Weights and Biases overview in MLOps - Step-by-Step Execution

Process Flow - Weights and Biases overview

Start ML Project

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Initialize W&B

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Log Configurations

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Train Model

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Log Metrics & Artifacts

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Visualize Results on W&B Dashboard

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Iterate or Share Results

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End

This flow shows how a machine learning project uses Weights and Biases to track experiments step-by-step.

Execution Sample

MLOps

import wandb
wandb.init(project="my-ml-project")
config = wandb.config
config.learning_rate = 0.01
for epoch in range(3):
    loss = 0.1 / (epoch + 1)
    wandb.log({"epoch": epoch, "loss": loss})

This code initializes W&B, sets a learning rate, and logs loss values for 3 epochs.

Process Table

Step	Action	Variable State	W&B Log Entry	Output/Result
1	Import wandb and initialize project	wandb initialized, project='my-ml-project'	None	W&B run started
2	Set config.learning_rate	config.learning_rate=0.01	None	Config saved
3	Epoch 0: calculate loss	epoch=0, loss=0.1	Log {'epoch': 0, 'loss': 0.1}	Loss logged
4	Epoch 1: calculate loss	epoch=1, loss=0.05	Log {'epoch': 1, 'loss': 0.05}	Loss logged
5	Epoch 2: calculate loss	epoch=2, loss=0.0333	Log {'epoch': 2, 'loss': 0.0333}	Loss logged
6	End of loop	epoch=3 (loop ends)	None	Training complete, data on W&B

💡 Loop ends after 3 epochs, all losses logged to W&B

Status Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
epoch	undefined	undefined	0	1	2	3 (loop ends)
loss	undefined	undefined	0.1	0.05	0.0333	undefined
config.learning_rate	undefined	0.01	0.01	0.01	0.01	0.01

Key Moments - 3 Insights

Why do we call wandb.log inside the loop?

Because each epoch produces new metrics (loss) to track, calling wandb.log inside the loop sends updated data to W&B after every epoch, as shown in steps 3-5.

What happens if we don't call wandb.init before logging?

Without wandb.init (step 1), W&B doesn't know which project or run to associate logs with, so logging metrics (steps 3-5) will fail or be ignored.

Is config.learning_rate updated during training?

No, config.learning_rate is set once before training (step 2) and remains constant during the loop, as seen in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution table, what is the loss value logged at step 4?

A 0.1

B 0.0333

C 0.05

D 0.0

💡 Hint

Check the 'W&B Log Entry' column at step 4 in the execution_table.

At which step does the training loop end according to the execution table?

A Step 6

B Step 3

C Step 5

D Step 2

💡 Hint

Look for the row mentioning 'End of loop' in the execution_table.

If we change config.learning_rate to 0.02 at step 2, what changes in variable_tracker?

A config.learning_rate remains 0.01

B config.learning_rate changes to 0.02 from step 2 onward

C epoch values change

D loss values become zero

💡 Hint

Refer to the 'config.learning_rate' row in variable_tracker and how it updates after step 2.

Concept Snapshot

Weights and Biases (W&B) helps track ML experiments.
Initialize with wandb.init(project).
Set config parameters before training.
Log metrics each epoch with wandb.log({metric:value}).
View results on W&B dashboard to compare runs.

Full Transcript

Weights and Biases is a tool to track machine learning experiments. First, you start a project with wandb.init. Then you set configuration parameters like learning rate. During training, you log metrics such as loss after each epoch using wandb.log. This data is sent to the W&B dashboard where you can see graphs and compare runs. The example code shows initializing W&B, setting learning rate, and logging loss for 3 epochs. The execution table traces each step, showing how variables change and when logs happen. Key points include calling wandb.log inside the loop to track progress, initializing W&B before logging, and that config values stay constant unless changed explicitly.