ceo@innomlopssolutions.com | contact@innomlopssolutions.com UK-Based | Global Reach

Our Services

End-to-end MLOps solutions for production ML systems

MLOps Engineering

Problem

Data science teams struggle to move models from notebooks to production. Manual deployment processes are error-prone, models drift without detection, and there's no systematic way to version experiments or reproduce results.

Solution

We build complete MLOps pipelines that automate the entire ML lifecycle:

  • Automated training pipelines with Airflow and Kubeflow
  • Experiment tracking and model versioning with MLflow
  • Feature stores for consistent data access
  • Automated model validation and testing
  • CI/CD pipelines for ML code and models
  • Model registry and deployment automation

Tools Used

MLflow, Airflow, Kubeflow, DVC, Great Expectations, Feast

Outcome

Reduce model deployment time from weeks to hours. Ensure reproducibility and enable rapid experimentation with confidence.

Cloud Architecture (AWS / Azure / GCP)

Problem

ML workloads require specialized infrastructure - GPU instances, distributed training, scalable inference, and cost optimization. Managing this across cloud providers is complex.

Solution

We design and implement cloud-native ML infrastructure:

  • Multi-cloud architecture design and implementation
  • Kubernetes clusters (EKS, AKS, GKE) for ML workloads
  • Auto-scaling inference endpoints
  • Cost optimization and resource management
  • Infrastructure as Code with Terraform
  • Serverless ML with Lambda, Cloud Functions

Tools Used

AWS SageMaker, Azure ML, GCP Vertex AI, Terraform, Kubernetes, Docker

Outcome

Scalable, cost-effective infrastructure that grows with your needs. 99.9% uptime SLA with automated failover.

AI/LLM Systems

Problem

Large language models and AI systems require specialized deployment strategies, prompt engineering, and integration with existing systems.

Solution

Production-ready AI and LLM deployments:

  • LLM deployment and optimization (GPT, Claude, Llama)
  • RAG (Retrieval Augmented Generation) systems
  • Vector databases and semantic search
  • Custom fine-tuning and model adaptation
  • API development with FastAPI
  • Prompt engineering and optimization

Tools Used

OpenAI API, Hugging Face, LangChain, Pinecone, Weaviate, FastAPI

Outcome

Intelligent AI systems that integrate seamlessly with your applications and deliver measurable business value.

DevOps & CI/CD

Problem

Traditional DevOps doesn't account for ML-specific needs like data versioning, model testing, and gradual rollouts.

Solution

ML-aware DevOps practices:

  • CI/CD pipelines for ML code and models
  • Automated testing (unit, integration, model performance)
  • GitOps workflows for infrastructure and models
  • Blue-green and canary deployments for models
  • Container orchestration and management
  • Secrets management and security

Tools Used

GitHub Actions, Jenkins, GitLab CI, ArgoCD, Docker, Kubernetes

Outcome

Reliable, automated deployments with rollback capabilities. Reduce deployment failures by 90%.

Quant / Data Engineering

Problem

ML models are only as good as their data. Poor data quality, inconsistent features, and slow data pipelines limit model performance.

Solution

Robust data infrastructure for ML:

  • Scalable data pipelines with Spark and Kafka
  • Real-time and batch processing
  • Data quality monitoring and validation
  • Feature engineering and feature stores
  • Quantitative analysis and backtesting frameworks
  • Data versioning and lineage tracking

Tools Used

Apache Spark, Kafka, Airflow, dbt, Feast, Great Expectations

Outcome

Clean, reliable data pipelines that feed your models with high-quality features in real-time.

Monitoring & Observability

Problem

Models degrade over time due to data drift, concept drift, and changing patterns. Without monitoring, you won't know until it's too late.

Solution

Comprehensive ML monitoring:

  • Model performance tracking and alerting
  • Data drift detection
  • Prediction monitoring and analysis
  • Infrastructure metrics (latency, throughput, errors)
  • Custom dashboards with Grafana
  • Automated retraining triggers

Tools Used

Prometheus, Grafana, ELK Stack, Evidently AI, WhyLabs

Outcome

Proactive detection of issues before they impact users. Maintain model performance over time with automated interventions.

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