Models often fail not because they lack quality, but due to flaws in the systems that support them. Most machine learning (ML) discussions focus heavily on models, metrics, and academic papers, while neglecting the operational realities that lead to failure. Successful ML implementations require robust data pipelines, effective serving infrastructure, consistent monitoring, and feedback loops. The talk challenges the misconception that a good model alone guarantees success, highlighting that training represents only about 10% of the work, while the rest involves managing the operational complexities. A typical scenario unfolds when a model performs well in a controlled environment but falters once deployed. Increased traffic and changing data can degrade predictions, often without immediate detection. It’s critical to ask system-level questions about data sources, ownership, and response mechanisms when inputs change. More than just software, ML systems evolve like living organisms—they age, drift, and can silently fail. If any part of the system, such as data ingestion or monitoring, is ignored, the entire setup can collapse. Data issues often masquerade as model problems. Differences in data pipelines or preprocessing can lead to significant discrepancies between training and serving data. Users are not just seeking predictions; they want actionable decisions and measurable outcomes. Outputs without proper context can be misleading, which is why confidence, constraints, and business rules must be integrated into the decision-making process. ML systems need to be designed for potential failures, accommodating bad inputs and unexpected traffic spikes. Monitoring traditional metrics is insufficient. Observability in ML demands attention to input and output distributions, decision rates, and overall business impact. A wrong prediction made at scale can have catastrophic consequences. Continuous feedback loops are essential for models to learn from mistakes and reduce bias. The foundation of effective MLOps lies in reliability and ownership, rather than just tools or automation. Simple models and strong defaults tend to be more resilient than complex, fragile systems. The essence of successful ML in production is how well the system manages reality, not just how sophisticated the model is.