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.

MLOps: Infrastructure for Training, Deploying, and Monitoring ML Models

The model is trained, metrics — F1 0.94 on validation. Three months later in production, quality drops by 12%. No one knows when — there is no monitoring. It's impossible to retrain quickly — the training script is in a Jupyter notebook of a data scientist who has already left. Data for retraining is collected manually from three disparate systems. About half of the projects come to us with this pain. We build a turnkey MLOps platform: from experiment tracking to automatic deployment and data drift monitoring. We will assess your infrastructure in 1–2 weeks, and in 4–6 weeks you will get a basic MLOps core running in production. Our team has 10+ years of experience in ML infrastructure, over 50 implementations.

How does MLOps infrastructure benefit your ML projects?

Experiment Tracking and Reproducibility

Without tracking, an ML project turns into chaos: it's unclear which checkpoint is better, which hyperparameters were used, which dataset. Reproducing a result a month later is a quest.

Why is experiment tracking the foundation of reproducibility?

MLflow is an open source standard for tracking. It logs parameters, metrics, artifacts (models, graphs), and code. MLflow Model Registry is a centralized model storage with versioning and lifecycle stages (Staging → Production → Archived). Deployment via MLflow Serving or integration with external systems.

Typical initialization in code:

import mlflow

mlflow.set_experiment("fraud-detection-v2")
with mlflow.start_run():
    mlflow.log_params({"learning_rate": 3e-4, "batch_size": 64, "epochs": 10})
    mlflow.log_metric("val_f1", val_f1, step=epoch)
    mlflow.pytorch.log_model(model, "model")

This is the minimum. In production, we add logging of system metrics (GPU utilization, memory), dataset (hash, version), code (git commit hash). Weights & Biases — richer UI, collaboration features, sweep for hyperparameter optimization. MLflow — for on-premise deployment without external dependencies.

DVC (Data Version Control) — versioning of data and models on top of git. Data is stored in S3/GCS/Azure Blob, only metadata (hashes) in git. dvc repro reproduces the entire pipeline from raw data to metrics.

To ensure reproducibility of training, fix random seeds (torch.manual_seed, numpy.random.seed, random.seed) and record them in experiment metadata. Without this, debugging irregular results is painful. Log the dataset version (DVC hash) and git commit — then any experiment can be reproduced down to the byte.

Pipeline Orchestration: Kubeflow, Airflow, Prefect

A pipeline orchestrator becomes necessary when: A 100-line training script in cron is fine for simple tasks. But as soon as you have a multi-step pipeline (data loading → preprocessing → feature engineering → training → validation → deployment if quality above threshold), you need an orchestrator with retry logic, visualization, and alerts.

Kubeflow — Kubernetes-native orchestrator for ML (see Kubeflow). Each step is a Docker container. Supports parallel steps, conditional branches, artifacts between steps. Integrates with Katib (AutoML), KServe (serving), Feast (feature store).

Apache Airflow — more general DAG orchestrator. Wide ecosystem of operators (S3, Spark, DBT, Kubernetes). Easier to deploy if Airflow already exists in the company.

Prefect / Metaflow — less boilerplate. Prefect 2.x with @flow and @task decorators — quick start for small teams.

Typical training pipeline architecture on Kubeflow:

1. Data ingestion component — fetches data from S3/DB, validates schema via Great Expectations
2. Preprocessing component — transformations, normalization, train/val/test split
3. Training component — training on GPU, logging to MLflow
4. Evaluation component — metric calculation, comparison with baseline in Model Registry
5. Conditional deployment — deploy only if new model is better than current by >2% F1

Each component is a separate Docker image. Pipeline is versioned in git. Scheduled run (retraining once a week on new data) or manual.

Model Registry and Lifecycle Management

Model Registry is not just a checkpoint store. It is a centralized system that knows:

• Which model is currently in production (and with what metrics)
• History of all versions with training parameters
• Metadata: dataset, git commit, validation results
• Lifecycle stage: None → Staging → Production → Archived

MLflow Model Registry — standard. For enterprise — Vertex AI Model Registry (GCP), SageMaker Model Registry (AWS), Azure ML Model Registry.

Model promotion through stages: automatically move model to Staging after successful eval, then manual or automatic (during A/B test) promotion to Production. Rollback — switch to previous Production version in seconds.

Serving: From FastAPI to Triton Inference Server

Simple case. FastAPI + PyTorch/ONNX on one server — 80% of production ML deployments are exactly that. Sufficient for most tasks with load up to 100 req/s.

from fastapi import FastAPI
import onnxruntime as ort

app = FastAPI()
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

@app.post("/predict")
async def predict(request: PredictRequest):
    inputs = preprocess(request.text)
    outputs = session.run(None, {"input_ids": inputs})
    return {"label": postprocess(outputs)}

Triton Inference Server — production standard for high loads (500+ req/s). Dynamic batching, concurrent model execution, model ensemble. Supports TensorRT, ONNX, PyTorch TorchScript, TensorFlow SavedModel.

KServe — Kubernetes-native ML serving with autoscaling, canary deployments, A/B testing out of the box. Scale-to-zero for inactive models — savings on infrastructure up to 40% annually for a project with 10 models.

Monitoring: Data Drift, Model Drift, Infrastructure Metrics

Monitoring — what is usually done last and regretted first. Three levels.

Infrastructure monitoring. Latency (P50/P95/P99), throughput (req/s), error rate (4xx, 5xx), GPU/CPU utilization. Prometheus + Grafana — standard. Alert when P99 latency > threshold or error rate > 1%.

Data drift monitoring. Distribution of input data changes over time. Detect via PSI (Population Stability Index) for numerical features: PSI > 0.2 — strong drift. Chi-squared test for categorical, Kolmogorov-Smirnov test for continuous. Evidently AI — open source library with ready-made drift tests.

Model drift monitoring. If ground truth is delayed (e.g., we know conversion after a week) — monitor real metrics. If not — surrogate metrics: distribution of prediction scores, proportion of confident predictions.

Alerting. Three levels: INFO (minor drift, log it), WARNING (significant, notify team), CRITICAL (quality dropped below threshold — automatic switch to fallback model).

Why is data drift monitoring important?

Without it, you learn about model degradation only from user complaints or ringing SLA. A drift alert allows you to retrain the model in advance, before errors start causing losses. In one of our projects, PSI monitoring detected drift 2 days after a data source change — this saved the campaign.

Feature Store

Feature Store solves the training-serving skew problem. If preprocessing during training and inference is implemented in two different places — divergence is inevitable.

A Feature Store is needed when:

• Several models use the same features
• Features are computed from streaming data (real-time)
• Large team with different people on feature engineering and model training

Feast — open source Feature Store. Offline store (S3 + Parquet) for training, online store (Redis, DynamoDB) for low-latency inference. Feature definitions as code, materialization job syncs offline → online.

Tecton (commercial), Vertex AI Feature Store (GCP), SageMaker Feature Store (AWS) — managed options with less ops overhead.

CI/CD for ML

ML CI/CD is regular CI/CD plus specific ML steps.

ML-specific checks in CI:

• Reproducibility check: run training with a fixed seed, result must match
• Data validation: Great Expectations or Pandera on schema/distribution checks
• Model performance check: automatic eval on holdout, block merge if degradation > threshold
• Latency regression test: inference must meet SLA

GitOps for deployment. Merge to main → CI triggers training → eval → if passes → automatic deployment to Staging → smoke tests → manual promotion to Production or automatic upon successful canary.

Tools: GitHub Actions / GitLab CI for CI, ArgoCD for GitOps deployment on Kubernetes.

What's Included in MLOps Platform Development

We provide a full cycle of work, documentation, and team training.

Total time from audit to full MLOps platform: 3–5 months. Also possible phased launch: basic level (tracking + serving) in 4–6 weeks.

Cost is calculated individually based on data volume, number of models, and infrastructure requirements. Order an MLOps infrastructure audit — get a roadmap in 1–2 weeks. Contact us for a project assessment — we will send a preliminary estimate within 2 business days.

Note: warranty on architectural solutions — 12 months. We provide integration certificates with major cloud providers (AWS, GCP, Azure). During our work, we have not lost a single client after the first implementation — the experience of 50+ successful MLOps projects speaks for itself. Get a consultation on building an MLOps platform today.