The Essential Elements of Structuring AI Models for Production

The integration of Artificial Intelligence (AI) into various industries has brought new opportunities for improvement and innovation. When planning an AI model to enhance a specific service, several crucial elements must be taken into account. These components play a significant role in determining the complexity and success of the project. In this post, we will delve into these factors, showcasing their individual importance and the interrelated nature that can impact the outcome of the project. Understanding these factors is key to effectively planning and executing a successful AI project.

Let's examine the key elements for structuring and planning successful AI deployment in production:

• The service that businesses aim to improve with AI can vary, from customer service to marketing to process optimisation. It's important to clarify how the AI model will be utilized, for instance, for discovery, recommendation, insights, or automation. The level of automation is determined by the model's accuracy and vice versa. Due to the "black box" nature of many AI models, conducting a comprehensive sensitivity analysis is crucial to successfully automate processes with AI. Different industries also have varying sensitivity and risk factors based on the services offered, particularly in sensitive industries like finance and healthcare, which have unique constraints such as legal and ethical considerations, reputation, and liability."

• The data that AI models can be fed with includes various input types including tabular data, images, text, and time-based data. Each data type requires different handling, expertise, and sometimes, models. Outputs from AI models can vary from strings, numeric values, vectors to more advanced forms like images, text, and sequences. It's crucial to comprehend the input and output of a model to design the proper pipeline for integrating into and with the model.

• The AI model's hosting platform can range from cloud-based solutions, to on-premise systems, to mobile devices, each with its own distinct features and restrictions. The platform choice affects various aspects such as the model's size and intricacy, data volume, and available resources for deployment and upkeep. Additionally, different platforms call for varying code dependencies, libraries, and expertise. Knowing the advantages and limitations of each platform is also key for a successful AI deployment.

• Machine learning (ML) entails training models to make predictions or decisions based on data. There are multiple machine learning techniques, such as supervised learning, unsupervised learning, and reinforcement learning. Each has its own computation methods, like classification and regression in supervised learning and clustering and association in unsupervised learning. If data and service are properly analyzed and limitations of the hosting platform are known, setting up machine learning should be straightforward. However, there is some room for error, like using a hammer instead of a screwdriver. An important milestone in this step is determining the appropriate metric for evaluating the model's quality.

• Once the ML method is selected, the next step is choosing an algorithm and setting its hyperparameters. The algorithm should handle the data complexity and scale as well as to enable training that deliver optimal results. Therefore, in addition to setting the algorithm’s architecture, hyperparameters are set to achieve an improved outcome. With advancements in ML, a variety of algorithms are now available, including a wide range of decision trees, SVMs, and neural networks. The algorithm selection can be done through auto-ml or through a manual ROI analysis, considering the accuracy’s impact on the outcome (e.g. revenue).

Choosing the right setup for the interlinked five elements requires both a strong grasp of the business and a sound engineering framework. For example, the use of sentiment classification from text reviews, can vary greatly depending on the platform it is trained on, such as mobile or cloud, or the purpose it is being used for, like a dating app or homeland security. By following a thoughtful approach, businesses can develop AI models that are efficient, effective and scalable, thereby aiding their automation and goal achievement.

From the AI Lab to Production

Recent studies indicate that the majority of investments in artificial intelligence (AI) do not meet production. This presents a significant challenge as negative experiences may discourage stakeholders from supporting future AI-based projects, and those less experienced may not adequately assess associated risks. In future posts, we will delve into the underlying causes of low yield in AI-driven projects and propose methods for mitigating such issues. In this post, I would like to highlight the main factors that contribute to the successful implementation of an AI project that reaches production.

To ensure a successful AI project that reaches production, I believe that there are five important factors that must be taken into account:

• Finding Talent - Task fit. An effective AI project team should consist of individuals with a range of skills, such as applied science, software development, domain expertise, product understanding, and more. However, it's also essential to match team members' qualifications and experience to avoid over- or under-qualification. Note, that while AI projects may involve scientific research, the ultimate goal is to solve a problem in a practical and straightforward manner. A production-focused mindset, rather than a purely research-oriented mindset, is necessary to ensure success in delivering practical solutions.

• Total complexity analysis is an essential process for evaluating and comprehending the entirety of a problem or situation, including all its interconnected components. It is critical to consider not only the technical aspects, such as algorithms and the code hosting framework, but also the legal and domain-related factors that may impact the project. By adopting a holistic perspective, it becomes possible to identify potential bottlenecks, risks, and opportunities to leverage existing tools. This comprehensive understanding of the problem at hand allows for the development of effective and efficient solutions, while also providing a clear understanding of the resources and expertise that may be required to overcome any obstacles and achieve the desired outcome.

• Clear and well-defined expectations are a must-have for an AI project, not only for stakeholders but also for all the disciplines involved in its execution. By setting specific goals, actionable tasks, and outlining responsibilities, all parties can understand their role and what is expected of them.

• Have a return of investment (ROI) perspective, AI projects require a careful evaluation of the actual value that deep models can bring to the table. This approach forces decision-makers to consider what kind of AI capability is necessary for the task at hand, and if a simpler alternative would suffice. For example, one could determine whether a large custom made deep neural network model is necessary, or if simpler models can be used initially until customer traction is gained. This approach allows for a more informed investment decision, and helps to mitigate the risk of expensive investments becoming obsolete either due to change in demand or as as new technologies emerge.

• A clear and well-defined working method is essential for a successful project. It helps to ensure that everyone involved understands their roles, responsibilities, and ownership rights. However, there can be tension between having clear rules and maintaining a sense of ownership and creativity. Finding the right balance between structure and flexibility is key. As the field of AI is relatively new, there is no "textbook" approach and organizations may need to experiment and learn through trial and error to find a method that works best for their specific setting.

In future posts we will break down those elements as well as propose methods to increase the yield in AI-driven projects on their way to production.