Machine Learning Developer Checklist: Are You Production Ready? (2026)

Can apply concepts like distributions, hypothesis testing, and Bayesian inference to model evaluation and uncertainty quantification.

University courses, 'Introduction to Statistical Learning' book

Understands matrix operations, eigenvectors, and gradients for implementing and debugging neural network training.

Khan Academy, 3Blue1Brown YouTube series

Can implement, evaluate, and tune models like Random Forests, SVMs, and Gradient Boosting for tabular data.

Scikit-learn documentation, Kaggle micro-courses

Proficient in cross-validation, bias-variance tradeoff, and using metrics like precision-recall, F1, and ROC-AUC.

ML course modules on validation, 'Hands-On ML' book

Can clean data, handle missing values, encode categorical variables, and create informative features.

Pandas tutorials, Kaggle feature engineering competitions

Can build and train feedforward, convolutional, and recurrent networks from scratch or using high-level APIs.

Deep Learning Specialization (Coursera), PyTorch tutorials

Can efficiently use tensors, autograd, and modules to build custom models and training loops.

Official PyTorch/TensorFlow guides, 'Deep Learning with PyTorch' book

Experienced with tools like Optuna or Ray Tune to systematically optimize learning rates, architectures, and more.

Documentation for Optuna, Hyperopt, or Weights & Biases

Can implement architectures like ResNet for tasks like image classification, object detection, and segmentation.

Fast.ai course, OpenCV tutorials, torchvision

Can build models for text classification, named entity recognition, and sentiment analysis using embeddings and RNNs/CNNs.

Hugging Face Course, 'Speech and Language Processing' book

Understands and can implement attention mechanisms and fine-tune pre-trained models like BERT or GPT for specific tasks.

The Illustrated Transformer blog, Hugging Face Transformers library

Can implement Q-learning, policy gradients, or use stable-baselines3 for simple environments.

Spinning Up in Deep RL, OpenAI Gym documentation

Can deploy models as REST APIs using Flask/FastAPI or containerize them with Docker for cloud services.

FastAPI tutorials, Docker for ML courses

Uses MLflow to log parameters, metrics, and artifacts to reproduce and compare model runs.

MLflow quickstart, Databricks community edition

Can manage model versions, stage transitions (staging to production), and lineage using MLflow or similar.

MLflow model registry guide, DVC tutorials

Understands CUDA basics to leverage GPU acceleration in PyTorch/TensorFlow for faster training and inference.

NVIDIA CUDA toolkit docs, PyTorch CUDA semantics guide

Can set up automated testing, building, and deployment of ML models using GitHub Actions or Jenkins.

GitHub Actions for ML, MLOps Zoomcamp

Implements logging and alerting for performance degradation and data/concept drift in live systems.

Evidently AI, Amazon SageMaker Model Monitor

Can read recent ML papers from arXiv/NeurIPS and implement key algorithms or reproduce results.

Papers With Code, ML conference proceedings

Proficient in using and fine-tuning state-of-the-art models from the Hugging Face Hub for complex NLP tasks.

Hugging Face advanced tutorials, transformer model documentation

Understands and can implement generative models like GANs or Diffusion Models for image/text generation.

GAN specialization (Coursera), Denoising Diffusion Probabilistic Models paper

Can adapt large pre-trained LLMs using techniques like LoRA or prompt tuning for specific applications.

Hugging Face PEFT library, OpenAI fine-tuning guide

Applies techniques like quantization, pruning, and knowledge distillation to optimize model size and speed.

PyTorch quantization, TensorFlow Model Optimization Toolkit