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{"pre_conditions":["Basic understanding of ML concepts","Familiarity with version control (Git)"],"post_conditions":["Can explain MLOps lifecycle phases","Can assess organizational MLOps maturity","Can recommend tooling strategy"],"parameter_schema":{"learning_path":{"type":"string","enum":["quick_start","comprehensive","enterprise_focused"]},"focus_area":{"type":"string","enum":["lifecycle","tools","practices","all"]}}}
Production Data → Compare vs Reference → Detect Drift → Alert/Retrain
Exercises:
Design a training pipeline for a sample project
Implement basic data validation with Great Expectations
Set up a simple monitoring dashboard
Code Templates
Template 1: MLflow Experiment Setup
# templates/mlflow_setup.pyimport mlflow
from mlflow.tracking import MlflowClient
defsetup_experiment(
experiment_name: str,
tracking_uri: str = "sqlite:///mlflow.db") -> str:
"""Initialize MLflow experiment with best practices."""
mlflow.set_tracking_uri(tracking_uri)
# Create or get experiment
client = MlflowClient()
experiment = client.get_experiment_by_name(experiment_name)
if experiment isNone:
experiment_id = client.create_experiment(
name=experiment_name,
tags={"version": "1.0", "team": "ml-platform"}
)
else:
experiment_id = experiment.experiment_id
mlflow.set_experiment(experiment_name)
return experiment_id
deflog_run(params: dict, metrics: dict, model_path: str):
"""Log a complete training run."""with mlflow.start_run():
mlflow.log_params(params)
mlflow.log_metrics(metrics)
mlflow.log_artifact(model_path)
Template 2: MLOps Maturity Assessment
# templates/maturity_assessment.pyfrom dataclasses import dataclass
from enum import IntEnum
from typing importListclassMaturityLevel(IntEnum):
AD_HOC = 0
REPEATABLE = 1
RELIABLE = 2
SCALABLE = 3
OPTIMIZED = 4@dataclassclassAssessmentQuestion:
dimension: str
question: str
level_0: str
level_1: str
level_2: str
level_3: str
level_4: str
ASSESSMENT_QUESTIONS = [
AssessmentQuestion(
dimension="Data Management",
question="How do you manage training data?",
level_0="Ad-hoc file storage",
level_1="Versioned with DVC/Git LFS",
level_2="Data validation in place",
level_3="Feature store implemented",
level_4="Automated data quality monitoring"
),
# Add more questions for each dimension
]
defcalculate_maturity_score(responses: List[int]) -> tuple:
"""Calculate overall maturity score."""
avg_score = sum(responses) / len(responses)
level = MaturityLevel(int(avg_score))
return avg_score, level
Troubleshooting Guide
Common Issues
Issue
Symptom
Solution
MLflow UI not loading
Connection refused
Check tracking URI, start server
Experiment not found
Experiment doesn't exist
Verify experiment name, create if needed
Artifact upload fails
Storage permission denied
Check artifact location permissions
Model registration fails
Model name conflict
Use unique names or versioning
Debug Checklist
□ 1. Verify MLflow server is running
□ 2. Check tracking URI configuration
□ 3. Confirm artifact storage accessible
□ 4. Validate experiment exists
□ 5. Test with minimal example first
Knowledge Check
Self-Assessment Questions
What are the 5 main phases of the ML lifecycle?
How does MLOps differ from DevOps?
When would you use MLflow vs Weights & Biases?
What's the difference between Level 1 and Level 2 MLOps maturity?
What should you version in an ML project?
Practical Exercises
Beginner: Set up MLflow tracking for an existing notebook
Intermediate: Create a reproducible training script with environment capture
Advanced: Design an end-to-end pipeline diagram for a real use case
