Manual ML Workflows

Manual ML workflows refer to the process of building, training, and deploying machine learning models through a series of human-driven, iterative steps without full automation. This involves tasks like data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation, typically performed using code and tools like Jupyter notebooks or scripts. It emphasizes hands-on experimentation and expert oversight, contrasting with automated machine learning (AutoML) approaches.

Also known as: Hand-crafted ML pipelines, Custom ML workflows, Traditional ML development, Code-first ML, Manual machine learning

Why learn Manual ML Workflows?

Developers should learn manual ML workflows when working on complex, domain-specific problems where custom model architectures or nuanced feature engineering are required, such as in research, healthcare, or finance. It provides greater control and interpretability, allowing for fine-tuning and debugging that automated systems might miss. This approach is essential for gaining deep understanding of ML concepts and for projects where transparency and model explainability are critical.

Learning Resources

• docs: Scikit-learn Documentation: Getting Started
• course: Coursera: Machine Learning by Andrew Ng
• tutorial: Kaggle Learn: Intro to Machine Learning
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book)
• video: YouTube: Machine Learning Workflow Tutorial by sentdex