ML Infrastructure Tools for Model Building

The machine learning workflow is broadly divided into three stages - data preparation, model building, and production. Model Building involves understanding business needs, feature exploration and selection, model management, experiment tracking, model evaluation, and pre-launch validation. Various ML Infrastructure companies offer platforms for different functions within the Model Building stage. Some of these include Alteryx/Feature Labs, Paxata(DataRobot), H20, SageMaker, DataRobot, Google Cloud ML, Microsoft ML, Weights and Biases, Comet ML, ML Flow, Domino, Tensorboard, Fiddler AI, Arize AI, and Stealth Startups. The challenges in Model Building include reproducibility of models, understanding model performance, and ensuring the model's performance in the experimental stage translates to real-world scenarios.