Taming ML Pipelines: CI/CD, Monitoring, and Deployment
ML projects break not where most think. Not at the model training stage — data scientists work there for years and know what they do. They break at the delivery stage to production: the model is trained, metrics are good, but deploying it in Kubernetes, setting up drift monitoring, and establishing a retraining process — nobody knows how. A DevOps engineer looks at a .pkl file weighing 2GB and has no idea what to do next. A typical scenario: the team spends 70% of its time on manual deployment and monitoring, not on improving the model. Pipeline automation can reduce time-to-market from two weeks to two days and cut monthly infrastructure costs from $50,000 to $30,000.
Request a consultation — we will analyze your infrastructure and propose the optimal stack within 48 hours. Over five years, we have implemented MLOps in 15+ projects, from startups to enterprise, with 99.9% deployment success rate. Pipeline automation reduces operational costs by 30–50% monthly (e.g., from $50,000 to $25,000 for a mid-size team). Below is a breakdown of the pipeline, stack, and pitfalls.
Why ML Pipeline Differs from Standard CI/CD
In standard CI/CD, code is deterministic: the same commit on the same platform yields the same artifact. An ML pipeline is not. The result depends on data, hyperparameters, random seed, library version. pip install tensorflow without a fixed version yields different results on different days.
Everything needs versioning:
- Code — as usual, via git
- Data — via DVC (Data Version Control), lakeFS, or Delta Lake
- Configuration — hyperparameters, preprocessing steps
- Artifacts — model weights, feature encoders, scalers
- Environment —
requirements.txtwith pinned versions, or a Docker image
A reproducible experiment is one where from a git commit you can restore exactly the same model with the same metrics. Without this, production troubleshooting is impossible.
A Typical MLOps Stack
Standard stack for production ML on Kubernetes:
- Data Storage: S3/GCS (datasets up to 50TB) → DVC for versioning
- Experiment Tracking: MLflow Tracking Server (up to 1000 experiments per day)
- Model Registry: MLflow Model Registry or Weights & Biases Registry
- Training Pipeline: Argo Workflows or Kubeflow Pipelines
- Model Serving: Seldon Core, BentoML, or Triton Inference Server
- Monitoring: Evidently AI + Prometheus + Grafana
- Feature Store: Feast (optional, for online features)
Official MLflow documentation recommends versioning not only code but also the environment.
Why Data Drift Is the Main Enemy of ML in Production?
After model deployment, data distribution may change — this is data drift. For example, a credit scoring model was trained on pre-pandemic data, but after the pandemic, borrower behavior changed. Evidently AI measures drift per feature: if >30% of features drift, accuracy drops by 15-20% in a week, and retraining is triggered. Without monitoring, accuracy drops unnoticed, and the business loses money.
How to Ensure Reproducibility of ML Experiments?
Pin everything: code versions (git), data (DVC), configuration (hyperparams.yaml), environment (Docker with pinned versions). For each experiment, save metadata in MLflow: commit, parameters, seed, metrics. Only then is rollback to a working version possible.
CI/CD for ML Models
Example GitLab CI Pipeline
---
stages:
- validate
- train
- evaluate
- register
- deploy
validate-data:
stage: validate
image: python:3.11-slim
script:
- pip install -r requirements.txt
- dvc pull data/train
- python scripts/validate_data.py --schema schemas/training_data.json
artifacts:
reports:
junit: validation_report.xml
train-model:
stage: train
image: ${CI_REGISTRY_IMAGE}/ml-training:latest
tags: [gpu]
variables:
MLFLOW_TRACKING_URI: "https://mlflow.internal"
MLFLOW_EXPERIMENT_NAME: ${CI_PROJECT_NAME}
script:
- dvc pull data/
- python scripts/train.py --config configs/model_config.yaml --experiment-id ${CI_PIPELINE_ID} --commit ${CI_COMMIT_SHA}
artifacts:
paths:
- outputs/model/
evaluate-model:
stage: evaluate
script:
- python scripts/evaluate.py --model-path outputs/model/ --test-data data/test/ --threshold-config configs/quality_gates.yaml
rules:
- if: '$CI_PIPELINE_SOURCE == "merge_request_event"'
register-model:
stage: register
script:
- python scripts/register_model.py --model-path outputs/model/ --version ${CI_COMMIT_SHORT_SHA} --stage Staging
only:
- main
deploy-to-production:
stage: deploy
script:
- python scripts/promote_model.py --model-name ${MODEL_NAME} --from-stage Staging --to-stage Production
- kubectl apply -f k8s/model-deployment.yaml
when: manual
only:
- main
---
Quality gate is key. The model is not registered if metrics are below thresholds. Example config configs/quality_gates.yaml:
accuracy: 0.92 f1_score: 0.88 precision: 0.90 recall: 0.86 The check is implemented by the evaluate.py script, which compares actual metrics against thresholds and returns a status.
Model Serving in Kubernetes
Seldon Core deploys models as Kubernetes-native services and is up to 3x faster than TensorFlow Serving:
---
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
name: fraud-detection
namespace: ml-production
spec:
predictors:
- componentSpecs:
- spec:
containers:
- name: fraud-model
image: ${ECR_URL}/fraud-detection:${MODEL_VERSION}
resources:
requests:
memory: "2Gi"
cpu: "500m"
limits:
memory: "4Gi"
cpu: "2"
graph:
name: fraud-model
type: MODEL
name: default
replicas: 3
traffic: 100
---
A/B testing between versions — via traffic distribution among predictors.
Monitoring Drift and Degradation
Evidently AI generates reports on data drift and model performance. If the share of drifting features exceeds 30%, a retraining pipeline is triggered. Grafana dashboards display model_accuracy, data_drift_score, prediction_latency_p99, request_count — all metrics from Prometheus via Seldon Core metrics endpoint.
How We Implement MLOps: Step-by-Step Process
The implementation process is divided into four stages:
- Audit and Design — We analyze the current ML workflow, infrastructure, and SLA requirements. We design the architecture: GitOps, versioning, CI/CD pipeline. Audit takes 1-2 days, design up to a week.
- Stack Deployment — We install MLflow, Seldon Core, configure monitoring (Prometheus, Grafana, Evidently). We train the team on the tools.
- Pipeline Development — We write CI/CD pipelines in GitLab CI or Argo Workflows, integrate quality gates, set up autoscaling (Karpenter, HPA).
- Launch and Optimization — We deploy the first model, configure alerting in PagerDuty/Slack, document the runbook. We hand over the project for operation.
| Criterion | GitLab CI | Argo Workflows |
|---|---|---|
| Setup complexity | Low | Medium |
| DAG support | Limited | Full |
| Execution speed | High | Medium |
| Suitable for | Small teams | Enterprise |
What's Included
| Stage | What's Included | Timeline |
|---|---|---|
| Basic MLOps | MLflow, CI/CD pipeline, model serving, documentation | 6–8 weeks |
| Advanced | Drift monitoring, auto-retraining, A/B testing, training | 3–5 months |
| Mature practice | Feature store, online learning, full automation | 6–12 months |
Additionally: security audit, RBAC setup, integration with existing systems (Jira, Slack).
Why Implement MLOps with Us
We are a team with five years of experience in DevOps and MLOps. We have completed over 15 pipeline implementations for ML products. We use a proven stack, write documentation, and train your team. We guarantee SLA on response time — we sign a contract with liability.
Key advantages:
- Certified expertise: Kubernetes, GitOps, ArgoCD, Seldon Core
- Own pipeline templates — launch time reduced by 40%
- Work with AWS EKS, GCP GKE, Yandex Cloud, Selectel
- Individual approach: a solution tailored to your stack, not a forced template
Project cost typically ranges from $80,000 to $150,000 depending on scope, determined after an audit. Practical experience shows that MLOps automation reduces operational costs by 30–50% by minimizing manual work and incidents. Our clients report 40% reduction in incident count.
Reach out to us for a consultation. Get a project estimate within 48 hours.