MLOps, LLMOps and Agentic AI
From Notebooks to Production.
From Notebooks to Production.
AI and ML That Actually Ships.
Automated model pipelines, GPU-optimised serving, LLMOps infrastructure, and Agentic AI orchestration built for AI and ML platforms that need to move fast without breaking things. Production-grade MLOps delivered in 90 days.
60%
Faster Model Deployments
Sub-100ms
LLM Inference Latency
70%
GPU Cost Reduction
100+
Models in Production
Zero
Model Drift Downtime
Agentic
AI Ready to Deploy
The Transformation
What Stonetusker Delivers
End-to-end MLOps and LLMOps infrastructure that takes your team from notebook chaos to production-grade AI delivery. Built around your stack, your models, and your scale targets from day one.
01
MLOps Platform and Pipelines
Kubeflow or MLflow on Kubernetes with experiment tracking, model registry, feature store, and full CI/CD for model deployment via Git push.
02
LLMOps and Inference Infrastructure
vLLM and TensorRT-LLM serving with vector databases for RAG, LoRA fine-tuning pipelines, prompt versioning, and multi-model routing with fallback strategies.
03
GPU Infrastructure and FinOps
GPU-optimised training on AWS, Azure, and GCP with spot instance automation, model quantisation, multi-tenancy, and transparent cost attribution per model and team.
04
Model Monitoring and AIOps
Drift detection, performance degradation alerts, automatic retraining triggers, predictive incident prevention, and A/B testing with automatic winner deployment.
05
Agentic AI Orchestration
LangChain and AutoGen agent frameworks with multi-agent collaboration, memory management, tool integration, and sandboxed code execution for autonomous AI systems.
06
Data Platform and Governance
Delta Lake and Iceberg lakehouses, real-time feature pipelines with Kafka and Flink, ML security controls, bias detection, and GDPR-compliant federated learning.
Days 1 to 30
01
MLOps Foundations. End Notebook Chaos.
Replace ad-hoc training scripts and manual deployments with reproducible pipelines, experiment tracking, and automated model versioning from the very first week.
- Comprehensive ML infrastructure audit covering training workflows, deployment gaps, data bottlenecks, and current toolchain limitations across your platform. Complete ML lifecycle visibility. Baseline DORA metrics for AI platforms.
- MLOps platform deployment with Kubeflow or MLflow on Kubernetes including experiment tracking, run comparison, and artifact management for every training job. 1000 plus experiments tracked automatically. Any model reproducible in minutes.
- Model registry and versioning with automated artifact management so every model version is tracked like code with instant rollback capability to any previous state. Version every model like code. Rollback to any previous version instantly.
- Feature store implementation with Feast or Tecton providing consistent features between training and inference to eliminate train-serve skew across all models. Train-serve skew eliminated. 90% fewer production bugs from data issues.
- Automated data validation pipelines with Great Expectations monitoring data quality gates at every stage to catch bad data before it enters training jobs. Bad data caught before training. Garbage-in-garbage-out prevented at source.
- GPU-optimised training infrastructure on AWS, Azure, or GCP with spot instance automation and elastic GPU scaling for cost-effective experimentation at any scale. 70% training cost reduction. Elastic GPU scaling for every experiment.
- CI/CD for ML models with automated testing, validation gates, and deployment pipelines so any model can be pushed to production via a standard Git workflow. Models deployed via Git push. Complete audit trail for compliance.
- Distributed training setup with Horovod or PyTorch DDP enabling large model training across multiple GPUs and nodes with automated job orchestration. 10B plus parameter models trained. Multi-GPU orchestration automated.
- Hyperparameter optimisation automation with Optuna or Ray Tune integration replacing manual grid search with intelligent, parallel hyperparameter exploration. 10x faster hyperparameter search. Optimal models discovered automatically.
- Model lineage tracking capturing data provenance, code versions, and hyperparameters for every model in production so every decision can be traced back to its source. Complete reproducibility. Every production model traced to source data.
Days 31 to 60
02
LLMOps and Production Serving.
Deploy large language models at scale with GPU-optimised inference, vector databases for retrieval-augmented generation, and real-time feature pipelines that keep models fresh under load.
- LLM deployment infrastructure with vLLM or TensorRT-LLM for high-throughput inference, delivering sub-100ms response latency at 5x higher throughput than naive model serving. Sub-100ms LLM responses. 5x higher throughput than naive serving.
- Vector database deployment with Pinecone, Weaviate, or Qdrant for retrieval-augmented generation applications with millisecond semantic search across billions of embeddings. Millisecond semantic search. Context-aware LLM responses at scale.
- LLM fine-tuning pipelines with LoRA and QLoRA for domain-specific model adaptation, delivering custom language models in days rather than months at 90% less compute. Custom LLMs in days. 90% less compute than full fine-tuning.
- Model serving infrastructure with Triton Inference Server or Seldon Core on Kubernetes capable of serving 100 plus models simultaneously with auto-scaling on request load. 100 plus models served simultaneously. Auto-scaling on request load.
- LLM prompt versioning and management with a prompt registry and systematic A/B testing so prompt performance is tracked and optimised as a first-class engineering concern. Prompt performance tracked. Response quality optimised systematically.
- Multi-model routing with intelligent fallback strategies routing requests to GPT-4, Claude, or self-hosted models dynamically based on cost, latency, and availability targets. Route across providers dynamically. Cost and latency targets always met.
- Real-time feature engineering pipelines with Kafka and Flink delivering fresh features for inference in milliseconds and enabling online learning for continuously improving models. Fresh features in milliseconds. Online learning enabled at scale.
- Model monitoring and observability with drift detection and performance degradation alerts that automatically trigger retraining pipelines before model decay impacts users. Model decay caught instantly. Retraining triggered automatically.
- Batch inference optimisation for high-volume prediction workloads enabling cost-effective overnight processing at 80% lower cost than equivalent real-time inference. Millions of predictions overnight. 80% cheaper than real-time.
- LLM evaluation automation measuring response quality metrics, toxicity detection, and hallucination rates continuously so bad model outputs are caught before reaching users. Continuous quality assurance. Bad LLM outputs blocked at the gate.
- GPU resource optimisation through model quantisation, dynamic batching strategies, and multi-tenancy delivering 50% GPU cost reduction while maintaining all performance SLAs. 50% GPU cost reduction. Performance SLAs maintained throughout.
Days 61 to 90
03
Agentic AI and Elite Performance.
Deploy autonomous AI agents, build self-improving model infrastructure, and establish the data governance and security posture that enterprise AI platforms require to scale confidently.
- Agentic AI infrastructure with LangChain or AutoGen orchestration frameworks enabling autonomous agents that reason, plan, and execute complex multi-step tasks without human intervention. Autonomous agents deployed. Complex tasks executed without human oversight.
- Multi-agent collaboration systems with task delegation and result aggregation enabling specialised agents to work in parallel, solving problems significantly faster than single models. Specialist agents collaborate. Problems solved 10x faster.
- Agent memory and context management with vector stores and conversation history giving agents the ability to remember past interactions and deliver personalised experiences at scale. Agents remember past interactions. Personalised experiences at production scale.
- Tool integration for Agentic AI covering API access, database queries, and sandboxed code execution so agents can interact with your systems and take actions autonomously. Agents interact with your systems. Actions taken without manual intervention.
- AIOps predictive monitoring with anomaly detection, incident prediction, and automated remediation preventing 60% of production incidents before they affect any end user. 60% of incidents prevented. ML-driven operations intelligence.
- Automated ML retraining pipelines triggered by performance degradation thresholds or arrival of new training data so models continuously improve without manual scheduling. Self-improving models. No manual intervention for model updates.
- A/B testing infrastructure for model variants with automatic statistical significance detection and winner deployment so the best model always reaches production without ceremony. 10 plus variants tested simultaneously. Best model auto-deployed.
- Custom DORA metrics for AI and ML platforms tracking model deployment frequency, inference latency, and training time alongside standard engineering delivery metrics. Daily model releases. Sub-50ms inference. Elite ML performance tracked.
- Data lake and lakehouse architecture with Delta Lake or Apache Iceberg providing a single source of truth with time-travel queries for full model reproducibility. Single source of truth. Time-travel queries for reproducibility.
- ML security and governance with model access controls, complete audit logging, and bias detection frameworks to meet regulatory requirements and enterprise compliance standards. Every model decision auditable. Bias detected before production.
- Edge ML deployment pipelines for low-latency inference on embedded devices or mobile applications enabling on-device model execution with zero cloud dependency. Models run on-device. Zero cloud dependency for sensitive use cases.
- Cost attribution and FinOps for ML tracking GPU hours and storage costs per model and per team so every dollar of compute spend is visible and accountable. Transparent ML costs. Spend optimised as platform scales.
- Federated learning infrastructure for privacy-preserving distributed model training across data sources that cannot be centralised, fully GDPR-compliant and audit-ready. Train on sensitive data without centralisation. GDPR-compliant at scale.
- Team enablement through MLOps best practices workshops, LLMOps training, and Agentic AI design patterns that turn data scientists into production ML engineers. Data scientists become ML engineers. 80% faster path to production.
- Full handover with MLOps runbooks, model deployment templates, monitoring dashboards, and a 12-month roadmap so the platform scales to 100 plus models independently. Self-sufficient AI platform. Scale to 100 plus models independently.
Proven Results
Delivered Worldwide
60%
Faster Model Deployments
Sub-100ms
LLM Inference Latency
70%
GPU Cost Reduction
100+
Models in Production
Agentic
AI Deployed and Running
Elite
MLOps DORA Metrics
Ready to Build
AI That Ships Reliably?
Start with a free MLOps audit. We will review your current model deployment pipeline, training infrastructure, and serving architecture in the first conversation, at no cost and with no commitment required.
No long-term contracts
Pilot-first engagement
Results in 90 days
NDA from day one