Research-to-Production & MLOps
Reproducible pipelines, packaged inference, cloud architecture, validation, and monitoring for models that need to become release artifacts — not just promising notebooks.
From working model to release artifact
A model that works in a notebook is only the beginning. Production teams need reproducible training, versioned data, packaged inference, deployment environments, monitoring, rollback paths, and evidence that the system behaves as expected after release. TensorHarmony helps close the gap between experimental models and operational software — whether the destination is a SaaS product, internal platform, or regulated medical-imaging workflow.
Reproducible training & retraining
Training pipelines that can be rerun, reviewed, and defended — with experiments, environments, and data treated as release-critical artifacts.
- Pinned environments and data versioning
- Experiment tracking and model lineage
- Reproducible seeds, splits, and evaluation runs
- Retraining triggers tied to data updates
- Fresh-machine reproducibility checks
Inference & release packaging
Model delivery as a versioned, testable release artifact — not a loose collection of scripts, weights, and undocumented assumptions.
- Containerized inference services
- Versioned model artifacts with checksums
- Release notes mapped to validation evidence
- Structured logging for review and operations
- Rollback and degradation strategies
Cloud architecture for AI workloads
Deployment architecture for training, inference, storage, queues, environments, and cost-aware operation across cloud infrastructure.
- AWS / GCP architecture for training and inference
- GPU cost discipline for batch and online workloads
- Dev / staging / production isolation
- Secrets, IAM, and key-management hardening
- Continuity and disaster-recovery basics
Monitoring & post-release behavior
Once a model ships, teams need visibility into latency, drift, failures, input distributions, and whether behavior still matches expectations.
- Inference logging with privacy-aware redaction
- Drift and distribution-shift monitoring
- Outcome and feedback-loop design
- Periodic revalidation against release endpoints
- Dashboards for technical and clinical reviewers
Production engagement flow
Audit the real system
We review the actual codebase, training scripts, data flow, model artifacts, and infrastructure to identify the gap between the current state and a release-ready system.
Harden the release path
We prioritize reproducibility, packaging, environment separation, deployment automation, monitoring, and validation evidence around the system’s real risks.
Transfer ownership
The deliverable includes documentation, decision logs, handover notes, and operational guidance so your team can maintain the pipeline after the engagement.
Move From Model to Production System
Discuss pipelines, packaging, inference, monitoring, validation, and release discipline for production-grade AI systems.
Focused on systems that can be tested, deployed, and maintained