Deploying an AI model in a business is a complex job. Organisations often face challenges despite following best deployment practices, adapting proven market strategies, and leveraging cutting-edge technologies.
Generally, AI model deployment faces bottlenecks because of abuse, misuse, bias, lack of integrity features like transparency, and integration issues with existing systems. These challenges can be overcome by data training processes, bias mitigation algorithms, powerful integration strategies, and regulated AI guidelines, as guided by the law of the land.
When deployed in edge devices, the AI model poses challenges, such as limited resources, connectivity, scalability, and security. To get it right, organisations must choose the optimised AI framework for edge devices. Less memory or processing power may be complemented by providing edge cloud services, and security measures like data encryption and access control must be guaranteed by the AI model.
Common Challenges
The involvement of several department experts makes the process error-free. Technically, the IT team assesses the infrastructure needs, data scientists should consider the training data set sourcing.
Developers, on the other hand, must estimate investments in other/related software or systems. From the operational point of view, the AI model must consider inputs from key departments like marketing, sales and human resources to reach the desired organisational purpose.
There are six common AI model deployment challenges starting from the scope preparation of the project to its live functioning. They are as follows -
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Data-set-related challenges
The AI model has its foundation in the training data sets. Data Scientists must ensure the quality and the width of data to determine its accuracy. Some of the common data problems are as follows –
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Imbalanced data
Using the right data is essential. A cloth retailer must not use the shoe data for sizing shirts or dresses.
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Limited data
AI models with insufficient data will never be able to predict desired data with accuracy.
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Poor quality data
The quality of data must be assured before creating the AI model.
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Algorithm-related challenges
The algorithm is the main structure of the AI model. Developers must prepare and train the algorithms so that they suit the project’s goals. The key challenges are as follows –
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Choosing the right algorithms
Having the right algorithms in place is the first step. Algorithms must fulfil the scope and purpose of the project.
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Overfitting
When an AI model delivers a specific outcome repeatedly while ignoring other desired results, the situation is known as the overfitting of algorithms.
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Underfitting
When the AI model delivers the desired outcome with training data sets but fails in the real-world test, the situation is called underfitting of algorithms.
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Hardware and software-related challenges
While Data Scientists lay the foundation of the AI model and Developers craft the algorithm structure, the IT department must take charge of the software and the hardware challenges of the AI model. The considerations are as follows –
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Hardware resources
Processing and reviewing large amounts of data sets in a complex AI model requires huge storage space and server performance.
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Software resources
Specialised software tools and frameworks are sometimes required to get integrated with the existing system to operate the desired AI model.
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Challenges in hiring skilled talent
It may be noted that an AI model can operate to its desired capacity provided it is crafted and operated by trained professionals. The challenges faced in hiring skilled talent are as follows -
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Search for AI talent.
Trained or veteran professionals like Data Scientists, Developers and IT professionals must be hired to train AI models to accuracy and expectation.
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Lack of trained AI professionals
When an organisation works with less trained people, they never reach their desired goal. They tend to lose both in terms of money spent on research and the time lost.
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Challenges in managing AI projects
Organisational AI projects are expensive and resource-heavy. The management, thus, needs to strike a balance between the finance, technology outsourced and operational schedules. Common challenges in managing AI projects are as follows –
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Communication gaps
To implement the AI model, several stakeholders like Data Scientists, Developers and professionals from IT legal and finance departments need to get involved. Gaps in inputs from any of the stakeholders may lead to the loss of time, money and accuracy.
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Unrealistic expectations
Users may expect miracles out of an AI model. They must understand the AI model’s purpose, goals, capabilities and limitations.
Guidelines for AI Deployment
Overcoming AI deployment issues is possible by following these guidelines -
- AI deployment best practices include the establishment of clear policies for AI model usage.
- It is required to collaborate with industry peers, institutions and Government bodies to exchange best practices.
- Access control should be with authorised users only.
- The AI model should be monitored to ensure misuse and abuse.
- The AI model is to be audited regularly for compliance with ethical standards.
AI model implementation also involves machine learning processes. Machine learning deployment challenges are an integral part of the AI model system. Dedicated AI training programs for employees can help an organisation reach that level of supremacy and accuracy in AI model implementation.
Conclusion
The AI model is the business future. Its predictions help organisations plan effectively for targeted business growth. Its implementation comes with enthusiasm and challenges.
The creation of an AI model involves several stakeholders and a pivotal leadership to control the process. It has also crafted a lucrative professional career in the field of machine intelligence.
The Professional Certificate In Product Management with CEC, IIT Roorkee by Imarticus will give the prospective candidates the perfect start at the beginning of their careers.
Visit the official website of Imarticus for more details.
Frequently Asked Questions
What are the ethical considerations in AI deployment?
The process should be bias-free, end-to-end encrypted for privacy, and be fair and legally accountable. Abuse and misuse of AI models must be checked. Bias mitigation algorithms are to be adapted.
How do organisations tackle the AI talent shortage?
The remedy to tackle the AI talent shortage is to upskill the existing AI workforce through reputed institutions or consultants. This also serves as a retention policy.
How can a business improve data?
Having good-quality data is the foundation of the AI model. Data must be fetched from reliable resources.
How can context understanding be improved in the AI model?
Context understanding in AI deployment may be improved through transfer learning, domain adaptation, integrating hybrid models and implementing human-in-the-loops systems.
