MLOps & AI infrastructure

Deploying and running infrastructure optimized for AI

In the age of AI, being able to deploy and run machine learning models in production efficiently has become a decisive competitive advantage. At OPSaaS, we help you build robust, scalable MLOps infrastructure optimized for the most demanding AI workloads, from classic machine learning models to large language models (LLMs).

Why adopt MLOps?

MLOps (Machine Learning Operations) brings clear benefits to your AI projects:

Faster time to market: Less time between experimentation and production
Reproducibility: Consistent results across environments
Governance: Full traceability of models and data
Scalability: Handle growing volumes of data and inference requests
Collaboration: Better teamwork between data scientists and engineers
Cost optimization: Efficient use of expensive compute resources
Advanced monitoring: Early detection of model drift

Our MLOps & AI infrastructure expertise

AI infrastructure design

We design infrastructure optimized for your AI workloads:

Analysis of your specific needs for training and inference
Technology selection suited to your use case
Right-sizing compute resources (CPU, GPU, TPU)
High-performance storage architecture for large datasets
Data and training pipeline design
Model deployment strategies for production
Scalability planning to support your growth

Deployment and orchestration

We deploy and orchestrate your AI workloads following best practices:

Kubernetes setup optimized for ML/AI workloads
GPU/TPU cluster configuration with efficient resource sharing
Full MLOps platform deployment (Kubeflow, MLflow, etc.)
Orchestration of training and inference pipelines
Automation of the model lifecycle
Integration with your existing CI/CD pipelines
Security for both infrastructure and models

Optimization and monitoring

We optimize and monitor your AI infrastructure:

Performance optimization for models in production
Advanced monitoring of technical and business metrics
Model and data drift detection
Infrastructure cost optimization
Load-based autoscaling
Efficient GPU/TPU resource management
Performance analysis and improvement recommendations

Technologies and frameworks

We cover the full MLOps and AI infrastructure ecosystem:

Platforms and orchestration

Kubeflow: End-to-end platform for ML workflows on Kubernetes
MLflow: ML model lifecycle management
Seldon Core: ML model deployment on Kubernetes
KServe: ML model serving on Kubernetes
Airflow: Orchestration of complex workflows
Argo Workflows: Kubernetes-native orchestration

GPU/vGPU infrastructure

NVIDIA GPU Cloud (NGC): AI-optimized containers
NVIDIA MIG: GPU partitioning for multi-tenancy
NVIDIA vGPU: GPU virtualization
AMD ROCm: Open source GPU ecosystem
TPU: Google accelerators optimized for TensorFlow

Frameworks and libraries

TensorFlow: Google’s end-to-end ML framework
PyTorch: Flexible ML framework originally developed at Facebook
Hugging Face: Ecosystem for NLP models
LangChain: Framework for LLM applications
Ray: Distributed computing for ML
ONNX: ML model interoperability

Monitoring and observability

Prometheus/Grafana: Technical metrics monitoring
TensorBoard: Visualization for TensorFlow
Weights & Biases: ML experiment tracking and monitoring
Evidently AI: Model quality monitoring
Seldon Alibi: ML model explainability
Great Expectations: Data validation

Advanced MLOps architectures

MLOps reference architecture

+------------------+     +------------------+     +------------------+
|  Data Pipeline   |---->|  Training        |---->|  Model Registry  |
|  - Ingestion     |     |  - Experiment    |     |  - Versioning    |
|  - Validation    |     |  - Hyperparameter|     |  - Metadata      |
|  - Transformation|     |  - Distributed   |     |  - Artifacts     |
+------------------+     +------------------+     +--------+---------+
                                                           |
                                                           v
+------------------+     +------------------+     +------------------+
|  Monitoring      |<----|  Serving         |<----|  Deployment      |
|  - Performance   |     |  - Inference     |     |  - Canary        |
|  - Drift         |     |  - Scaling       |     |  - A/B Testing   |
|  - Explainability|     |  - Caching       |     |  - Rollback      |
+------------------+     +------------------+     +------------------+

This modular architecture provides:

Clear separation of concerns
End-to-end automation
Full governance and traceability
Independent scaling of each component
Room to adopt new technologies

Infrastructure optimized for LLMs

For large language models (LLMs), we design specialized infrastructure:

High-performance GPU clusters with fast interconnects (NVLink, InfiniBand)
Memory optimization for large models (sharding, quantization)
Horizontal scaling for parallel inference
Smart caching to reduce latency
Cost optimization with autoscaling and spot instances
LLM-specific monitoring (latency, tokens/sec, etc.)

Use cases

MLOps platform for a retail company

A retail company wanted to industrialize its demand forecasting and recommendation models. We:

Deployed Kubeflow on a multi-cloud Kubernetes cluster
Set up automated data ingestion and preparation pipelines
Configured versioning for models and datasets
Implemented advanced monitoring with drift detection
Automated periodic model retraining

Results: Time to production for models cut from 6 weeks to 2 days, 15% better prediction accuracy, and 30% infrastructure savings.

LLM infrastructure for a generative AI startup

A startup building LLM-based applications needed infrastructure that was both fast and cost-effective. We:

Designed an optimized GPU architecture on a European cloud
Implemented quantization and other optimization techniques
Set up a smart caching layer
Configured demand-based autoscaling
Built monitoring for LLM-specific metrics

Results: 60% lower inference costs, latency down from 300ms to 80ms, and the capacity to handle 10x more concurrent requests.

Integration with your tech stack

MLOps and DevOps

We integrate your MLOps practices cleanly with your existing DevOps stack:

CI/CD: Extend your pipelines to cover ML workflows
Infrastructure as Code: Manage ML infrastructure with Terraform/Ansible
Containers: Consistent packaging of models and dependencies
Unified monitoring: ML metrics in your existing dashboards
GitOps: GitOps principles applied to ML models

MLOps and data engineering

We connect your data pipelines and your ML workflows:

Data lineage: Full traceability from data to predictions
Feature store: Centralized, reusable features
Data validation: Automated quality checks on input data
Metadata management: A catalog of datasets and models
Unified orchestration: Coordinated data and ML workflows

MLOps & AI infrastructure FAQ

What do we need in place before adopting MLOps?

Ideally, existing DevOps practices, a culture of collaboration between data scientists and engineers, and some maturity in your ML projects. That said, we adapt our approach to where you are today and can support you all the way from the foundations to operational excellence.

How do you optimize infrastructure costs for AI workloads?

Through several strategies: spot instances for training, autoscaling for inference, model quantization, efficient GPU sharing, and result caching. Applying FinOps to AI typically yields savings of 30% to 60%.

What is the difference between MLOps and DevOps?

MLOps extends DevOps principles to the specifics of machine learning: versioning data and models (not just code), monitoring model drift, experiment tracking and traceability, and managing specialized resources such as GPUs. Our approach covers these specifics while staying aligned with your DevOps practices.

How do you secure models and data in an MLOps pipeline?

Security in MLOps covers several areas: protecting sensitive data, hardening models against adversarial attacks, fine-grained access control, and auditing predictions. We take a security-by-design approach that addresses these concerns at every stage of the ML lifecycle.

Do we have to use Kubernetes for MLOps?

Kubernetes has become the de facto standard for large-scale MLOps infrastructure, but it is not always necessary, especially for teams just getting started. We take an incremental approach, starting with simpler setups (such as standalone MLflow) and moving to full Kubernetes-based platforms when your needs justify it.

Ready to move your AI projects forward with solid MLOps infrastructure?

Whether you are just starting with MLOps or looking to optimize existing infrastructure, our team of experts can help you build an MLOps platform that fits your needs.