Model Versioning

The core serialized file containing the learned parameters (weights and biases) of the neural network. This is the mathematical representation of the model's knowledge. For PEFT models, this often refers to the frozen base model (e.g., LLaMA-3-8B) and the separate, much smaller adapter weights (e.g., a LoRA file). The artifact's format (e.g., SafeTensors, ONNX, TorchScript) is critical for serving compatibility.

The exact version of the model architecture definition, training script, and any pre/post-processing logic used to produce the version. This is typically captured via a git commit hash or container image digest. For PEFT, this includes the code defining the adapter architecture (e.g., rank for LoRA) and the merging/injection logic. Without this, the weights file is unusable.

Metadata identifying the specific training dataset, validation dataset, and any fine-tuning data used. This includes dataset names, version hashes, and splits. For PEFT, this is crucial as the adapter is only meaningful in the context of the data it was tuned on. Provenance enables auditing for bias, license compliance, and debugging performance regressions.

The complete set of experimental settings that defined the training run. This includes:

• Core hyperparameters: learning rate, batch size, optimizer type.
• PEFT-specific configs: adapter rank (r), alpha, target modules.
• Training duration: number of epochs/steps.
• Random seeds: for full reproducibility. These are often stored in a structured file (e.g., YAML, JSON) alongside the model.

The quantitative and qualitative results that justify promoting this version. This includes:

• Performance metrics: Accuracy, F1, loss on hold-out validation sets.
• Business metrics: Impact on key performance indicators (KPIs).
• Bias/fairness assessments: Results of specific evaluation suites.
• Inference benchmarks: Latency (p95), throughput, memory footprint. These reports provide the objective evidence for version comparison.

The administrative and relational data that contextualizes the version. Essential fields include:

• Unique version ID: Often a semantic version (v2.1.0) or hash.
• Author and creation timestamp.
• Parent version: The base model or previous version this was derived from.
• Stage: Development, Staging, Production, Archived.
• Dependencies: Framework versions (PyTorch 2.1), library versions. This metadata is the record of the model's lifecycle, managed by a Model Registry.

PEFT model versioning must track two distinct, interdependent artifacts: the frozen base model and the trainable adapter module.

• Base Model: Versioned by its original pre-trained checkpoint (e.g., meta-llama/Llama-3.1-8B), framework, and quantization state.
• Adapter Module: Versioned independently, storing its architecture (e.g., LoRA rank r=8), trained weights, and hyperparameters. A complete PEFT model version is a composite reference, such as Base:llama-3.1-8B-v1 + Adapter:finance-chat-lora-v3. This separation allows multiple adapter versions to be tested against a single base model.

A dedicated adapter registry is required to manage the proliferation of lightweight modules. This registry tracks:

• Adapter ID: A unique hash or tag (e.g., lora_finance_20241027).
• Base Model Dependency: Explicit link to the specific base model version it was trained on.
• Training Data Snapshot: Versioned reference to the dataset used for fine-tuning.
• Performance Metrics: Task-specific scores (e.g., accuracy on a validation set) for comparison.
• Parent Adapters: Lineage for adapters fine-tuned from other adapters, creating a directed graph of incremental improvements. This granular tracking is essential for reproducibility and rollback.

Inference serving of PEFT models requires runtime composition, where the base model and selected adapter are dynamically combined. This impacts versioning in production:

• Multi-Adapter Inference Servers: Systems like vLLM or Triton with PEFT support must load one base model and can hot-swap between many registered adapters.
• Versioned Endpoints: A serving endpoint URL may encode the adapter version (e.g., /predict/{base_model}/{adapter_version}).
• Consistency Checks: The serving system must validate adapter-base compatibility (e.g., architecture, dimensionality) at load time to prevent runtime errors from version mismatches.

Monitoring must be applied to both the input data and the adapter's specialized behavior. Since the base model is frozen, performance degradation is almost always tied to the adapter's domain.

• Feature Space Drift: Monitor the distribution of inputs specific to the adapter's task (e.g., financial jargon for a finance adapter).
• Adapter-Output Drift: Track the statistical properties of the adapter's activations or the final model outputs.
• Base Model as Control: Use the frozen base model's performance on a general benchmark as a stable baseline to isolate drift to the adapter component. This allows for targeted retraining of the adapter, not the entire model.

Deployment strategies must account for the dual-component nature. A rollback may involve reverting only the adapter.

• Atomic Swaps: Deploying a new adapter version should be an atomic operation to prevent serving a partially composed model.
• Canary Releases for Adapters: Route a percentage of traffic to a new adapter-v2 while adapter-v1 remains primary, comparing performance metrics in real-time.
• Fast Rollback: If adapter-v2 fails, traffic can be instantly re-routed back to adapter-v1 without restarting the base model, minimizing downtime. This makes blue-green deployment patterns highly effective for PEFT model updates.

The storage strategy must efficiently handle many small adapter files and their dependencies.

• Efficient Delta Storage: Adapters are small (often <1% of base model size). Storage systems should use delta encoding to store only the differences between adapter versions, saving significant space.
• Immutable Artifact Store: Both base model checkpoints and adapter weights must be stored immutably in a system like an ML artifact store (e.g., MLflow Artifact Repository, S3 with versioning).
• Dependency Manifest: Each adapter artifact should include a machine-readable manifest (e.g., a config.json) that declares its exact base model dependency, library versions (e.g., peft==0.11.0), and other environment details for full reproducibility.

An artifact store is a versioned storage system for immutable outputs generated by machine learning pipelines. It is a foundational dependency for reproducible model versioning.

• Stores binary artifacts like trained model weights (.safetensors, .bin), serialized preprocessing objects, evaluation reports, and dataset snapshots.
• Ensures immutability and traceability; each artifact is referenced by a unique hash or URI.
• Common implementations use object storage services like Amazon S3, Google Cloud Storage, or Azure Blob Storage, often integrated with tools like MLflow or DVC.

CI/CD for ML (Continuous Integration and Continuous Delivery/Deployment) extends software engineering practices to automate the testing, building, and deployment of machine learning models and their associated pipelines.

• Continuous Integration (CI) automatically tests new model code, data schemas, and training scripts upon commit.
• Continuous Delivery (CD) automates the packaging of a validated model version and its environment into a deployable artifact.
• Key for versioning as it creates a reliable, automated pathway for new model iterations to progress from development to production.

A canary release is a deployment strategy where a new model version is initially rolled out to a small, controlled subset of live traffic or users to monitor its performance and stability before a full rollout.

• Mitigates risk by limiting the impact of a potentially faulty new model version.
• Requires robust monitoring of key performance indicators (KPIs), latency, and error rates for the canary group compared to the baseline.
• Informs the rollback decision; if metrics degrade, traffic is routed back to the stable version, and the new version is investigated.

Shadow deployment is a safe validation pattern where a new model version processes live input requests in parallel with the production model, but its predictions are logged and not returned to end-users.

• Zero-risk validation allows for performance comparison on real-world data without affecting the user experience.
• Measures predictive performance (e.g., accuracy drift) and operational metrics (e.g., latency) under true production load.
• Essential for high-stakes models in finance or healthcare, where direct A/B testing carries unacceptable risk.

Drift detection is the automated process of monitoring production model inputs and outputs to identify significant changes in data distribution (data drift) or the input-output relationship (concept drift).

• Triggers model version review; persistent drift is a key signal that a new model iteration may be required.
• Uses statistical tests (e.g., Kolmogorov-Smirnov, PSI) or ML-based detectors to compare live data against a training/reference baseline.
• Integrates with monitoring dashboards to alert engineers when drift thresholds are exceeded.