Data Engineering For Machine Learning: How to load, clean and prepare data.

2026-03-10 · Source: Data Engineering on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

This article details the construction of a practical machine learning data pipeline using Python and PyTorch, specifically for multimodal datasets. It focuses on preparing the MELD dataset, which includes text transcripts, video clips, and audio signals for emotion recognition. The pipeline covers loading structured metadata with pandas, preprocessing text using a BERT tokenizer, extracting and standardizing video frames to 224x224 pixels, and converting audio into Mel spectrograms at a 16 kHz sample rate. It also addresses challenges like handling varying data lengths, normalizing pixel values, reordering tensor dimensions, and gracefully managing corrupted or missing data. The process culminates in implementing a PyTorch `Dataset` class and `DataLoader` for efficient batching and training.

Key takeaway

For AI Engineers building multimodal machine learning systems, understanding and implementing robust data pipelines is crucial. Your model's performance heavily relies on the quality and consistency of its input data. Focus on standardizing data formats, handling missing values, and efficiently batching diverse modalities using tools like PyTorch's `Dataset` and `DataLoader` to ensure a stable and effective training process.

Key insights

Effective multimodal ML requires robust data pipelines for loading, cleaning, and aligning diverse data types.

Principles

• Standardize data dimensions for consistent batching.
• Normalize input values for stable neural network training.
• Handle missing data gracefully to prevent pipeline crashes.

Method

The method involves defining a PyTorch `Dataset` class to load metadata, tokenize text, extract and resize video frames, and convert audio to Mel spectrograms, followed by using `DataLoader` for batching.

In practice

• Use `pandas` for structured metadata loading.
• Employ `cv2` for video frame extraction and resizing.
• Utilize `torchaudio` for audio processing and spectrogram generation.

Topics

• Data Engineering
• Multimodal Data
• PyTorch Data Pipeline
• Data Preprocessing
• MELD Dataset

Best for: AI Engineer, Machine Learning Engineer, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.