The Machine Learning Lifecycle: 10 Steps From Problem to Production (And Why Most Projects Fail at Step 3)

Most ML projects fail at problem definition due to unclear goals and incorrect data collection. To succeed, define a specific business problem, determine if ML is the right tool, and establish measurable success metrics. This involves understanding the type of ML problem and collecting the right data. Key steps include problem definition, data collection, data cleaning, exploratory data analysis, feature engineering, model selection, model training, model evaluation, model deployment, and monitoring. Start by defining a clear problem statement and collecting high-quality data.