Machine Learning Operations (MLOps) is the engineering discipline that combines DevOps, Continuous Integration / Continuous Deployment (CI/CD), data versioning, and live model monitoring to move ML systems from notebook prototype to reliable production service. At hjLabs.in we build enterprise MLOps platforms on Kubeflow, MLflow, BentoML, Seldon and SageMaker with Feature Store integration, drift detection, and automated retraining loops. We deploy production models in 4–8 weeks — closing the gap that strands 87% of AI projects before launch. Below: capabilities, stack, pricing from ₹5L, and FAQs.
Working systems you can open right now, without talking to us. There are no client logos below and no testimonials — we will not publish proof we cannot substantiate. What we can put in front of you is software hjLabs.in built, deployed and keeps running in public. Open it, push on it, and judge the engineering yourself before you spend time on a call.
Not prototypes and not a portfolio page — thirteen separate tools built, deployed and kept running by hjLabs.in, each on its own subdomain. Click any of them and see for yourself.
Every model in the catalogue is integrated, loadable and running client-side through WebGPU across 22 task types — audio classification, background removal, chat, depth estimation, embeddings, fill-mask, image captioning, classification and segmentation, NER, object detection, OCR, question answering, sentiment, speech-to-text, summarization, text-to-speech, translation and the zero-shot variants. That integration and upkeep is exactly the work MLOps is bought for.
TrendMaster, a Transformer architecture for stock price prediction, has 279 GitHub stars. hjalgos_notebooks has 110, o1-meta-prompt 47, PersonalGoalAssistant 22, and AutoCut — an Arduino-based automatic wire cutting machine — 6. Code quality is not something you should have to take on trust from a vendor.
We are a small specialist team, not a large consultancy, and we are not going to dress up as one. Here is what that actually buys you.
There is no account-manager layer between you and the person writing the code. The engineer who takes your scoping call is the engineer on your project, and stays reachable for the length of it.
hjLabs.in designs, manufactures and sells production machines — CNC routing, selective soldering, wire cutting, spring coiling, transformer winding. When a vision model has to survive a real factory line, cameras, lighting, PLCs and vibration are our day job, not a subcontractor’s.
Live tools you can open, open-source repositories you can read, and a standards implementation you can check line by line against the DICOM specification. You do not have to take a claim on trust when you can go and test it.
87% of AI projects never make it to production. The gap between a working model and a production system is massive. Our MLOps consulting services bridge this gap by providing:
of data science projects never make it to production
average time to deploy without MLOps
with proper MLOps infrastructure
End-to-end MLOps as a service — deployment infrastructure for production AI
Build complete automated pipelines from data ingestion to model deployment with CI/CD best practices.
Real-time performance tracking, automated alerting for model degradation, and drift detection systems.
Automated testing, retraining triggers, canary deployments, and rollback mechanisms for ML systems.
Expert deployment on AWS SageMaker, Google Vertex AI, Azure ML, or on-premise infrastructure.
Implement MLflow, Weights & Biases, or custom registries for experiment tracking and model versioning.
Reduce inference costs through model quantization, pruning, distillation, and infrastructure right-sizing.
Data prep, feature engineering, model training, experiment tracking
Unit tests, integration tests, model validation, A/B testing
Containerization, API creation, scaling, load balancing
Performance tracking, drift detection, automated retraining
Best-in-class MLOps tooling
AWS SageMaker · Google Vertex AI · Azure ML Studio · Databricks
Kubeflow · Airflow · Metaflow · Prefect
MLflow · Weights & Biases · Neptune.ai · Comet
Docker · Kubernetes · BentoML · Seldon Core
Deploy models that process millions of transactions per second with automatic retraining on new fraud patterns.
Challenge: 99.99% uptimeScale personalization engines to handle millions of users with A/B testing and gradual rollouts.
Challenge: Low-latency predictionsDeploy IoT sensor models with edge computing, periodic updates, and centralized monitoring.
Challenge: Edge deploymentDeploy object detection and image classification models processing thousands of images per second.
Challenge: Cost optimizationTrack every experiment, hyperparameter, and metric systematically
Trigger retraining based on data drift or performance degradation
Real-time dashboards for accuracy, latency, throughput, and errors
Audit trails, compliance tracking, and explainability frameworks
Handle traffic spikes automatically with horizontal scaling
Instant rollback to previous model versions if issues detected
Battle-tested write-ups from real deployments:
Senior specialists. Transparent scope.
Senior specialists. Transparent scope.
Senior specialists. Transparent scope.
/month retainer
"Fantastic AI engineer with pragmatic business and technical skills. Great to work with. An asset to any team."
Production ML has different requirements in healthcare than in e-commerce. We build MLOps pipelines tailored to your industry's compliance, scale, and performance needs.
HIPAA-compliant ML pipelines for diagnostic models, patient risk scoring, and drug discovery — with full audit trails and model governance.
Low-latency MLOps for fraud detection, credit scoring, and algorithmic trading — with real-time monitoring and automatic rollback on performance drops.
MLOps for recommendation engines, demand forecasting, and dynamic pricing — with auto-retraining triggered by sales events and seasonal shifts.
MLOps for predictive maintenance, visual quality inspection, and yield optimization — deployed on-premise or hybrid cloud for factory environments.
Route optimization and ETA prediction models running in production — with continuous monitoring and retraining as traffic patterns evolve.
MLOps for churn prediction, network anomaly detection, and usage-based pricing models — serving millions of events per second at low cost.
Stop letting great models languish in notebooks. Get them to production in weeks, not months.
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