?How to build ML pipelines

How to build ML pipelines - explained in simple terms with code implementation

1/ ML Pipeline is a systematic sequence of data processing and modeling steps that aims to automate and streamline the process of developing and deploying machine learning models. It provides a framework for organizing and managing the various stages.

2/ You need to know 6 steps ( each explained in this thread below)-

• Data Preprocessing
• Model Training
• Model Evaluation
• Model Deployment
• Model Monitoring and Maintenance
• Scikit-learn Pipeline library

3/ Data preprocessing is an essential stage in ML pipelines. It involves preparing and cleaning the data to make it suitable for model training. Techniques for Data preprocessing are Handling Missing Data, Encoding Categorical Variables, Feature Scaling.

4/ Handling Missing Data - Missing data can impact model training and performance. Common techniques to handle missing data include imputation (filling missing values with estimated values) or removing instances with missing data.

5/ Categorical variables need to be encoded numerically for most ML algorithms. Common encoding techniques include one-hot encoding or label encoding.

6/ Feature scaling ensures that all features have a similar scale, preventing any particular feature from dominating the training process. Common techniques include standardization or normalization.

7/ Model Training: Model training involves building and training an ML model using the preprocessed data.

8/ Model Evaluation: Model evaluation assesses the trained model's performance and generalization on unseen data. Common evaluation metrics for classification tasks include accuracy, precision, recall, and F1 score.

9/ Model Deployment: Model deployment involves making the trained model accessible for predictions on new data. This can be achieved through various means, such as creating an API endpoint or saving the model to disk.

10/ Model Monitoring and Maintenance: Once model is deployed, it's important to continuously monitor its performance and ensure its accuracy & reliability. This stage involves monitoring incoming data, retraining the model periodically with new data, and handling model

11/ For creating ML pipelines in Python - scikit-learn provides the Pipeline class for building and managing ML workflows. Each stage in the pipeline is represented by a tuple containing a name and an instance of a transformer or an estimator.

12/ Transformers are used for data preprocessing and feature engineering, while estimators represent the ML models. Next, Instantiate the Pipeline class with the defined stages. Then, Fit the pipeline to the training data and transform it.

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