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Large-Scale Data Training Techniques

Build production-ready ingestion systems for LLM training and fine-tuning datasets

12 lessons
4 weeks
1200 XP
Your progress0 / 1200 XP
Click a lesson to start learning!
Lessons
1
Introduction to Large-Scale Data Ecosystems
Navigate the architecture of Spark and Airflow for model training.
Quick winBeginnerMilestone
+50 XP
~5 min
2
Setting Up Your Spark Environment
Configure a local Spark cluster for large dataset processing.
BeginnerPractice
+50 XP
~6 min
3
Basic ETL Patterns with Spark SQL
Learn to extract, transform, and load raw data using Spark.
BeginnerTheory
+75 XP
~8 min
4
Orchestration Fundamentals with Apache Airflow
Build your first DAG to automate simple data tasks.
BeginnerPracticeMilestone
+75 XP
~9 min
5
Cleaning and Normalizing Text Data
Implement regex and normalization for LLM-ready text formatting.
PracticeTheory
+100 XP
~11 min
6
Optimizing Data Shuffling and Partitioning
Master Spark partitioning to handle 10GB+ datasets efficiently.
IntermediateMilestoneMilestone
+100 XP
~12 min
7
Integrating Spark Jobs into Airflow
Use the SparkSubmitOperator to trigger jobs from Airflow.
IntermediatePractice
+100 XP
~14 min
8
Handling Failures and Retries Dynamically
Build resilient pipelines with Airflow error handling and sensors.
IntermediateTheoryMilestone
+125 XP
~15 min
9
Formatting Data for Fine-Tuning
Transform processed data into JSONL and Parquet for LLMs.
PracticeProject
+125 XP
~17 min
10
Scaling Up to 10GB Datasets
Execute a full-scale ingestion run on large datasets.
AdvancedProject
+125 XP
~18 min
11
Documenting Pipelines for Technical Portfolios
Structure your blog post to explain architecture and results.
AdvancedQuick win
+150 XP
~19 min
12
Capstone: End-to-End LLM Ingestion System
Deploy the final integrated pipeline and publish your documentation.
AdvancedMilestoneMilestone
+150 XP
~20 min

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