracking. - TensorBoard: For visualization and tracking model training. **Governance Techniques:** - Standard Operating Procedures (SOPs): To ensure consistent model development and deployment. - Auditing and Logging: For keeping track of model decisions and data usage. - Model Validation Frameworks: To enforce performance thresholds and fairness checks. - Access Controls: To regulate who can view or alter models and data. - Continuous Monitoring: To oversee model performance and drift over time.

Model versioning, monitoring and governance is a process issue, not an automation issue. Establish well-defined processes before you start automating them. Focus on outcomes not output.

Data versioning for training data ,incremental training data for Finetuning ,prompts data for prompt learning ,cross validation data,RAG embedding data are equally important .

- Documentation: Maintain comprehensive documentation for every model version, including development context, changes, and decision-making rationale. - Training and Skills Development: Ensure that team members are trained in best practices for model versioning and governance. - Stakeholder Engagement: Regularly involve stakeholders to align model development with business objectives and ethical considerations. - Impact Assessment: Conduct regular assessments of the models' impact, considering potential risks and unintended consequences. - Change Management: Implement a robust change management process to handle updates and modifications to models systematically.