Machine Learning with Scala Corporate Training

Build Scala ML Pipelines With Distributed Features and Defensible Evaluation

Edstellar develops scalable machine learning skills through Scala and Spark exercises. Participants process DataFrames, train MLlib estimators, and organize feature transformations into an end-to-end pipeline. Cross-validation and tuning complete the build, with review of distributed processing choices, pipeline consistency, evaluation evidence, and the rationale behind the final model configuration.

1. Step 01

   Consult

   We assess programming and data experience, Spark familiarity, and challenges handling large datasets to identify the cohort's Scala, pipeline, and distributed feature-engineering needs.

2. Step 02

   Plan

   We establish Scala and Spark DataFrames before MLlib estimators, then connect pipeline composition, distributed features, and cross-validated tuning in the capstone workflow.

3. Step 03

   Align

   We align Spark ML engineering expertise with data engineering, data science, and platform teams, confirming suitable compute and datasets for observing distributed pipeline behavior.

4. Step 04

   Customize

   We adapt sample large-data tasks to familiar prediction needs, preserving feature-processing and evaluation requirements that participants must carry consistently through the Spark pipeline.

5. Step 05

   Execute

   Participants build DataFrame transformations, train MLlib models, and tune pipelines with cross-validation, receiving feedback on distributed execution, feature consistency, and interpretation of evaluation results.

6. Step 06

   Evaluate

   We assess the pipeline and evaluation defense for coherent transformations, appropriate estimators, reproducible tuning, and conclusions supported by the chosen validation and data-processing approach.