There was a time when Python was just my go-to for scripts, cleaning localisation data, or automating little tasks. But along the way, it quietly became the language of AI—and I’m thrilled.

Whether you're into MLOps-style workflows, tinkering with local models, fine-tuning prompts, or building self-hosted intelligence tools, Python offers an incredible playground. I’ve grouped some core libraries by purpose, added pip install commands, and linked each to its PyPI page so you can dive straight in.

📊 Data Manipulation & Analysis

NumPy

pip install numpy

Python’s foundational library for numerical computing. It gives you arrays and matrix operations that everything else is built on.

Use case: If you're doing anything vector-based—embedding comparisons, token-level analysis, or building scoring systems—you’ll want NumPy in your corner.

Pandas

pip install pandas

The go-to for working with structured data (spreadsheets, CSVs, API responses). Think: tabular data made easy.

Use case: Analysing TMX or bilingual corpora, parsing QA reports, or converting between formats.

📊 Visualization

Matplotlib

pip install matplotlib

Low-level yet powerful—great for custom plots of model metrics or scoring trends.

Use case: Create visualisations for training data, fine-tuning results, or error analysis.

Seaborn

pip install seaborn

Sits prettily on Matplotlib. Use it for clean visualisations of BLEU scores, clusters, or quality-estimation trends.

Use case: Make your graphs more informative and more beautiful with minimal effort.

🏋️‍♂️ Machine Learning

scikit-learn

pip install scikit-learn

Great for lightweight ML—fuzzy matching, classification, clustering, language detection, or simple quality estimation.

Use case: Train classic models on pre/post-edited segments or feature-based metrics for MT evaluation.

XGBoost

pip install xgboost

Serious structured-data modeling with speed—perfect for model-backed QA pipelines or prediction tasks.

Use case: Build high-performance models for scoring, anomaly detection, or structured classification.

👩‍🧬 Natural Language Processing

Gensim

pip install gensim

Handy for topic modelling, terminology extraction, semantic similarity, or corpora exploration.

Use case: Build semantic similarity models or run topic clustering across multilingual datasets.

Transformers (Hugging Face)

pip install transformers

State-of-the-art models—fine-tune or run BERT, T5, LLaMA, Whisper. Useful for translation, summarisation, semantic tagging, and more.

Use case: Drop-in access to powerful NLP models with options for self-hosting and prompt experimentation.

🧠 Deep Learning

TensorFlow

pip install tensorflow

Flexible and enterprise-ready; though heavier, it’s an option for robust backend pipelines or research deployments.

Use case: Create scalable deep learning models for complex language or vision tasks.

PyTorch

pip install torch

My personal favourite—great for research-style projects, custom fine-tuning, and prototyping MLOps pipelines.

Use case: Rapid experimentation with custom architectures, fine-tunes, and small-scale hosting.

📷 Computer Vision

OpenCV

pip install opencv-python

Vital for image-based workflows like OCR, visual QA, or automated inspection in localisation contexts.

Use case: Integrate OCR and layout detection into localisation QA or document automation.

Dlib

pip install dlib

Useful for face/object detection complementing OpenCV for visual data extraction.

Use case: Explore or experiment with image feature extraction or visual comparison pipelines.

📚 Final Thoughts: Python Is Home

Python has been my happy place long before "MLOps" became a buzzword. It's human-readable, hackable, and now the AI language hub.

For anyone in MLOps, LangOps, hybrid automation, or hobbyist AI—this toolkit gives you building blocks to explore, iterate, and own your intelligence workflows.

💡 Tinker as you build. Break things. Remix them. Learn by poking at them until they do something interesting. Python will help you glue it back together.

Happy tinkering! 🤖