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Day 6/14 - Databricks Al Challenge
From Chaos to Clarity: Architecting a Production-Ready AI Pipeline to Predict 519,144 Match Outcomes ππ
@databricks
@sahibmusharraf_
@codebasicshub
@indiandataclub
#DatabricksWithIDC #Databricks
Day 5/14 - Databricks Al Challenge Diving into my journey into the Databricks. Worked on feature table and perform validation using test train split. Grateful to
@databricks
@sahibmusharraf_
@codebasicshub
@indiandataclub
#DatabricksWithIDC #Databricks
Day 14 of 14 Days AI Challenge 2
Built a complete end-to-end ML system using Bronze β Silver β Gold architecture. Integrated data pipeline, model training, MLflow tracking, and batch predictions with monitoring.
@databricks
@codebasicshub
@indiandataclub
#DatabricksWithIDC
Day 13 of 14 Days AI Challenge 2
Designed an end-to-end ML pipeline using Bronze β Silver β Gold architecture, mapped the data flow, and defined a simple model retraining strategy.
@databricks
@codebasicshub
@indiandataclub
#DatabricksWithIDC #data #jobreceh
Day 12 of 14 Days AI Challenge 2
Explored cost optimization in Databricks.
Analyzed runtime, reduced unnecessary actions, and used partition filtering to improve efficiency.
@databricks
@codebasicshub
@indiandataclub
#DatabricksWithIDC #DataEngineering
Day 11 of 14 Days AI Challenge 2
Explored Delta Time Travel and Data Recovery.
Appended records, queried older versions, and tested table rollback.
Understanding production data versioning in Databricks.
@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC #DeltaLake
Day 10 of 14 Days AI Challenge 2
Analyzed query execution using .explain(), applied partition pruning, and compared execution times to understand Spark performance behavior.
Improving query efficiency in Databricks.
@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC
Day 9 of 14 Days AI Challenge 2
Built a Recommendation System using ALS
Created rating mapping Trained collaborative filtering model
Generated Top 5 recommendations
@databricks
@codebasicshub
@indiandataclub
#DatabricksWithIDC #ALS #MachineLearning
Day 8 of 14 Days AI Challenge 2
Built a Batch Inference Pipeline
Scored 3M+ users Saved predictions to Gold Delta
Created percentile based user segments
Used ranking instead of fixed thresholds for imbalanced data
@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC
Day 7 of 14 Days AI Challenge 2
Used MLflow to track experiments, log model metrics, and version Logistic Regression and Random Forest models. Compared runs to identify the best performer.
@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC #MLflow #MachineLearning
Day 6 of 14 Days AI Challenge 2
Trained Logistic Regression and Random Forest on 2.4M+ records. Tuned parameters and compared AUC to select the best model.
@Databricks @codebasicshub @Indiandataclub
#DatabricksWithIDC #MachineLearning #SparkML
Thankful to the team of @databricks @codebasicshub @indiandataclub for this challenge.
#DatabricksWithIDC
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