EN

How to train AI ML models? Full pipeline in 15 mins

#AI Tools
2025-09-01 18:218 min read

Content Introduction

This video provides a comprehensive guide on building production-level machine learning (ML) models. It stresses the importance of a structured workflow that includes data cleaning, processing, and model training. Viewers learn that a successful ML model is not just about fitting data but requires attention to pipeline integrity and performance metrics like accuracy, precision, and recall. The video also discusses common pitfalls such as overfitting and underfitting, the significance of using consistent scalers for train/test datasets, and the need for hyperparameter tuning. Additionally, practical tips are offered for handling imbalanced datasets and ensuring models remain effective as data shifts over time. The content targets beginners and emphasizes iterating on models to identify the best performing techniques.

Key Information

  • Building production-level machine learning models requires following a well-designed workflow.
  • It is not as simple as just calling model.fit; incorrect steps can compromise the entire pipeline.
  • A generalized pipeline aids beginners in understanding the different stages of building machine learning models.
  • Data sets must be cleaned to remove Nan values, corrupted data, and duplicates, as they can skew model performance.
  • Proper pre-processing techniques include scaling and standardizing data, as well as hyperparameter tuning.
  • When splitting data into training and test sets, it is crucial to maintain the balance of classes to avoid bias.
  • Models can overfit or underfit based on how well they generalize to unseen data, and performance should be evaluated using appropriate metrics.
  • Random state is a hyperparameter that affects the reproducibility of the split process.
  • Always save the parameters and weights of the scaler used in pre-processing, alongside the model itself.

Timeline Analysis

Content Keywords

Machine Learning Models

Building production-level machine learning models requires a well-designed workflow that ensures optimal model performance. It's crucial to avoid common pitfalls, such as neglecting data cleaning and preprocessing steps.

Data Pipeline

A generalized pipeline can help beginners understand the stages of machine learning model creation, from data cleaning, splitting into training and test sets, to model training and evaluation.

Data Preprocessing

Data preprocessing involves cleaning, normalizing, and scaling data, which is essential for effective model training. The importance of maintaining consistency in preprocessing across training and test sets is emphasized.

Hyperparameter Tuning

Selecting and tuning hyperparameters is a critical step in optimizing model performance. It includes experimenting with different models and their parameters to find the best fit for the dataset.

Model Evaluation Metrics

Choosing the right evaluation metrics (like accuracy, precision, or F1 score) is vital, especially in cases of imbalanced datasets, as these metrics can impact the understanding of model performance.

Model Overfitting

Overfitting occurs when a model performs well on training data but poorly on unseen data, which necessitates the need for careful evaluation and adjusting of model complexity.

Random Train-Test Splitting

The process of splitting data should be random yet stratified when necessary, to ensure that all classes are adequately represented in both training and test sets.

Data Drift

Data drift occurs when the characteristics of the input data change over time, leading to model underperformance. It's crucial for model maintainers to monitor and adjust for these changes.

Practical Application

Successfully applying machine learning models in real-world scenarios requires understanding dynamic data sets and continual model evaluation against evolving data.

Related questions&answers

What is the first step in building production-level ML models?

The first step is cleaning the data.

What does cleaning a dataset involve?

Cleaning a dataset involves removing NaN values, corrupted data, and duplicates.

Why is it important to follow a structured workflow when building ML models?

A well-structured workflow ensures that your model performs to the best extent possible.

What happens if I make a mistake in my ML pipeline?

If you make a mistake in your pipeline, it can compromise the entire model.

Can I use any dataset to train my model?

No, the dataset used should be representative of the real-world problem and should be well-prepared.

What should I do if my dataset is imbalanced?

Consider augmenting the underrepresented data or using stratified splitting.

Is it necessary to save the scaler's weights after training my model?

Yes, you need to save the scaler's weights to preprocess the test set correctly.

What evaluation metrics can I use for my ML model?

You can use metrics like accuracy, precision, recall, and F1 score.

How can I avoid overfitting my model?

To avoid overfitting, you should validate the model on a separate dataset and use techniques such as cross-validation.

What is hyperparameter tuning?

Hyperparameter tuning involves adjusting the parameters of your model to improve its performance.

More video recommendations

Share to: