Implement a simple ML Pipeline with Apache Spark MLLib / Databricks
An ML pipeline’s efficiency is best when it’s in a unified environment
Intro to MLLib
Built on top of Spark, Databricks MLlib provides common machine learning algorithms and evaluation metrics (APIs). Spark MLLib is integrated in Databricks runtime and this library is programmable in Java, Scala, and Python languages.
Pipeline
A Pipeline is a sequence of stages , with a Transformer or an Estimator present at each stage. These stages are run as sequence for transforming the input DataFrame
DataFrame
This ML API fetches DataFrame from Spark SQL as an ML dataset, which may contain different types of values like text, feature vectors, labels, and predictions.
Transformer
A Transformer is defined as an algorithm that transforms a features-based DataFrame into a predictions-based DataFrame.
Estimator
An Estimator is a learning algorithm that trains on a DataFrame to produce a ML model.
ML Monitoring metrics for Databricks-MLLib
List of Metrics for different Classification and Regression Models
Binary Classification Metrics
Precision (Positive Predictive Value), Recall (True Positive Rate), F-measure, Receiver Operating Characteristic (ROC), Area Under ROC Curve, Area Under Precision-Recall Curve.
Multi-class Classification Metrics
Confusion Matrix, Accuracy, Weighted precision, Weighted recall, Weighted F-measure
Multi-label Classification Metrics
Precision, Recall, Accuracy, Precision by label, Recall by label, F1-measure by label, Hamming Loss, Subset Accuracy, F1 Measure, Micro Precision, Micro recall, Micro F1 measure.
Ranking Systems Metrics
Precision at k, Mean Average Precision, Normalized Discounted Cumulative Gain.
Regression Model Metrics
Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination, Explained Variance.
The widespread adoption of Spark in ML based applications has made Databricks a preferred environment for ML monitoring
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