MLOps expertise ensures smooth transitions from development and training to production with automated pipelines handling versioning, monitoring, and scaling.
Multi-Model System deployment and orchestration
Computational Resource Management and Optimization
Optimal GPU Utilization
MLops
# mlops_pipeline.flow
workflow {
build( <autumated_pipeline>)
deploy( <trained_models>)
scale( <compoute_resources>)
monitor( <production_health>)
}
# deployment.flow
workflow {
deploy( <models>)
optimize( <inference>)
stream( <responses>)
secure( <privacy>)
}
Deploy LLMs effectively across environments, from local hosting for data privacy to hybrid architectures balancing cost, performance, and a restrictive data access policy.
Streaming implementations and inference optimization make real-time AI interactions practical even with resource constraints.
Local hosting - Running 70B parameter models
Inference optimization
Cost optimization
Streaming
Hybrid local/cloud deployment for cost optimization
Privacy-preserving inference without data leaving premises
Match the right model to each task, avoiding the inefficiency of using oversized models for simple problems or undersized ones for complex challenges.
Our systematic approach evaluates task requirements against model capabilities, creating efficient workflows that dynamically route queries to appropriate models based on complexity and required accuracy.
Matching model size to task complexity
Efficient and understandable LLM workflows
Model switching based on query complexity
