Your Models Work in Notebooks. They Fail in Production.
87% of ML models never make it to production. The ones that do often decay within months. We build the MLOps infrastructure that gets your models deployed, monitored, and continuously improving.
Deployment Time
Model Uptime
Drift Detection
Retrain Cycle
MLOps Challenges We Solve
If any of these resonate, we should talk
"Our data scientist built a great model, but it's been 6 months and it's still not in production"
The notebook works perfectly. But deploying it? That requires infrastructure, APIs, monitoring, and skills your ML team doesn't have. We bridge the gap between experimentation and production.
"We deployed the model but now it's wrong more often than it's right"
Model decay is silent and deadly. Without proper monitoring, you won't know your model is failing until business metrics tank. We implement drift detection and automated retraining pipelines.
"Every model deployment is a custom snowflake project"
Your ML team spends 80% of their time on infrastructure and 20% on actual ML. We build self-service platforms that let data scientists deploy models without DevOps tickets.
"We can't reproduce our training results or debug production issues"
Which version of the data? Which hyperparameters? Which dependencies? Without proper experiment tracking and versioning, ML becomes a black box. We bring reproducibility and auditability.
What We Believe About MLOps
Six years of deploying models has taught us what actually works
The Real Problem Isn't ML - It's Operations
Most organizations hire brilliant data scientists and then wonder why models never make it to production. The bottleneck isn't model quality - it's the lack of engineering infrastructure to deploy, monitor, and maintain models at scale.
MLOps isn't a tool you buy. It's a capability you build. The organizations winning with ML have invested in platforms that make deployment routine, not heroic.
"A good model in production beats a great model in a notebook. Every time."
We've built MLOps platforms for regulated industries where model failures have real consequences. That experience shapes how we think about reliability, governance, and operational excellence.
Production is the Only Metric
A model that isn't deployed is a model that isn't delivering value. Optimize for production velocity.
Monitoring Before Training
Build observability first. You can't improve what you can't measure in production.
Automate Everything
Manual deployments don't scale. Every step should be automated, versioned, and repeatable.
Data Scientists Should Ship
Self-service platforms empower ML teams to deploy without waiting for DevOps.
Models Are Perishable
Every model decays. Build retraining pipelines from day one, not after the model fails.
The DaasLabs MLOps Methodology
A proven path from notebook to production in 8 weeks
Assess
Audit current state & gaps
Deliverables
- MLOps maturity assessment
- Infrastructure review
- Tool recommendations
Design
Architecture & platform design
Deliverables
- Platform architecture
- CI/CD pipeline design
- Monitoring strategy
Build
Platform implementation
Deliverables
- ML platform deployment
- Feature store setup
- Model registry config
Deploy
First model to production
Deliverables
- Pilot model deployment
- Monitoring dashboards
- Runbook documentation
Operationalize
Scale & continuous improvement
Deliverables
- Team training
- Self-service enablement
- Operational playbooks
MLOps Capabilities That Ship
End-to-end services to productionize and scale your ML
ML Platform Engineering
Self-service platforms that let data scientists deploy without DevOps tickets.
- Platform architecture design
- Infrastructure automation (IaC)
- GPU cluster management
- Multi-tenant environments
ML CI/CD Pipelines
Automate training, testing, and deployment with production-grade pipelines.
- Automated training pipelines
- Model validation gates
- A/B testing frameworks
- Blue-green deployments
Model Monitoring
Catch model decay before it impacts business outcomes.
- Performance monitoring
- Data drift detection
- Concept drift alerts
- Automated retraining triggers
Feature Store
Centralize features for consistency between training and inference.
- Feature registry & discovery
- Online/offline serving
- Point-in-time correctness
- Feature versioning
Experiment Tracking
Make every experiment reproducible and comparable.
- MLflow/Weights & Biases setup
- Hyperparameter tracking
- Model registry
- Artifact management
ML Governance
Deploy AI responsibly with audit trails and compliance built in.
- Model documentation (Model Cards)
- Lineage tracking
- Bias detection & fairness
- Regulatory compliance
MLOps Tools We Work With
Deep expertise across the modern MLOps ecosystem
ML Platforms
Orchestration
Model Serving
Why Companies Choose DaasLabs for MLOps
See how we compare to your other options
vs. Big Consulting Firms
They'll assess your maturity for 3 months. We'll have your first model in production in 8 weeks. Our engineers build platforms, not PowerPoints.
vs. Building In-House
MLOps engineers are expensive and scarce. It takes years to build institutional knowledge. We bring battle-tested patterns and accelerate your journey by 12+ months.
vs. Managed ML Platforms
Cloud ML services are generic and often lead to vendor lock-in. We build platforms tailored to your stack, your workflows, and your governance requirements.
What Our Clients Achieve
10x Faster Deployment
From months to days for model deployments
99.9% Model Uptime
Production-grade reliability with SLA guarantees
60% Less ML Ops Time
Data scientists focus on ML, not infrastructure
Full Reproducibility
Every experiment and deployment is traceable
Ready to Get Models into Production?
Let's discuss your ML infrastructure challenges. We'll give you an honest assessment of your MLOps maturity and a roadmap to production-grade ML operations.