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Implementing Neural Network Solutions in Enterprise Environments





Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 75.24 MB | Duration: 33m

This course covers implementing neural network solutions and models in an enterprise. The topics include model deployment strategies, monitoring systems in production, integration with ETL and databases, and security.


Neural networks are rapidly becoming integral to enterprise solutions, revolutionizing various business processes with their advanced analytical and predictive capabilities. Their ability to learn from vast amounts of data and provide insights in real-time is transforming sectors such as finance, healthcare, and customer service, making them indispensable tools for businesses looking to gain a competitive edge.
In this course, Implementing Neural Network Solutions in Enterprise Environments, you'll learn to effectively integrate these advanced neural network technologies into business practices.
First, you'll explore various model deployment strategies, including cloud-based, on-premises, and edge deployments, considering factors like cost, scalability, and data security.
Next, you'll discover best practices for managing neural networks in production, which involve continuous monitoring for performance, versioning and tracking changes, and regular model updates and retraining to ensure system stability and optimal user experience.
Finally, you'll learn about the critical aspects of security and compliance in neural network applications, such as guarding against threats like prompt injection and adversarial attacks, adhering to regulations like GDPR and HIPAA, and implementing strategies for securing model deployments to protect data privacy and integrity.
When you're finished with this course, you'll have the skills and knowledge of how to effectively implement neural network systems in an enterprise.

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https://www.pluralsight.com/courses/implementing-neural-network-solutions-enterprise-environments

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rapidgator.net:

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