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- What You'll Find Inside:
Machine learning is becoming part of the analytics fabric of many organizations as its competitive value becomes more understood.

Once organizations get comfortable with machine learning, they may also use techniques such as deep learning for use cases that often involve image recognition (e.g., identifying objects such as cars for automated auction sites or for medical use cases such as disease testing) or venture into other AI-based technologies such as natural language processing. Although many companies are excited about machine learning, they often overlook some key success factors.

Download this free TDWI report to learn how to avoid common mistakes and embrace the full machine learning life cycle in a unified way—from data management and governance to data engineering to building the model and putting it into production while ensuring that the organizational culture embraces predictive applications.

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Ten Mistakes to Avoidin the Machine Learning Life Cycle

Ten Mistakes to Avoid
in the Machine Learning Life Cycle