Experiment tracking (MLflow) in ML Python - Model Pipeline Trace

Model Pipeline - Experiment tracking (MLflow)

This pipeline shows how MLflow helps track machine learning experiments. It records data, model training steps, and results so you can compare and improve models easily.

Data Flow - 6 Stages

1Data Loading

1000 rows x 10 columns→Load dataset from CSV file→1000 rows x 10 columns

Sample row: {"feature1": 5.1, "feature2": 3.5, ..., "label": 1}

↓

2Data Preprocessing

1000 rows x 10 columns→Fill missing values and normalize features→1000 rows x 10 columns

Normalized feature1 value: 0.45

↓

3Train/Test Split

1000 rows x 10 columns→Split data into training and testing sets (80/20)→Train: 800 rows x 10 columns, Test: 200 rows x 10 columns

Train sample label: 0, Test sample label: 1

↓

4Model Training

Train: 800 rows x 9 features→Train logistic regression model→Trained model object

Model coefficients: [0.5, -0.3, 0.1, ...]

↓

5Experiment Logging

Trained model, training metrics→Log parameters, metrics, and model to MLflow→Experiment record in MLflow server

Logged accuracy: 0.85, loss: 0.35

↓

6Model Evaluation

Test: 200 rows x 9 features→Predict and calculate accuracy→Accuracy score: 0.83

Predicted labels: [0,1,0,0,1,...]

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss is high, accuracy low
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.78	Model continues to improve
4	0.35	0.82	Loss lowers further, accuracy near good level
5	0.33	0.85	Training converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Linear Combination

Layer 3: Sigmoid Activation

Layer 4: Thresholding

Model Quiz - 3 Questions

What does MLflow track during model training?

A Only the final model

B Only the training data

C Parameters, metrics, and models

D Only the test results

Key Insight

Using MLflow to track experiments helps keep all training details organized. This makes it easier to compare models, understand improvements, and reproduce results reliably.

Practice

1. What is the main purpose of using MLflow in machine learning projects?

easy

A. To write machine learning algorithms from scratch

B. To visualize data without any coding

C. To track and manage machine learning experiments

D. To deploy machine learning models to mobile apps

Solution

1. Step 1: Understand MLflow's role

   MLflow is designed to help track experiments by logging parameters, metrics, and models.

2. Step 2: Compare options

   Options A, C, and D describe tasks outside MLflow's main purpose.

3. Final Answer:

   To track and manage machine learning experiments -> Option C

4. Quick Check:

   MLflow = experiment tracking [OK]

Hint: MLflow is for tracking experiments, not coding models [OK]

Common Mistakes:

• Confusing MLflow with model building libraries
• Thinking MLflow is only for visualization
• Assuming MLflow deploys models directly

2. Which MLflow function is used to start a new experiment run in Python?

easy

A. mlflow.init_experiment()

B. mlflow.start_run()

C. mlflow.create_run()

D. mlflow.begin_experiment()

Solution

1. Step 1: Recall MLflow run start syntax

   The correct function to start a run is mlflow.start_run().

2. Step 2: Check other options

   Functions like init_experiment, create_run, and begin_experiment do not exist in MLflow API.

3. Final Answer:

   mlflow.start_run() -> Option B

4. Quick Check:

   Start run = mlflow.start_run() [OK]

Hint: Use start_run() to begin tracking an experiment run [OK]

Common Mistakes:

• Using non-existent MLflow functions
• Confusing experiment and run functions
• Misspelling function names

3. What will be the output of this MLflow code snippet?

import mlflow
with mlflow.start_run():
    mlflow.log_param('alpha', 0.1)
    mlflow.log_metric('accuracy', 0.95)
    run_info = mlflow.active_run().info
print(run_info.run_id is not None)

medium

A. True

B. False

C. None

D. Error

Solution

1. Step 1: Understand mlflow.start_run() context

   The code starts a run, logs parameters and metrics, then gets the active run info.

2. Step 2: Check run_id existence

   Inside the run context, run_id is assigned and not None, so print(run_info.run_id is not None) outputs True.

3. Final Answer:

   True -> Option A

4. Quick Check:

   Active run has run_id = True [OK]

Hint: Active run always has a run_id during context [OK]

Common Mistakes:

• Assuming run_id is None before logging
• Confusing run info with experiment info
• Expecting print to show run_id string

4. Identify the error in this MLflow code snippet:

import mlflow
mlflow.log_param('learning_rate', 0.01)
with mlflow.start_run():
    mlflow.log_metric('loss', 0.2)

medium

A. log_param called outside a run context

B. start_run() missing parentheses

C. log_metric called before log_param

D. No error, code runs fine

Solution

1. Step 1: Check order of MLflow calls

   log_param is called before starting a run, which is not allowed.

2. Step 2: Validate run context requirement

   MLflow requires logging functions inside a run context started by start_run().

3. Final Answer:

   log_param called outside a run context -> Option A

4. Quick Check:

   Logging outside run = error [OK]

Hint: Always log params and metrics inside start_run() [OK]

Common Mistakes:

• Calling log_param before start_run()
• Ignoring run context for logging
• Assuming log_metric order matters

5. You want to compare two ML models' performance using MLflow. Which steps correctly describe this process?

hard

A. Only log parameters for both models, metrics are not needed for comparison

B. Log both models' metrics in the same run, then compare metrics manually

C. Use mlflow.log_artifact() to save models, no need to log metrics or parameters

D. Run each model in separate mlflow.start_run() blocks, log metrics, then use MLflow UI to compare

Solution

1. Step 1: Understand MLflow run separation

   Each model should be run and logged in its own mlflow.start_run() block to keep results separate.

2. Step 2: Use MLflow UI for comparison

   After logging metrics and parameters, MLflow UI allows easy comparison of runs.

3. Final Answer:

   Run each model in separate mlflow.start_run() blocks, log metrics, then use MLflow UI to compare -> Option D

4. Quick Check:

   Separate runs + UI = compare models [OK]

Hint: Use separate runs and MLflow UI to compare models easily [OK]

Common Mistakes:

• Logging multiple models in one run
• Skipping metric logging
• Ignoring MLflow UI for comparison