Model serving and infrastructure
Put the model to work. Manage what it runs on.
Serve selected models, run processing jobs and manage the resources behind your AI system. Bibha brings inference, compute and deployment controls together, from capacity planning to release changes and recovery.
What does Bibha cover in model serving and deployment?
Bibha serves selected models for applications and manages the compute, GPU resources and supported jobs around them. Deployment capabilities cover hosted, customer-cloud, local and mixed arrangements, with capacity management, scaling, health monitoring, rollout and rollback, backup and restore, model or provider changes, and resource budgets. The qualified stack, resources and operating responsibilities are defined for the workload.
Turn a selected model into an operating service.
Inference is the step where a model produces an output. Model serving makes that capability available to the application that needs it. A selected model or version moves from development and evaluation into an approved deployment configuration.
The model is one part of the operating system. Its data connections, workflow, runtime and monitoring need to fit the task as well. Plan the serving setup around the requests it will handle and the result the application needs.
Match resources and jobs to the workload.
Provide or connect the compute and GPU resources required by supported workloads. Job execution runs training and other processing work that consumes those resources.
Define the work before choosing the capacity: the model, processing task, expected request pattern and operating environment. This makes resource allocation a workload decision rather than an assumption based on model size alone.
Compute and GPU resources
Use the resources qualified for the workload and its operating requirements.
Job execution
Run supported training and processing jobs as part of the model and application lifecycle.
Capacity and concurrency
Define the volume of work the configured environment can handle, supported by measurement.
Adjust capacity within agreed limits.
Scaling and resource allocation adjust supported capacity as demand changes. Resource budgets and quotas give workloads defined consumption limits and cost visibility.
Capacity, concurrency and response needs should be considered together. The appropriate resource arrangement depends on the workload; no universal throughput or latency figure describes every system.
Place each component in the right environment.
Operate an agreed hosted environment, run the qualified stack in a customer cloud, or use customer-controlled facilities or local infrastructure. Mixed arrangements place different components in approved environments when the workflow needs that combination.
Map the complete path: where the model runs, where information is processed, which business systems it reaches and who operates each dependency. A deployment label alone does not answer those questions.
Hosted service
Use an agreed environment operated as a service.
Customer cloud
Run the qualified stack within your cloud account.
On-premises or local
Use the qualified stack in customer-controlled facilities or local infrastructure.
Mixed arrangements
Place components in different approved environments with their connections and responsibilities defined.
Make release changes and recovery part of the design.
Roll out an approved configuration and restore a prior one when needed. Health monitoring and recovery identify unhealthy components and support restoration of the contracted service.
Backup and restore protect the agreed assets and include testing their restoration. The assets, recovery procedure and operating responsibilities belong in the system's scope, alongside the normal release process.
Rollout and rollback
Introduce approved configurations with a path back to a previous configuration when needed.
Health and recovery
Detect unhealthy components and carry out the agreed recovery work.
Backup and restore
Protect the assets in scope and test how they are restored.
Keep model and provider changes practical.
Move workloads to compatible models or suppliers where the change has been tested and the licences permit it. Model choice remains connected to the task, its quality requirements and its operating constraints.
Work through compatibility before treating a change as a simple substitution. Review the application's expected inputs and outputs, evaluate the replacement and agree how it will be introduced.
Scope the system you need to operate.
Bring the workload, the environment requirements and the responsibilities your team wants to retain. We can then define resources, deployment controls and the operating arrangement together.
Co-build with our engineers or agree managed delivery and operation. Capacity, support and recovery commitments are specified for the engagement.
Questions and answers
They answer different questions. Model serving makes a selected model available for application requests. The deployment environment determines where the model and other system components run, what they connect to and who operates them.
Release rollout and rollback support introducing an approved configuration and restoring a prior one when needed. The supported configuration, assets and recovery procedure are defined for the system; rollback is not a promise that every external business action can be undone.
Model and provider changes are supported where the replacement is compatible, tested and permitted by the relevant licences. Evaluate the change against the application's requirements and agree how it will be released.
Capacity, concurrency and response requirements are scoped and measured for the workload. They depend on the model, resources, processing path and demand. The engagement defines the configuration and any service commitments.