Logging parameters and metrics in MLOps - Step-by-Step Execution

Process Flow - Logging parameters and metrics

Start Training

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Set Parameters

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Log Parameters

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Train Model

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Evaluate Model

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Log Metrics

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End Training

This flow shows how parameters are set and logged before training, then metrics are logged after evaluation.

Execution Sample

MLOps

params = {"lr": 0.01, "epochs": 5}
log_params(params)
metrics = {"accuracy": 0.85, "loss": 0.35}
log_metrics(metrics)

This code logs training parameters first, then logs evaluation metrics after training.

Process Table

Step	Action	Input	Logged Data	Output/State
1	Set parameters	{"lr": 0.01, "epochs": 5}	None	Parameters ready for training
2	Log parameters	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	Parameters saved in log
3	Train model	Parameters	None	Model trained with given parameters
4	Evaluate model	Trained model	None	Metrics calculated
5	Log metrics	{"accuracy": 0.85, "loss": 0.35}	{"accuracy": 0.85, "loss": 0.35}	Metrics saved in log

💡 All parameters and metrics logged after training and evaluation complete

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5
params	{}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}
metrics	{}	{}	{}	{}	{"accuracy": 0.85, "loss": 0.35}	{"accuracy": 0.85, "loss": 0.35}
log_storage	{}	{}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5}	{"lr": 0.01, "epochs": 5, "accuracy": 0.85, "loss": 0.35}

Key Moments - 3 Insights

Why do we log parameters before training instead of after?

Logging parameters before training ensures the exact settings used are saved, as shown in step 2 of the execution_table where parameters are logged right after being set.

Can metrics be logged before training is complete?

No, metrics depend on model evaluation after training, so they are logged only after evaluation as shown in step 5 of the execution_table.

What happens if parameters or metrics are not logged?

Without logging, you lose track of what settings were used or how well the model performed, making it hard to reproduce or improve results, as implied by the log_storage variable in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution_table, what data is logged at step 2?

A Training parameters

B Training metrics

C Model weights

D Evaluation results

💡 Hint

Check the 'Logged Data' column at step 2 in the execution_table

At which step does the metrics variable first get a value?

A Step 1

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the variable_tracker row for 'metrics' and see when it changes from empty to having values

If we skip logging parameters, what changes in the variable_tracker?

A params variable would be empty

B log_storage would not contain parameters after step 2

C metrics would not be logged

D Model training would fail

💡 Hint

Refer to the 'log_storage' row in variable_tracker and step 2 in execution_table

Concept Snapshot

Logging parameters and metrics:
- Set parameters before training
- Log parameters immediately after setting
- Train and evaluate model
- Log metrics after evaluation
- Logging ensures reproducibility and tracking

Full Transcript

This visual execution shows how parameters and metrics are logged in a machine learning workflow. First, parameters like learning rate and epochs are set and logged before training starts. Then the model trains using these parameters. After training, the model is evaluated to produce metrics such as accuracy and loss. These metrics are then logged. The execution table traces each step, showing when parameters and metrics are saved. The variable tracker shows how variables change over time. Key moments clarify why logging order matters. The quiz tests understanding of when and what data is logged. This process helps keep track of experiments and results clearly.