ML/LLM Ops

ML/LLM Ops involve deploying and maintaining advanced technologies that enable systems to learn from data, ensuring organizations maximize their potential

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ML Ops is a discipline that combines machine learning and operations to streamline the deployment, monitoring, and management of ML models in production environments. The goal of ML Ops is to enhance collaboration between data scientists, IT teams, and business stakeholders, enabling organizations to leverage machine learning effectively.


Data Analysis
Feature Store

Raw data is analyzed and stored in a centralized feature repository for efficient model development.

Orchestrated
Experimentation

This involves structured processes of data validation, preparation, model training, evaluation, and validation to ensure robust model creation.

Automated
Pipeline

Facilitates batch data fetching, extraction, validation, and training, allowing for continuous integration and retraining of models ​

Model
Registry

Stores and manages different model versions, simplifying deployment and version control.

Deployment &
Serving

Trained models are deployed to provide predictions in real-time or batch modes, integrating seamlessly with production systems.

Performance
Monitoring

Continuous tracking of model performance ensures alignment with business objectives and addresses issues like model drift.

Feedback &
Loop

Incorporates real-world performance data to refine and enhance models iteratively

Expert

Data
Integration

Utilize Enterprise Data and Public Data through Data Processing Pipelines and Knowledge Graphs to enrich model training and provide contextual understanding.

Fine-Tuning
Methods

Implement Supervised Fine-Tuning and Few-Shot Learning techniques to tailor models to specific tasks effectively.

Model
Management

Model Versioning
Model Caching
Model Monitoring ​

User
Interaction

Deploy fine-tuned LLMs through Mobile/Web UIs, facilitating seamless access for end-users while gathering valuable feedback for further improvement.

Reinforcement
Learning

Incorporate Reinforcement Learning from Human Feedback (RLHF) to continually enhance model responses based on real-time user interactions and feedback.

Expert