ML/LLM Ops involve deploying and maintaining advanced technologies that enable systems to learn from data, ensuring organizations maximize their potential
Raw data is analyzed and stored in a centralized feature repository for efficient model development.
This involves structured processes of data validation, preparation, model training, evaluation, and validation to ensure robust model creation.
Facilitates batch data fetching, extraction, validation, and training, allowing for continuous integration and retraining of models
Stores and manages different model versions, simplifying deployment and version control.
Trained models are deployed to provide predictions in real-time or batch modes, integrating seamlessly with production systems.
Continuous tracking of model performance ensures alignment with business objectives and addresses issues like model drift.
Incorporates real-world performance data to refine and enhance models iteratively
Utilize Enterprise Data and Public Data through Data Processing Pipelines and Knowledge Graphs to enrich model training and provide contextual understanding.
Implement Supervised Fine-Tuning and Few-Shot Learning techniques to tailor models to specific tasks effectively.
Model Versioning
Model Caching
Model Monitoring
Deploy fine-tuned LLMs through Mobile/Web UIs, facilitating seamless access for end-users while gathering valuable feedback for further improvement.
Incorporate Reinforcement Learning from Human Feedback (RLHF) to continually enhance model responses based on real-time user interactions and feedback.