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Craig Lincoln
Senior Data Scientist

The AI Development Process Deconstructed: From Concept to Deployment

In this post, we break down the AI development process, from the initial planning phases to data collection, model training and testing, deployment, and monitoring

At first glance, the AI development process can seem complex, involving multiple stages from conceptualization to deployment. Understanding this process is crucial for organizations seeking to leverage AI to drive innovation and growth. 

In this post, we will provide an overview of the AI development process, highlighting the importance of ethical considerations at each stage. We will also explore how Daiki can help organizations navigate the complexities of AI development and develop their AI systems in an ethical and responsible manner. This article is intended for AI development teams, ethical AI practitioners, and organizations interested in developing ethical AI systems.

Understanding the AI Development Process

To understand the AI development process, we must combine fundamental aspects of business analysis, machine learning, and software development. Since there is no single AI development process, the overall procedure is adapted to business needs, resources, and a team’s way of working (see Project Management for Responsible AI). 

Typically, stages such as business understanding, data engineering, model development, deployment, and monitoring are mentioned in relation to AI development processes. Each of these stages consists of various points to be considered and can vary depending on the overall machine learning lifecycle maturity.

1. Business understanding: Identifying business problems, requirements, and opportunities where AI can be applied (use case discovery).

2. Data engineering: Involves general topics around data, from collection to processing and cleaning for consumer-ready datasets.

3. Model development: This is the core phase of AI development, where the actual model is built. It involves selecting appropriate machine learning algorithms, training the model with prepared data, and tuning the model to improve its performance.

4. Deployment and monitoring: Bringing developed models into a production environment, including monitoring solutions and machine learning lifecycle considerations.

It is essential to include ethical considerations in all stages to ensure AI systems are developed in an ethical and responsible manner. This includes addressing issues such as data privacy, bias, and transparency, as well as documentation and handling of potential failure modes.

Planning: Laying the Foundation

Planning is an important step in the AI development process, as it lays the foundation for the entire project and sets up the project for success. Identifying business objectives and goals is crucial not only to define the scope and desired outcome of the project but also to involve domain experts and developers in understanding the problem the AI is supposed to solve.

Based on the identified project goal and requirements, a project team can be assembled, and roles can be defined. Teams involved in AI development are often multidisciplinary, including domain experts such as business owners, project managers, data scientists, and engineers.

Including ethical considerations already in the project planning phase is recommended. Topics such as risk impact analysis and ethical assessments of data sources and models are important to cover throughout the project to develop fair and transparent technologies.

Data Collection: Fuel for AI

Data is the fuel that powers AI systems, and its quality and relevance are crucial for the success of AI development projects. AI systems learn from data, and the accuracy and effectiveness of the system rely heavily on the quality of the data used for training. Hence, it is important to outline an organizational data strategy, including (but not limited to) the following considerations: data requirements, data sourcing, data collection methods, and data quality assessment.

Data requirements: Before collecting data, it is essential to define the data requirements for the AI project. Not only data that serves as an input to the model is considered here but also what the desired output, i.e., the label, will be. To train AI models in a supervised manner, both input and output data must be collected.

Data sourcing: To identify potential data sources, organizations can leverage internal, external, as well as public data sources. As mentioned above, it is crucial to maintain data privacy and ethical guidelines when collecting and using data.

Data collection methods: A potential data collection strategy must consider that to train AI models in a supervised manner, both the input as well as the desired output (label) are needed. Depending on the problem tasks, the desired label might already be available in the data, e.g. deriving customer churn based on inactivity. If the output label is not directly available, internal or external labeling campaigns are required, relying on human observations to assign the correct label, e.g., marking emails as spam or classifying the sentiment of a customer email.

Additional data collection options include integrations into the customer journey, e.g., thumbs up or down feedback. Moreover, organizations must comply with data protection regulations and ethical guidelines when collecting and using data. This includes obtaining informed consent from data subjects, ensuring data security, and being transparent about data usage.

Data quality assessment: Once the data is collected, it is essential to assess its quality, accuracy, completeness, and consistency. Checking for errors, inconsistencies, and missing values, but also assessing potential collection biases, are involved at this stage.

Finally, once the data collection is in rapid progress, data engineering, pipelines including data integration, transformation, and cleaning can be prepared to ensure a high-quality data set is ready for downstream machine learning.

Model Training: Teaching the AI

In the AI development process, model training is the core phase of AI development where the actual model is built. It involves selecting the appropriate machine learning algorithms, training the model with prepared data, and tuning the model to improve its performance. The goal of model training is to develop a model that can accurately perform the intended task, such as image recognition, natural language processing, or predictive analytics.

As navigating the vastness of machine learning algorithms and architectures can be a challenging task, starting with the most simple model is often the recommended approach. Simple baseline heuristics allow assessment of the initial performance and are a good starting point for further development (See Doing AI Right: How to Successfully Start Your First AI Project). Depending on the task at hand, different machine learning algorithms might be preferred, e.g., using decision trees or random forests on tabular data, or using deep neural networks for images or natural language processing.

As mentioned, AI models that are trained in a supervised manner are fed with input and corresponding output data to iteratively identify patterns and adjust internal parameters to minimize errors. An often-mentioned challenge is the so-called bias-variance tradeoff in machine learning. Ideally, the model strikes the right amount of complexity to not miss the relevant relations between features and target outputs (underfitting) while not being too complex and modeling the noise in the data (overfitting). Techniques such as data splitting, cross-validation, regularization, and hyperparameter tuning are essential to improve the performance of the model and reduce the risks of model overfitting.

Testing: Ensuring Reliability and Performance

While modeling is at the core of AI development, the importance of rigorous testing cannot be overstated, as AI systems are increasingly being used in critical applications such as healthcare, finance, and transportation, where reliability and accuracy are paramount.

Several types of tests are conducted during the AI development process. Unit tests focus on individual components of the AI system, such as a specific algorithm or module, to ensure they function correctly. Integration tests, on the other hand, examine how different components work together to ensure the system functions as a whole. System tests, also known as end-to-end tests, simulate real-world scenarios to ensure the AI system performs as expected in different situations.

While these tests ensure the system works reliably, measuring performance metrics or key performance indicators is an important part of the evaluation process. Metrics depend on the specific application, e.g., accuracy, precision, and recall for classification, or can even be tailor-made for specific business needs.

It is important to include feedback from domain experts, users, and business owners to ensure the desired business criteria are met. Such iterative feedback cycles are crucial and should be considered throughout the machine learning lifecycle as they allow for continuous improvements of the AI system.

Deployment: Bringing AI into Real-World Use

Deployment of AI models involves bringing the AI model into real-world use. Several factors require careful consideration at this stage, including the integration of the AI model with existing systems, scalability, data security, and privacy, and the monitoring and maintenance of the AI system post-deployment.

Best practices for deploying AI models include testing and validating the model on real-world data, automating and scaling the deployment process, and continuously monitoring and updating the model to ensure its performance and accuracy. Continuous integration and deployment can assist in ensuring the AI model is properly integrated with other systems and that any issues or errors are identified and addressed quickly.

An important aspect of the deployment process to consider is subsequent monitoring solutions, e.g., via logs or dashboards, to ensure the system is performing as expected. Monitoring includes not only model predictions but also the input data quality, as any issues or unexpected values in the data processing can cause potential model failures. Therefore, it is important to maintain and update AI systems over time, considering the insights gained to ensure the system remains accurate and reliable.

Post-Deployment: Life After Launch

Once an AI system has been deployed, the work is not yet done. In fact, the post-deployment phase is vital to ensure the long-term success of AI solutions. Here are some key aspects of managing AI applications after deployment:

Ongoing ethical use of AI: Organizations must ensure their AI systems continue to operate ethically and responsibly over time, requiring ongoing monitoring and assessment of the system’s performance, as well as a process for addressing any ethical concerns that arise.

Expanding functionality: As AI systems prove their value, organizations may want to expand their functionality. This can involve integrating AI with other systems, developing new use cases, or applying AI to new domains. However, such changes can also introduce new ethical considerations, and organizations must be prepared to address these issues proactively.

Gathering user feedback for future iterations: User feedback is essential for improving AI systems and ensuring they meet the needs of stakeholders. Organizations should establish mechanisms for gathering feedback from users and incorporate the feedback into future refinements of their AI systems. 

Mastering The AI Development Process with Daiki

The AI development process can be challenging at first, but with a comprehensive understanding of the various stages involved, the process can be mastered easily. Integrating ethical considerations into the individual stages, such as business understanding, data engineering, ML modeling and deployment, is crucial to aligning systems with organizational values and principles. 

At Daiki, we aim to facilitate the development process while integrating ethical considerations into every stage. Our SaaS platform offers structured and standardized processes for AI development projects and ensures compliance via our document and quality management system. Leveraging a structured approach reduces the risk of ethical breaches and ensures that ethical concerns are addressed from the outset. As such, organizations can develop AI systems responsibly and ethically, driving innovation and growth while maintaining ethical standards.

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