Chatbot ML Development and Evaluation Pipeline

ML & AI · flowchart diagram · NOASSERTION

A sequential pipeline illustrating the stages of a chatbot's machine learning model development, including preparation, testing, evaluation, bias detection

Source: https://github.com/eCom-dev5/verta-chatbot/blob/91f0c62187b6a69db2dcbe5b0259449b1cc43dd4/readme/00_ML_PIPELINES.md
Curated by eCom-dev5
Machine Learning AI Chatbot Pipeline MLOps Evaluation Bias Detection

Mermaid source

flowchart TD
        node1["prepare_base_model"]
        node1-->node2
        node2["test_ingestion"]
        node2-->node3
        node3["evaluation"]
        node3-->node4
        node4["bias_detection"]
        node5["failure_detection"]
        node3-->node5
        node4-->node5

What this diagram shows

This flowchart depicts a machine learning pipeline for a chatbot, starting with 'prepare_base_model', followed by 'test_ingestion'. The 'evaluation' stage branches into 'bias_detection' and directly into 'failure_detection'. 'bias_detection' also feeds into 'failure_detection', indicating a comprehensive testing and validation process.

When to use it

Use this diagram to visualize the workflow for developing, testing, and validating machine learning models, especially for AI applications like chatbots, where robust evaluation, bias mitigation, and failure detection are critical before deployment.

How to adapt it for your project

This pipeline can be adapted by adding more specific testing stages (e.g., performance testing, security testing), integrating feedback loops from production, or incorporating continuous integration/continuous deployment (CI/CD) steps. Stages can be made conditional or parallel based on specific project needs.

Key concepts