Implementing Neural Network Solutions in Enterprise Environments from Pluralsight

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.

What's inside

Syllabus

Course Overview

Foundations of Enterprise Neural Networks

Integration, Security, and Compliance in Neural Network Applications

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Tom Taulli, recognized for their work in neural networks and enterprise solutions

Covers essential aspects of neural network implementation in an enterprise setting

Provides strategies for integrating neural networks with ETL and databases

Emphasizes security and compliance considerations in neural network applications

May require prior experience with neural networks and enterprise systems

Career center

Learners who complete Implementing Neural Network Solutions in Enterprise Environments will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with data scientists to identify and prepare data, and with software engineers to integrate models into production systems. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Data Scientist

Data Scientists use machine learning and other statistical techniques to analyze data and extract insights. They work with businesses to identify opportunities for improvement, and develop and implement solutions to solve problems. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They work with machine learning engineers and data scientists to integrate machine learning models into production systems. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Data Analyst

Data Analysts use data to identify trends and patterns, and to make recommendations for improvement. They work with businesses to understand their data, and to develop and implement solutions to solve problems. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Business Analyst

Business Analysts work with businesses to identify and solve problems. They use data to analyze business processes and systems, and to recommend improvements. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Project Manager

Project Managers plan, execute, and deliver projects. They work with teams to define project goals, develop project plans, and track project progress. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Product Manager

Product Managers develop and manage products. They work with customers to understand their needs, and with engineers to design and build products that meet those needs. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Database Administrator

Database Administrators manage databases. They work with data analysts, data scientists, and software engineers to ensure that data is stored and managed efficiently. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Systems Engineer

Systems Engineers design, develop, and maintain systems. They work with hardware and software to ensure that systems meet the needs of the business. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Network Administrator

Network Administrators manage networks. They work with hardware and software to ensure that networks are available and secure. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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Security Analyst

Security Analysts monitor and analyze security systems. They work with security engineers to identify and respond to security threats. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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IT Manager

IT Managers oversee the IT department. They work with senior management to develop and implement IT strategy. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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CIO

CIOs oversee the IT department. They work with senior management to develop and implement IT strategy. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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CEO

CEOs lead and manage companies. They work with senior management to develop and implement business strategy. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

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CFO

CFOs lead and manage the financial department. They work with senior management to develop and implement financial strategy. This course provides a strong foundation in the principles and practices of machine learning, and will help learners develop the skills and knowledge needed to be successful in this field. In particular, the course covers topics such as model deployment strategies, monitoring systems in production, and security, which are all essential for building and maintaining machine learning systems in enterprise environments.

See salaries and explore the career path for CFO