Build an Enterprise MLOps Pipeline
Set up a production-grade ML pipeline on cloud infrastructure with experiment tracking, model versioning, automated evaluation, and deployment monitoring.
Download workflow config (JSON)How the data moves
This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
Set Up Experiment Tracking
Integrate Weights & Biases into your training code. Log hyperparameters, metrics, and artifacts for every training run. Tag experiments by team member and objective so results are searchable across the team.
Step 2
Choose Your Model Serving Platform
Select your cloud model serving stack: Amazon Bedrock for AWS-native teams, Azure OpenAI for Microsoft shops, or Google Vertex AI for GCP. Standardize your team on one platform to simplify compliance and billing.
Step 3
Add LLM Observability
Connect LangSmith to your LLM application layer for full trace logging. Set up dashboards that track output quality, latency, and cost per model across all production requests.
Step 4
Build the Evaluation Pipeline
Create a dataset of representative test cases in LangSmith. Run evaluations as part of every deployment to confirm the new model or prompt doesn't regress on your quality metrics.
Step 5
Set Up Automated Alerts
Configure alerts in Weights & Biases for: model performance dropping below baseline, inference latency exceeding SLA, and unusual cost spikes. Route alerts to Slack for immediate visibility.
Time Required
1–2 weeks
Expected Result
A reproducible ML pipeline where every experiment is tracked, every model version is documented, and quality regressions are caught automatically before production deployment.
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