End-to-End MLOps Platform Implementation: Full ML Model Lifecycle

End-to-End MLOps Platform Implementation: Full ML Model Lifecycle

A data scientist trained a gradient boosting model with ROC-AUC 0.97, precision 0.92, recall 0.88 on a local Jupyter notebook. A week later, in production, precision dropped to 0.65, recall to 0.55 – the data had changed, and the model wasn't retrained. Training-serving skew, data drift, manual deployment without versioning – typical symptoms of missing MLOps. Over 5 years, we've implemented MLOps platforms for 15+ teams and guarantee that your model will perform in production exactly as it did on the laptop. The platform solves three key problems: experiment reproducibility, feature consistency, and automated monitoring.

How an MLOps Platform Bridges the Gap Between Development and Production

The core issue is the lack of a Model Registry and Feature Store. Without them, data scientists lose track of experiments, and engineers lose version control. An MLOps platform enforces discipline: every experiment is logged, every model is versioned, every feature is computed once. An MLOps platform integrates a stack of tools for the entire lifecycle: data → experiments → training → deployment → monitoring.

• Feature Store (Feast) – features are computed once for training and inference, eliminating training-serving skew.
• Model Registry (MLflow) – model versioning, promotion workflow (Staging → Production), automatic rollback on metric degradation.
• Automated Pipelines (Kubeflow) – retraining triggered automatically when new data arrives, without manual intervention.
• Monitoring (Evidently + Prometheus) – alerts on data drift or accuracy drop.

Without these components, each model risks becoming legacy within a week.

Which Components Are Included in the Basic MLOps Stack?

We start with a minimal viable set: MLflow for tracking and registry, S3 for artifacts, PostgreSQL for metadata. Then we add Feast for the Feature Store and Kubeflow for orchestration. Below is a detailed configuration of each component.

MLflow: Setting Up Tracking Server and Model Registry

We deploy an MLflow Tracking Server with S3 artifact store and PostgreSQL backend. This takes 1–2 weeks and provides: experiment logging, model registry, promotion workflow.

import mlflow import mlflow.sklearn from mlflow.models import infer_signature mlflow.set_tracking_uri("http://mlflow.mlops.svc.cluster.local:5000") mlflow.set_experiment("fraud-detection-v2") with mlflow.start_run(run_name="lgbm-baseline") as run: mlflow.log_params({"n_estimators": 500, "learning_rate": 0.05, "max_depth": 6}) model = LGBMClassifier(**params) model.fit(X_train, y_train) y_pred = model.predict(X_test) mlflow.log_metrics({ "precision": precision_score(y_test, y_pred), "recall": recall_score(y_test, y_pred), "f1": f1_score(y_test, y_pred), "roc_auc": roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]) }) signature = infer_signature(X_train, model.predict(X_train)) mlflow.sklearn.log_model(model, artifact_path="model", signature=signature, registered_model_name="fraud-detection") mlflow.log_figure(plot_feature_importance(model), "feature_importance.png") mlflow.log_artifact("shap_values.html") print(f"Run ID: {run.info.run_id}") from mlflow.tracking import MlflowClient client = MlflowClient() best_run = client.search_runs(experiment_ids=[experiment.experiment_id], order_by=["metrics.f1 DESC"], max_results=1)[0] model_version = mlflow.register_model(f"runs:/{best_run.info.run_id}/model", name="fraud-detection") client.transition_model_version_stage(name="fraud-detection", version=model_version.version, stage="Staging", archive_existing_versions=False) client.transition_model_version_stage(name="fraud-detection", version=model_version.version, stage="Production", archive_existing_versions=True) 

Feature Store: Eliminating Training-Serving Skew with Feast

We add Feast with online storage on Redis/Hazelcast. Now historical features for training and the same features via REST API for inference – without skew.

from feast import FeatureStore store = FeatureStore(repo_path="./feature_repo") training_df = store.get_historical_features( entity_df=entity_df_with_timestamps, features=["customer_stats:transaction_count_7d", "customer_stats:avg_amount_30d", "merchant_stats:fraud_rate_90d"] ).to_df() online_features = store.get_online_features( features=["customer_stats:transaction_count_7d", "customer_stats:avg_amount_30d"], entity_rows=[{"customer_id": "12345", "merchant_id": "MCC001"}] ).to_dict() 

Kubeflow: Automating Pipelines

We deploy Kubeflow Pipelines for automatic retraining on trigger (new file in S3) or on a schedule. Training runs on GPU pools with virtualization.

Monitoring with Evidently and Prometheus

We integrate Prometheus and Grafana to collect metrics: latency, throughput, GPU utilization. For data drift, we use Evidently.

import evidently from evidently.report import Report from evidently.metric_preset import DataDriftPreset, ClassificationPreset report = Report(metrics=[DataDriftPreset(), ClassificationPreset()]) report.run(reference_data=training_data, current_data=production_data_last_week) report.save_html("drift_report.html") data_drift_score = report.as_dict()["metrics"][0]["result"]["dataset_drift"] if data_drift_score: alerts.send("Data drift detected", severity="warning") 

Why We Choose a Self-Hosted Stack on Kubernetes?

Self-hosted gives full control over data and configuration, and at the scale of 5+ models, it is significantly cheaper than managed solutions. Below is a comparison:

Important: Monitoring must be implemented from day one, not after an incident.

What Is Included in Deliverables?

We hand over:

• Documentation: architecture diagram, deployment instructions, API specifications.
• Access: configured MLflow, Feast, Grafana, kubectl context.
• Training: 2–3 workshops on working with MLflow and Feast.
• Support: 2 weeks of post-deployment support.

Timelines and Cost

Cost is calculated individually – depends on integration complexity and data volume. Estimated timelines:

Typical Mistakes and How to Avoid Them

• Skipping the Feature Store – teams start with pipelines, and training-serving skew kills accuracy. We always start with MLflow + Feast.
• Lack of model versioning – manual deployment of model.pkl leads to chaos. Model Registry is mandatory.
• Ignoring monitoring – data drift is detected only through customer complaints. Evidently must run from day one.

Get a consultation on MLOps implementation – we will select the optimal stack for your project. We'll assess your project and propose an MLOps platform architecture tailored to your needs. Contact us to receive an estimate and roadmap.