Overview
This module covers essential data management practices for machine learning in production. You’ll learn how to deploy storage systems, work with various data formats, handle streaming datasets, implement vector databases for RAG applications, and set up data labeling workflows.What You’ll Learn
Data Storage
Deploy MinIO locally and on Kubernetes, implement S3-compatible storage, and manage datasets with DVC
Data Formats
Compare storage formats, benchmark pandas performance, and optimize data loading/saving
Streaming Datasets
Create and consume streaming datasets for efficient data loading during training
Vector Databases
Build RAG applications with LanceDB and implement semantic search
Data Labeling
Deploy Argilla for data annotation and create synthetic datasets
Practice Tasks
Complete hands-on exercises to reinforce your learning
Learning Objectives
By the end of this module, you will be able to:- Deploy and configure object storage systems (MinIO, S3)
- Implement Python clients for cloud storage with comprehensive tests
- Benchmark and select appropriate data formats for your use case
- Create streaming datasets for efficient training pipelines
- Build vector databases for semantic search and RAG applications
- Set up data labeling workflows with annotation tools
- Version control datasets using DVC
Module Structure
This module is divided into two main sections:H3: Data Storage & Processing
Focus on storage systems, data formats, and processing performance:- MinIO deployment (Docker, Kubernetes)
- CRUD operations with Python clients
- Data format benchmarking
- Parallel inference optimization
- Streaming datasets
- Vector databases
H4: Data Labeling & Validation
Focus on data quality and annotation:- Labeling guidelines development
- Argilla deployment and usage
- Synthetic data generation
- Data validation techniques
- Dataset versioning with DVC
Prerequisites
- Python 3.10+
- Docker and Kubernetes basics
- Understanding of pandas and NumPy
- Familiarity with S3 storage concepts
Key Technologies
- Storage: MinIO, S3, DVC
- Formats: Parquet, Feather, HDF5, CSV
- Streaming: MosaicML Streaming, WebDataset
- Vector DB: LanceDB, sentence-transformers
- Labeling: Argilla, Label Studio
- Processing: Ray, multiprocessing, concurrent.futures