MLOps Services
MLOps is the practice of reliably deploying, monitoring and updating machine learning models in production. We set up the pipelines, model registry, monitoring and automatic retraining that keep your models and LLM applications accurate, fast and auditable after launch.
Many AI projects stall after the demo because nobody planned how to run them. We build the operational side from the start: repeatable training, version control for data and models, alerts when accuracy drops, and cost tracking.
What we offer
- Training pipelinesRepeatable, automated model training.
- Model registryVersioned models with approval steps.
- DeploymentAPIs, batch jobs and canary releases.
- MonitoringAccuracy, drift, latency and cost alerts.
- LLMOpsPrompt versioning, evaluation and tracing for LLM apps.
Common use cases
- Moving a notebook model into production
- Monitoring forecast accuracy over time
- Governance for regulated industries
- Running several LLM features reliably
Technology
- MLflow
- Kubeflow
- AWS SageMaker
- Azure ML
- Vertex AI
- Docker
- Kubernetes
Business benefits
- Models reach production fasterwith automated pipelines
- Reliable predictionswith monitoring for data drift and accuracy
- Reproducible resultsversioned data, code and models
- Lower infrastructure costswith right-sized training and serving
Why work with Webtech Evolution
- 10+ years of delivery272+ projects for 118+ clients in 12+ countries since 2014.
- One team, end to enddesign, front end, back end, mobile, QA and DevOps under one roof, so nothing gets lost between vendors.
- Clear estimatesa written scope, timeline and price before work starts, and demos throughout the build.
- You own everythingcode, designs and documentation are handed over in full and covered by an NDA.
- Working hours that overlap with yoursMonday to Friday, 10:00–19:00 IST, with extended hours available for clients in the USA, Canada and New Zealand.
How we deliver
Discovery
agree the goal, users, data and how success will be measured.
Prototype
a working version on real examples within the first weeks.
Build and integrate
connect to your systems, add security, testing and monitoring.
Pilot
launch to a small group, measure results and improve.
Scale and support
roll out widely, with ongoing monitoring and updates.
Need people rather than a project? Hire AI developers.
Frequently asked questions
Yes, in a lighter form. Even one model in production benefits from versioning, monitoring and a simple retraining process.
LLMOps applies the same ideas to language-model apps, focusing on prompts, evaluation, tracing and cost rather than retraining.
Yes. We review your current setup and add the missing pieces step by step.
The practices and tools that take machine-learning models from experiments to reliable production systems, including automated training, deployment, monitoring and retraining.
MLflow, Kubeflow, SageMaker, Vertex AI, Weights & Biases, DVC and standard CI/CD tools.
Yes. For LLM applications we add prompt versioning, evaluation suites, cost tracking and output monitoring.
Let's Talk!
Have a question about MLOps Services? Send us a message and our team will reply with next steps.
- Reply within a few hours (Mon–Fri)
- NDA available
- You own the code
Prefer to talk? Call +91-9601965456WhatsApp ushello@webtech-evolution.com
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