AI Services
MLOps and Model Deployment
We build model serving, monitoring, versioning, retraining, evaluation, and ML infrastructure for production AI systems.
Why it matters
Keep models reliable after they go live.
A trained model is not a production system. Production ML needs deployment, observability, version control, drift detection, retraining processes, incident response, and clear ownership.
Solutyics helps teams turn models into reliable services or pipelines. We design the operational layer around model quality, data changes, latency, cost, approvals, and the environments where predictions are used.
Primary intent
Deploy and operate machine learning models reliably in production.
Fit
Where this work creates leverage.
Ideal for
- Teams with models stuck in notebooks
- Products that need model APIs or batch inference
- Organizations worried about drift and monitoring
- AI systems that need reproducible deployment and retraining
Typical use cases
- Deploying forecasting or classification models
- Serving models through APIs
- Monitoring model drift
- Automating retraining pipelines
Scope
What Solutyics delivers.
Deliverables
- Model serving architecture
- Versioning and deployment pipeline
- Monitoring and alerting setup
- Evaluation and retraining workflow
- Operational documentation
Considerations
- Model behavior can change as data changes
- Infrastructure should match latency and cost constraints
- Reproducibility matters for audit and debugging
- Ownership between data, ML, and software teams must be clear
Outcomes
What should improve after the work.
Reliable model deployment
Clear model version history
Monitoring for quality and drift
A maintainable ML operations path
Process
How the work moves
Review the model lifecycle
We inspect training data, model artifacts, deployment expectations, evaluation method, and ownership.
Design serving and monitoring
We define APIs, batch jobs, logs, metrics, alerts, versioning, and rollback behavior.
Implement the pipeline
We build deployment, inference, monitoring, and retraining workflows around the chosen infrastructure.
Operationalize handover
We document runbooks, model versions, data dependencies, and monitoring responsibilities.
FAQ
Questions that shape the work.
What is MLOps? +
MLOps is the practice of deploying, monitoring, versioning, retraining, and operating machine learning models reliably after development.
Can you deploy an existing model? +
Yes. We first review the model artifact, dependencies, input and output contracts, performance requirements, and monitoring needs before deployment.
How do you monitor model quality? +
We track data drift, prediction distributions, latency, errors, business metrics, and where possible ground-truth performance over time.
Do all models need automated retraining? +
No. Some models need manual retraining with review. Others benefit from scheduled or triggered retraining. The right approach depends on risk and data change.
Can MLOps work with cloud and on-premise systems? +
Yes. Architecture can be designed for AWS, Azure, container platforms, private infrastructure, or hybrid setups depending on data and operational constraints.
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Next
Move models from experiments to reliable production systems.
Bring the model, data flow, and deployment constraints. We will help build the operating layer around it.