1) Netflix uses machine learning algorithms driven by Apache Spark to power over 80% of recommendations to its members. 2) Netflix runs A/B tests on recommendation models by first evaluating them offline on historical data and then deploying the best-performing models in live experiments. 3) Netflix's recommendation pipeline involves feature engineering on a standardized data format, training models both locally and in distributed mode, and scoring and ranking items to produce recommendations.