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COMPLECT
Machine Learning Operations
Ship Models to Production.
Ship Models to Production.
At Scale. With Confidence.
Most ML models never make it to production—and those that do often degrade silently. Complect's MLOps practice brings DevOps discipline to the ML lifecycle: automated pipelines, model registries, continuous training, drift monitoring, and production-grade governance so your AI investments deliver real business value.
10×
Faster Model Deployment
90%
Reduction in Manual ML Ops
100%
Model Lineage & Audit Trail
40+
ML Models Deployed in Production
What is MLOps?
MLOps — The Bridge Between Data Science and Production
MLOps is the practice of applying DevOps, DataOps, and ML engineering principles to the machine learning lifecycle. It covers everything from data versioning and experiment tracking through to automated training pipelines, model deployment, A/B testing, and production monitoring.
Without MLOps, data science teams spend the majority of their time on manual, error-prone operational tasks—re-training models by hand, deploying via ad-hoc scripts, and discovering model degradation only after it impacts business metrics. With MLOps, these processes are automated, auditable, and repeatable.
- Automate the entire ML pipeline from data ingestion to model serving
- Track experiments, models, and datasets with full versioning and lineage
- Monitor model performance and data drift in production continuously
- Enforce governance, compliance, and bias checks across all models
End-to-End Pipeline
The Complete MLOps Lifecycle
We design and implement every stage of your ML pipeline—from raw data to business-ready predictions in production.
Data Engineering
Versioned data pipelines, feature extraction & validation
Experimentation
Tracked experiments, reproducible notebooks & HP tuning
CI/CT Pipeline
Automated training triggers on data or code changes
Model Registry
Centralised versioned registry with approval workflows
Model Serving
REST/gRPC endpoints, shadow deployments & A/B testing
Monitoring
Drift detection, data quality alerts & retraining triggers
Service Offerings
Our MLOps Capabilities
From platform architecture to model governance, our MLOps engineers bring production ML expertise built across finance, e-commerce, healthcare, and SaaS verticals.
ML Pipeline Automation
Design and implement end-to-end automated ML pipelines using Kubeflow Pipelines, Apache Airflow, MLflow, or ZenML. Eliminate manual steps from data preprocessing through model training, evaluation, and registration.
Model Versioning & Registry
Implement centralised model registries with MLflow, DVC, or Weights & Biases. Track every model artifact, its training data version, hyperparameters, and performance metrics—enabling full reproducibility and audit trails.
Continuous Training (CT)
Build automated retraining pipelines triggered by data drift, schedule, or performance threshold breaches. Implement champion/challenger testing so new models are only promoted when they demonstrably outperform the incumbent.
Model Drift & Data Monitoring
Deploy production monitoring for concept drift, data drift, and prediction distribution shifts using Evidently AI, Whylogs, or Arize. Trigger automated alerts and retraining workflows when model health degrades.
Feature Store Design & Build
Architect and implement centralised feature stores using Feast, Tecton, or Vertex AI Feature Store. Ensure consistent feature computation between training and serving environments, eliminating training-serving skew.
ML Infrastructure on Kubernetes
Deploy Kubeflow, Ray, or Seldon Core on Kubernetes for scalable, GPU-accelerated model training and serving. Configure autoscaling inference endpoints, multi-model serving, and resource isolation for ML workloads.
Cloud ML Platform Setup
Implement managed MLOps on AWS SageMaker, Google Vertex AI, or Azure ML. Configure training clusters, model registries, endpoint management, and cost controls so teams can focus on model quality, not infrastructure.
Model Governance & Compliance
Implement model cards, bias audits, explainability reports (SHAP, LIME), and approval workflows. Ensure AI systems meet regulatory requirements (GDPR, DPDP, SEBI, HIPAA) with complete audit trails and documentation.
LLM & GenAI Operations
Operationalize large language models and generative AI applications with prompt versioning, evaluation pipelines (RAGAS, LangSmith), fine-tuning workflows, and cost-optimised inference serving using vLLM or TGI.
MLOps Maturity
We Meet You Where You Are
Whether you're running ML for the first time or scaling a mature platform, our engagement model adapts to your MLOps maturity level.
Level 0 — Manual
Data Science Exploration
Your team runs experiments in notebooks. We build your first reproducible pipeline, establish experiment tracking, and create a path to production deployment.
Level 1 — Automated Training
CT Pipeline & Model Registry
You have models in production but manage them manually. We automate your training pipelines, implement a model registry, and add production monitoring with drift alerts.
Level 2 — Full MLOps
CI/CD/CT + Feature Store + Governance
You run multiple models at scale. We architect a full MLOps platform with feature stores, automated A/B testing, governance workflows, and organisation-wide self-service ML capabilities.
Technology Stack
MLOps Tools & Platforms We Use
MLflow
Kubeflow
DVC
ZenML
Weights & Biases
Apache Airflow
Feast
Tecton
Seldon Core
Ray
AWS SageMaker
Google Vertex AI
Azure ML
Evidently AI
Arize
vLLM
LangChain
LangSmith
Kubernetes
Docker
Python
Spark
Ready to Put Your ML Models to Work?
Let Complect's MLOps engineers assess your current ML maturity and design a production-ready platform that scales with your data science ambitions.
Get Your MLOps Roadmap Get Your MLOps Roadmap
