Get started with for the ML copilot

Introduction

The ML Copilot simplifies machine learning workflows from data ingestion to model deployment. This guide will walk beginners through the step-by-step process of implementing machine learning projects.

1. Data preparation and ingestion

Start with collecting and preparing data from various sources and formats.

• Collect data: Raw data can come from databases, spreadsheets, APIs, JSON, CSV, or other formats.
• Load data: Use Pandas, NumPy, or specialized AutoML libraries like Auto-Sklearn, AutoGluon or H2O.ai.
• Data cleaning: Handle missing values, remove duplicates, and correct inconsistent data.

2. Column type detection

Correctly identify data types to process them appropriately.

• Boolean: True/False, Yes/No values.
• Discrete Numerical: Integer-based values (e.g., number of products sold).
• Continuous Numerical: Floating-point numbers (e.g., temperature, price).
• Text: Unstructured text data.

3. Target column detection

Identify the purpose of each column:

• Target/Label: The variable to be predicted.

4. Data normalization and cleaning

A critical step is the inspect the structure of your data and clean it

• Feature transformation: If necessary, applying log transformations
• Feature scaling: Normalization and standardization.
• PCA: Unsupervised analysis to see the global structure. Can help to detect outliers.
• Outlier detection: Use several univariate methods to detect outliers. Basic methods are z-score and IQR method.
• Remove outliers: If required remove outliers

5. Task detection

Determine the type of learning problem:

Supervised analysis

• Binary classification: The variable to predict has two classes (e.g., healthy or sick).
• Multi-class classification: The variable to predict has more than two possible categories (e.g., healthy or diabetes/sepsis).
• Regression: Predicting continuous numerical values.

Unsupervised analysis

• Clustering: Grouping similar data points.
• PCA: Unsupervised linear analysis to to reduce dimensionality see the data in lower dimensionalities (could help identifying similarities between data point)
• t-SNE Unsupervised non-linear analysis to reduce dimensionality see the data in lower dimensionalities (could help identifying similarities between data point)
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (Arxiv, JOSS, GitHub). Analogous to t-SNE but alos for more general dimension reduction.

6. Feature engineering

Feature engineering improves model performance by creating new features:

• Feature Creation: Deriving new variables from existing data.
• Feature scaling: Normalization and standardization.
• Encoding categorical variables: One-hot encoding or label encoding.
• Feature transformation: Applying log transformations, PCA, or embeddings.

7. Feature Selection

Select the most important features to reduce dimensionality and improve efficiency:

• Filter methods: Select features based on statistical tests.
• Wrapper Methods: Recursive feature eliminati), forward/backward selection.
• Embedded methods: Use models like Lasso and Decision Trees to rank feature importance.

8. Analysis of Obtained Results

Interpreting model outcomes:

• Model explainability: SHAP values, feature importance.
• Bias and fairness analysis: Checking for model biases.
• Model drift detection: Tracking performance over time.

9. Creating User Interfaces and Visualizations

Developing interactive tools for users:

• Dashboards: Displaying real-time insights.
• Visualizations: Feature importance, confusion matrix, decision boundaries.
• APIs & Deployment: Creating user-friendly interfaces for ML models.

Conclusion

AutoML simplifies the entire machine learning pipeline by automating data processing, model selection, feature engineering, and hyperparameter tuning. This guide provides an end-to-end overview for beginners to build their own AutoML solutions effectively.