{"id":266508,"date":"2024-10-21T14:08:25","date_gmt":"2024-10-21T14:08:25","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=266508"},"modified":"2024-10-21T14:08:25","modified_gmt":"2024-10-21T14:08:25","slug":"challenges-in-ai-model","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/challenges-in-ai-model\/","title":{"rendered":"Common Challenges in AI Model Deployment and How to Overcome Them"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Deploying an <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> 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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Generally, <\/span><span style=\"font-weight: 400;\">AI model <\/span><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When deployed in edge devices, the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> 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 <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\">.\u00a0\u00a0\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Common Challenges\u00a0\u00a0<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Developers, on the other hand, must estimate investments in other\/related software or systems. From the operational point of view, the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> must consider inputs from key departments like marketing, sales and human resources to reach the desired organisational purpose.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are six common <\/span><span style=\"font-weight: 400;\">AI model deployment challenges<\/span><span style=\"font-weight: 400;\"> starting from the scope preparation of the project to its live functioning. They are as follows -\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><strong>Data-set-related challenges\u00a0<\/strong><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The<\/span><span style=\"font-weight: 400;\"> AI model<\/span><span style=\"font-weight: 400;\"> 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 \u2013<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Imbalanced data\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Using the right data is essential. A cloth retailer must not use the shoe data for sizing shirts or dresses.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Limited data\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">AI models with insufficient data will never be able to predict desired data with accuracy.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Poor quality data<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The quality of data must be assured before creating the AI model.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><strong>Algorithm-related challenges\u00a0<\/strong><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The algorithm is the main structure of the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\">. Developers must prepare and train the algorithms so that they suit the project\u2019s goals. The key challenges are as follows \u2013<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Choosing the right algorithms\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Having the right algorithms in place is the first step. Algorithms must fulfil the scope and purpose of the project.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Overfitting\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">When an <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> delivers a specific outcome repeatedly while ignoring other desired results, the situation is known as the overfitting of algorithms.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Underfitting\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">When the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> delivers the desired outcome with training data sets but fails in the real-world test, the situation is called underfitting of algorithms.\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><strong>Hardware and software-related challenges\u00a0<\/strong><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">While Data Scientists lay the foundation of the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> 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 \u2013<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Hardware resources\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Processing and reviewing large amounts of data sets in a complex <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> requires huge storage space and server performance.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Software resources\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Specialised software tools and frameworks are sometimes required to get integrated with the existing system to operate the desired <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><strong>Challenges in hiring skilled talent\u00a0<\/strong><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It may be noted that an <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> 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 -\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Search for AI talent.\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Trained or veteran professionals like Data Scientists, Developers and IT professionals must be hired to train AI models to accuracy and expectation.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Lack of trained AI professionals\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><strong>Challenges in managing AI projects\u00a0<\/strong><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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 \u2013<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Communication gaps\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h4><span style=\"font-weight: 400;\">Unrealistic expectations\u00a0<\/span><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Users may expect miracles out of an <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\">. They must understand the AI model\u2019s purpose, goals, capabilities and limitations.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Guidelines for AI Deployment<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Overcoming AI deployment issues<\/span><span style=\"font-weight: 400;\"> is possible by following these guidelines -\u00a0\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI deployment best practices<\/span><span style=\"font-weight: 400;\"> include the establishment of clear policies for <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> usage.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It is required to collaborate with industry peers, institutions and Government bodies to exchange best practices.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Access control should be with authorised users only.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> should be monitored to ensure misuse and abuse.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> is to be audited regularly for compliance with ethical standards.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> implementation also involves machine learning processes. <\/span><span style=\"font-weight: 400;\">Machine learning deployment challenges<\/span><span style=\"font-weight: 400;\"> are an integral part of the<\/span><span style=\"font-weight: 400;\"> AI model<\/span><span style=\"font-weight: 400;\"> system. Dedicated <\/span><a href=\"https:\/\/imarticus.org\/executive-programme-in-ai-for-business-iim-lucknow\/\"><span style=\"font-weight: 400;\">AI training programs<\/span><\/a><span style=\"font-weight: 400;\"> for employees can help an organisation reach that level of supremacy and accuracy in <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> implementation.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Conclusion\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> is the business future. Its predictions help organisations plan effectively for targeted business growth. Its implementation comes with enthusiasm and challenges.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The creation of an <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\"> 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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The Professional Certificate In Product Management with CEC, IIT Roorkee<\/span><span style=\"font-weight: 400;\"> by Imarticus will give the prospective candidates the perfect start at the beginning of their careers.\u00a0\u00a0\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Visit the official website of <\/span><a href=\"https:\/\/imarticus.org\/\"><span style=\"font-weight: 400;\">Imarticus<\/span><\/a><span style=\"font-weight: 400;\"> for more details.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Frequently Asked Questions<\/span><\/h3>\n<p><b>What are the ethical considerations in AI deployment?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><b>How do organisations tackle the AI talent shortage?\u00a0\u00a0\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><b>How can a business improve data?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Having good-quality data is the foundation of the <\/span><span style=\"font-weight: 400;\">AI model<\/span><span style=\"font-weight: 400;\">. Data must be fetched from reliable resources.<\/span><\/p>\n<p><b>How can context understanding be improved in the <\/b><b>AI model<\/b><b>?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Context understanding in AI deployment may be improved through transfer learning, domain adaptation, integrating hybrid models and implementing human-in-the-loops systems.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deploying an AI model in a business is a complex job. Organisations often face challenges despite following best deployment practices,...<\/p>\n","protected":false},"author":1,"featured_media":266509,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24],"tags":[4885],"pages":[],"coe":[],"class_list":{"0":"post-266508","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-technology","8":"tag-ai-model"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Common Challenges in AI Model Deployment and How to Overcome Them<\/title>\n<meta name=\"description\" content=\"Interested in knowing about the common challenges in AI Model deployment? 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