ML use cases and tooling

Machine learning is usually a prediction tool first and a decision tool second: it learns patterns from past data, then helps software rank, flag, forecast, or route the next action. (developers.google.com, developers.google.com) The first step is problem framing, which means turning a business question into a model output and a success metric. Google’s machine-learning course teaches teams to decide whether they need classification, regression, ranking, clustering, or another approach before they write code. (developers.google.com, developers.google.com) Classification is the yes-or-no bucket: spam or not spam, fraud or not fraud, churn risk or not churn risk. Google’s crash course describes classification models as probability generators that then depend on thresholds and metrics such as receiver operating characteristic and area under the curve. (developers.google.com, learn.microsoft.com) Regression is the how-much bucket: sales next week, travel time, or demand for a product. Google’s problem-framing guide uses changing cache needs as one example where a regression model fits better than a fixed-threshold classifier. (developers.google.com, docs.cloud.google.com) Recommendation systems are ranking machines built for large catalogs. Google’s course breaks them into candidate generation, scoring, and re-ranking, with the last stage used to add constraints such as freshness, diversity, and fairness before showing results to a user. (developers.google.com, developers.google.com) Clustering handles cases where there is no label yet and the goal is to group similar items or users. Google’s clustering course lists similarity measurement, k-means, and quality evaluation as core pieces of that workflow. (developers.google.com) That progression explains why production teams often move from prediction to optimization. Once a model estimates demand, risk, or click probability, a second system can decide which truck to send, which shift to staff, or which order to fulfill under real constraints. (developers.google.com, developers.google.com) Google’s OR-Tools package is built for that second layer. Its documentation says the open-source suite handles vehicle routing, flows, integer and linear programming, and constraint programming, with examples for time windows, capacity limits, and factory scheduling. (developers.google.com, developers.google.com) On the tooling side, TensorFlow and PyTorch remain the two most visible general-purpose frameworks. TensorFlow describes itself as an end-to-end open-source machine-learning platform, while PyTorch calls itself an optimized tensor library for deep learning on graphics processing units and central processing units. (tensorflow.org, docs.pytorch.org) The split in practice is often less ideological than practical. TensorFlow’s official site emphasizes deployment across environments and a broader production ecosystem, while PyTorch documentation centers on model building and performance for deep-learning workloads. (tensorflow.org, docs.pytorch.org) The through line is simple: start with the question, pick the prediction type, measure whether it works, and only then automate the decision around it. That is why the same field can cover a linear regression forecast, a fraud alert, a recommendation feed, and a delivery schedule without changing its basic workflow. (developers.google.com, developers.google.com)