Beyond training
A trained model is not a production system. Production ML needs deployment, observability, version control, drift detection, retraining processes, incident response, and clear ownership.
AI Services
AI Services service track
MLOps and Model Deployment
We build model serving, monitoring, versioning, retraining, evaluation, and ML infrastructure for production AI systems.
Approach
Keep models reliable after they go live.
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
Operational reliability
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.
Fit
Where this creates leverage
The strongest engagements have a clear operating constraint, decision, workflow, or delivery risk to improve.
Best fit
Conditions that make the work valuable
- 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
Situations the service can address
- Deploying forecasting or classification models
- Serving models through APIs
- Monitoring model drift
- Automating retraining pipelines
Deliverables
What Solutyics actually delivers
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
Serving architecture
Model serving architecture
Release pipeline
Versioning and deployment pipeline
Monitoring and alerts
Monitoring and alerting setup
Retraining workflow
Evaluation and retraining workflow
Operations guide
Operational documentation
Process
How the work moves
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
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.
Outcomes
What should improve after the work
Reliable model deployment
Clear model version history
Monitoring for quality and drift
A maintainable ML operations path
FAQ
Questions that shape the work
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
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 step
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.