Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, and improving manufacturing quality. Traditional ML development cycles take months and require scarce data science and ML engineering skills. Analysts’ ideas for ML models often sit in long backlogs awaiting data science team bandwidth, while data scientists focus on more complex ML projects requiring their full skillset.
To help break this stalemate, we’ve introduced Amazon SageMaker Canvas, a no-code ML solution that can help companies accelerate delivery of ML solutions down to hours or days. SageMaker Canvas enables analysts to easily use available data in data lakes, data warehouses, and operational data stores; build ML models; and use them to make predictions interactively and for batch scoring on bulk datasets—all without writing

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