This story was originally published on HackerNoon at:
https://hackernoon.com/modern-world-problems-of-ml-in-production-and-how-to-cope-with-them.
Discover the real-world challenges of deploying machine learning models and explore practical solutions.
Check more stories related to data-science at:
https://hackernoon.com/c/data-science.
You can also check exclusive content about
#ml-model,
#ml-in-production,
#model-drift,
#concept-drift,
#model-drift-detection,
#problems-of-ml-in-production,
#model-interpretability,
#ml-model-data-privacy, and more.
This story was written by:
@viktoriaerokhina. Learn more about this writer by checking
@viktoriaerokhina's about page,
and for more stories, please visit
hackernoon.com.
Deploying machine learning models in real-world scenarios brings challenges like model drift, scalability issues, interpretability concerns, data privacy, and the need for continuous integration/deployment (CI/CD) pipelines. Solutions involve monitoring and retraining for model drift, optimizing model architectures, leveraging hardware accelerators, and implementing explainable AI for model interpretability. Maintaining data privacy involves techniques like differential privacy and federated learning. Establishing robust CI/CD pipelines is crucial, with tools like MLflow and Kubeflow aiding in the process. Real-world examples from companies like Amazon, MobiDev, Citibank, Google, and Netflix illustrate the practical application of these solutions, emphasizing the evolving nature of machine learning challenges and solutions.