24/06/2025
As an ML Engineer, I have to write lots of production ready code. But the tools I used 2-3 years back are outdated now. Hence, I created this updated toolkit for aspiring ML Engineers in 2025 and beyond.
1. Core ML Libraries (Still Relevant & Powerful)
• Scikit-learn – Your go-to for traditional ML (regression, SVMs, trees)
• XGBoost / LightGBM / CatBoost – For tabular data, these remain unbeatable
• TensorFlow & PyTorch – Deep learning’s two titans; PyTorch dominates R&D, TF excels in production
2. NLP & LLM Ecosystem
• Hugging Face Transformers – Pretrained models, tokenizers, fine-tuning: all in one place
• LoRA / PEFT – Finetune massive models cheaply and efficiently
• LangChain / LlamaIndex – Build RAG, chatbots, and LLM apps with few lines
• OpenAI / Cohere / Anthropic SDKs – When you just want to plug in power
3. Data Cleaning & Preprocessing
• Pandas – Still king for fast, intuitive data wrangling
• Polars – A faster, multi-threaded Pandas alternative
• Feature-engine / Sklearn-Pandas – Feature engineering pipelines made easy
4. Experiment Tracking & MLOps
• Weights & Biases (wandb) – Track experiments, compare runs, visualize metrics
• MLflow – Model tracking, packaging, deployment all-in-one
• DVC / Prefect / Airflow – For managing pipelines & reproducibility
• Docker + FastAPI – Deploy ML models in style
5. Visualization & Dashboards
• Seaborn / Matplotlib / Plotly – EDA classics
• Streamlit / Gradio – Instantly create UIs for your models with 5 lines of code
• Dash / Panel – For robust dashboards and more control
6. Other Must-Know Tools
• JupyterLab + VSCode Notebooks – Your coding canvas
• Kaggle Datasets / Notebooks – For quick prototyping
• Colab / Paperspace / Replicate – For free/cheap GPU access
7. Bonus: What's Emerging in 2025?
• Modular .ai – PyTorch-native LLM infra built for scale
• BentoML – A new favorite for model serving
• Pydantic v2 + FastAPI – For ML + API combo with validation
My personal tip:
The tool doesn’t make the engineer - but knowing the right tool makes you faster, better, and more effective developer. You should always:
• Stay updated
• Try new libraries
• Master the ones that matter
I am still novice in 4 and 7, hence I am learning. What about you? What are you learning? Share in comment.
If you are preparing for Data Scientist / ML Engineer role, check-out my ML articles for in-depth theoretical understanding with mathematical intuition [link in comment].
Save this / repost - you'll need it in 2025 and beyond!
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