Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, choice of algorithms, and other aspects of ML in search of optimal models that generalize well on real-world data and deliver the desired results. Because speed in doing business disproportionately matters, this extremely tedious and iterative process may lead to project delays and lost business opportunities.
Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for ML from weeks to minutes, and Amazon SageMaker Autopilot automatically builds, trains, and tunes the best ML models based on your data. With Autopilot, you still maintain full control and visibility of your data and model. Both services are purpose-built to make ML practitioners more productive

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