Choose & Deploy AI Models: A Guide for Industries

The Rise of Specialized AI Models

The rapid evolution of artificial intelligence is leading to a situation where formerly state-of-the-art AI models, like Google Cloud’s Vision AI and Amazon Rekognition, are becoming increasingly widespread.

Despite their effectiveness in certain applications, these generalized solutions often fall short when addressing the unique requirements of specific industries. Organizations aiming for peak accuracy in their AI projects will likely need to explore models tailored to their sector.

Strategies for Obtaining Industry-Specific AI

Several pathways exist for companies to achieve industry-specific AI capabilities. A common strategy involves a hybrid model – leveraging an open-source, generic AI foundation and then refining it through further training to match precise business demands.

Alternatively, businesses can procure complete, ready-to-use solutions from specialized vendors like IBM or C3. A third option, though resource-intensive, involves internal data science teams constructing bespoke models from the ground up.

Let's examine each of these methods in detail, providing insights to help businesses determine the optimal approach for their individual situations.

Exploring the Different Approaches

Each method of acquiring industry-specific AI presents its own set of advantages and disadvantages. The best choice depends heavily on factors such as available resources, technical expertise, and the complexity of the required solution.

1. The Hybrid Approach: Fine-Tuning Open-Source Models

This strategy involves starting with a pre-trained, publicly available AI model and then customizing it using a company’s own data. This can significantly reduce development time and cost compared to building a model from scratch.

However, successful implementation requires a skilled data science team capable of effectively training and validating the model. Careful consideration must also be given to data quality and quantity.

2. Utilizing Third-Party Vendors

Vendors like IBM and C3 offer pre-built, industry-specific AI solutions. This provides a quick and convenient path to implementation, eliminating the need for in-house development.

The trade-off is often reduced customization options and potential vendor lock-in. It’s crucial to thoroughly evaluate vendor offerings to ensure they align with specific business needs.

3. In-House Model Development

Building an AI model entirely in-house grants maximum control and customization. This approach is best suited for organizations with substantial data science resources and highly specialized requirements.

It is also the most time-consuming and expensive option, demanding significant investment in personnel, infrastructure, and ongoing maintenance.

Choosing the Right Path

Selecting the appropriate strategy for obtaining industry-specific AI requires a careful assessment of a company’s unique circumstances. Factors to consider include:

• Budget: How much can be allocated to AI development and implementation?
• Expertise: Does the organization possess the necessary data science skills?
• Data Availability: Is sufficient, high-quality data available for training?
• Customization Needs: How much tailoring is required to meet specific business requirements?

By thoughtfully evaluating these considerations, businesses can make informed decisions and unlock the full potential of AI within their respective industries.

The Limitations of Generalized AI Models

Frequently, broadly applicable AI models, such as Vision AI or Rekognition, alongside open-source alternatives from platforms like TensorFlow and Scikit-learn, prove inadequate for specialized applications.

Industries with specific requirements, including finance and energy, often find these models lacking. The core issue lies in the absence of industry-specific contextual data within the model’s training.

Challenges in Niche Industries

Many organizations possess distinct operational needs. Consequently, models untrained on relevant industry information struggle to deliver pertinent and accurate outcomes.

Contextual understanding is paramount for effective AI implementation. Generic models simply cannot replicate the nuanced insights gained from specialized datasets.

Why Customization is Key

• Generic models lack the specific knowledge base required for complex industry tasks.
• The absence of contextual data leads to irrelevant or inaccurate predictions.
• Businesses with unique needs require AI solutions tailored to their specific challenges.

Therefore, relying solely on off-the-shelf AI solutions can hinder a company’s ability to leverage the full potential of artificial intelligence. A more focused approach is often necessary.

Leveraging and Enhancing Open-Source AI Models

ThirdEye Data recently collaborated with a utility provider to implement an AI-driven system for identifying and categorizing defects in electric poles through image analysis. Initial attempts utilized the Google Vision API, but the outcomes proved unsatisfactory. The precision and recall metrics of the pre-trained models were deemed inadequate for the task.

Specifically, the models struggled to accurately read the characters on the pole tags, failing approximately 90% of the time. This was attributed to the API’s inability to recognize the unconventional fonts and diverse background colors present on the tags.

Consequently, we opted to build upon existing computer vision models sourced from TensorFlow. These foundational models were then meticulously optimized to meet the utility company’s unique requirements.

Following a two-month period dedicated to developing AI models capable of both detecting and interpreting the tags, and an additional two months focused on training, the resulting system now demonstrates accuracy rates exceeding 90%. Continuous improvement is anticipated through ongoing retraining cycles.

Evaluating Generic Models First

Organizations aiming to bolster their AI capabilities should initially evaluate the performance of generic models when applied to their specific data and use cases. Numerous open-source algorithms are readily available within AI and ML frameworks such as TensorFlow, Scikit-learn, and Microsoft Cognitive Toolkit.

At ThirdEye Data, we employed convolutional neural network (CNN) algorithms within the TensorFlow environment.

If the initial results are not sufficient, further refinement can be achieved by training the algorithm on a dataset tailored to the specific industry.

The Power of Industry-Specific Training Data

Consider a tax services firm, operating in Los Angeles and catering to small and medium-sized businesses. Implementing a generic chatbot solution would likely result in limited effectiveness when addressing specialized tax-related inquiries.

However, by training the chatbot on current, industry-specific data – encompassing federal and state IRS regulations, processes, and forms – the model’s ability to provide accurate responses to complex customer questions regarding 2021 tax filings, and associated nuances, is significantly enhanced.

• Training on specialized data improves accuracy.
• Generic models often lack the necessary domain knowledge.
• Continuous retraining is key to sustained performance.

Leveraging Pre-built, Specialized AI Applications

The increasing demand for customized AI is driving significant growth in the market for models pre-trained on data specific to various industries. Numerous such solutions are currently available, largely originating from major technology firms and dedicated AI companies. Further expansion of these offerings is anticipated within the coming five years.

IBM, for instance, provides a comprehensive portfolio of industry-focused AI models, encompassing Watson applications for areas like customer support, supply chain management, financial processes, and cybersecurity, among others. Recently, IBM partnered with Atos to develop specialized automation solutions integrating AI and hybrid cloud infrastructure.

C3 AI, a leading enterprise AI provider, has unveiled a new collection of industry-specific AI solutions. These include applications designed for energy optimization, combating financial crime, and streamlining production scheduling. The company reports having an extensive 4.8 million AI models deployed, delivering “robust predictive capabilities for highly complex organizations,” as stated on their website.

H2O offers solutions spanning financial services, insurance, manufacturing, and retail. Within the retail sector, their AI models have enabled prominent brands such as Macy’s, Walgreens, and eBay to accurately predict product demand, enhance customer interactions through personalization, and optimize inventory control.

Although developers typically do not have complete access to the underlying model, these solutions provide access to a range of supplementary, valuable services. These can be crucial during AI model implementation.

For example, assistance may be required in establishing secure data pipelines – a critical requirement considering the multitude of data sources often involved. Additional services could encompass data preparation, data privacy and security validation, and version control for the AI models themselves.

Consequently, what criteria should organizations employ to determine the optimal strategy for their needs?

Strategic Considerations for AI Deployment

Selecting the optimal strategy for implementing AI solutions tailored to specific industries requires careful evaluation of several key factors.

Organizations contemplating the utilization of open-source models should recognize the inherent advantages of expanding upon existing AI capabilities rather than initiating development from the ground up. Access to the original model's source code empowers data science teams with the ability to continuously refine and evolve the end product.

However, it’s crucial to establish a suitable environment for ongoing model development and training. Platforms like Azure ML provide a developer environment facilitating the import of open-source algorithms and subsequent model construction.

Despite these benefits, adopting an open-source approach can present challenges. Managing models distributed across multiple cloud environments can prove both time-intensive and resource-demanding. Furthermore, the performance and service levels are not guaranteed, as these are not typically part of a paid service agreement – ongoing maintenance falls to the data science team.

Businesses prioritizing guaranteed performance and a comprehensive suite of supporting services may find leveraging an off-the-shelf AI tool to be the more suitable path.

Investing in pre-trained AI models is particularly advantageous for companies seeking long-term deployment, requiring both scalability and reliability. This is often the case for larger enterprises with clearly defined AI requirements and a need for assured service-level agreements.

A potential drawback is the vendor lock-in associated with these models. Developers will generally be unable to migrate AI projects developed within the vendor’s environment to other platforms. Therefore, choosing an out-of-the-box solution represents a significant, long-term commitment, trading ownership for advanced functionality.

For a limited number of organizations, building an AI solution from scratch may be the most appropriate course of action. However, given the substantial time and resource investment required to develop a specialized solution, this approach is only advisable when other options prove inadequate.

Highly specialized industries engaged in pioneering work, such as pharmaceutical manufacturing or medical research, often necessitate the creation and training of custom algorithms. Consider, for instance, researchers employing AI to identify patterns within genetic code to understand a specific disease; pre-built models are unlikely to provide a sufficient foundation for further training in such scenarios.

The ideal path for any company will be dictated by the specifics of the AI project. A thorough assessment of technological and maintenance requirements, alongside budgetary and time constraints, is essential for informed decision-making.

One trend is clear: the demand for industry-specific AI models will continue to grow rapidly in the coming years, as businesses across all sectors recognize their potential for delivering precise and impactful insights.