Authentrics AI - MLOps Platform

Authentrics MlOps platform turns checkpoint history into a centralized, auditable MLOps workspace where teams can manage projects, track model changes, persist analyses, and coordinate corrective interventions across the model lifecycle.

Addressing Core MLOps Lifecycle Challenge

• Expensive Retrain after Model Regression: A corrupted training epoch, failed fine-tuning run, task interference, catastrophic forgetting or behavioral regression can force teams to repeat hours or days of GPU-intensive training. Authentrics enable targeted model correction and optimization without rebuilding the entire model pipeline.
• Machine Unlearning (GDPR / CCPA) and Data Removal Requirements: Privacy requests, revoked datasets, licensing changes, or poisoned/backdoor training data may require removing learned influences. Authentrics enables removing these targeted influences from the final model with a very low compute intervention while preserving desired model behaviour and reducing the need for full retraining.
• Difficult to Diagnose Model Drift/Regression: Traditional evaluation benchmarks/metrics can show that model quality declined/behavior changed with no insight to what changed internally or when the regression was introduced. Authentrics audit functions across checkpoints help teams localize the source of behavioural drifts.
• Pre-Deployment Model Quality Assurance Beyond Static Evals: Traditional evaluation suites measure model behavior against a fixed set of prompts, benchmarks, or metrics. A model can pass those evaluations while still carrying hidden regressions, unstable weight changes, task interference, or behavior that only appears outside the tested cases. Authentrics complements static evals with drift scores of weight shifts and intermediate activations, giving teams additional structural evidence of how the model changed before it reaches production.
• Data Residency & IP Constraints: Third-party evaluation violates security boundaries by requiring weight/data uploads. Authentrics maintains 100% data residency by running all analysis locally inside your VPC or local machine. Runs Fully On-prem

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1. Project & Checkpoint History Management: Organize models, projects, training runs, checkpoints, analyses, and corrective interventions in one persistent workspace. Teams can review how models evolved over time and reproduce prior analysis and remediation workflows.
2. Multi-User Team Workflows: Provide shared access to model projects and analysis history so engineering, MLOps, security, and governance teams can collaborate around the same model lifecycle records.
3. Parameter & Activation Auditing (static_analysis & activation_analysis): Tracks parameter-level weight changes and layer-by-layer deltas between checkpoints. By evaluating behavioral drift in intermediate layers, it can flag parameter drift and catch silent regressions before deployment.
4. Fine-Tuning Attribution & Correlation(correlation_analysis): Attributes final inference outputs back to intermediate layers and specific fine-tuning sessions. It localizes which layers are driving specific model behaviors or regressions, and provides percentage-based training session attribution to trace behaviors back to individual epochs or training data slices.
5. Machine Unlearning (exclude_training): Instantly suppress the influence of specific training sessions, poisoned batches, or revoked datasets from a checkpoint in-place. This enables rapid, compliant model edits and restores model function while avoiding full retraining pipelines.
6. Zero-Training Optimization & Maintenance (ZTOM): Adjusts checkpoints back to proper alignment gradient-free without backpropagation. ZTOM leverages historical checkpoint data to optimize model accuracy against custom loss functions, suppressing issues like catastrophic forgetting, task interference, or subgroup regressions.
7. Isolated Cluster Deployment: Deploy Authentrics as a containerized service inside an isolated cluster, providing centralized model operations while keeping proprietary checkpoints, weights, and training data within your security boundary.

To understand more on how address these use cases:

1. Authentrics Colab Notebook Demonstations 
2. Python SDK Samples 
3. Backdoor Data identification and Surgical Removal without Retrain 
4. Understanding Expert Routing in mistralai/Mixtral-8x7B and Training Dynamics Nvidia Nemotron 8B