The algorithm works perfectly on the training sample

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Lack of a clear goal. Many companies implement ML for the sake of a trend, which leads to expenses without tangible benefits.
Model overfitting: but generalizes poorly to new data.
Underestimating infrastructure. ML requires powerful servers and tools to work with large volumes of data.
Recommendations for risk reduction
Develop models based on real-world use cases.
Conduct stress testing to validate models on new data.
Use platforms that support data protection to avoid leaks.
Machine learning

Development cyprus bulk sms packages and long-term implementation of machine learning in business
Analysis of current trends and technologies
Important indicators, data and metrics
Machine learning continues to evolve. Key trends today include:
Process automation: More than 45% of companies use ML to automate various business operations. Examples include automated scheduling or payment analysis.

Model interpretability: As models like neural networks become more complex, there is a need for tools to explain AI decisions. Solutions like LIME and SHAP help companies explain algorithms to users and regulators.