What are common AI model development pitfalls?

Artificial intelligence (AI) is a powerful and exciting field of computer science that can create amazing applications and solutions. However, developing AI models is not an easy task and it requires careful planning, testing, and evaluation. In this article, you will learn about some of the common pitfalls that can affect the quality, performance, and reliability of your AI models and how to avoid them.

1 Data issues

One of the most critical aspects of AI model development is the data that you use to train, validate, and test your models. Data issues can include insufficient data, biased data, noisy data, or inconsistent data. These issues can lead to poor generalization, overfitting, underfitting, or inaccurate results. To prevent data issues, you should always perform data analysis, cleaning, preprocessing, augmentation, and splitting before feeding your data to your models. You should also use appropriate data sources, formats, and labels that match your problem domain and objectives.

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Mehul Sachdeva

Lead SDE @ Bank of New York | CSE, BITS Pilani | MITACS GRI 2022 | Apache Iceberg, Contributor | Dremio | Samsung Electronics
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Common pitfalls in AI model development include insufficient data quality, leading to biased or inaccurate models. Overfitting may occur when a model fits the training data too closely, performing poorly on new data. Lack of interpretability hinders understanding and trust in complex models. Ignoring ethical considerations, such as fairness and privacy, can lead to unintended consequences. Inadequate model evaluation and validation may result in misleading performance metrics.

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2 Model issues

Another common pitfall of AI model development is choosing or designing the wrong model for your problem. Model issues can include using too complex or too simple models, using inappropriate architectures, parameters, or algorithms, or using outdated or unsupported frameworks or libraries. These issues can lead to high computational costs, low efficiency, low accuracy, or compatibility problems. To avoid model issues, you should always research and compare different models that suit your problem, use best practices and standards for model design and implementation, and update and maintain your frameworks and libraries.

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Raja Das

Technical Program Management | Azure, ITIL, AI, Digital Transformation
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Here are some additional reasons why model issues might occur: 1. Lack of clarity on what features should be considered while applying machine learning. 2. Lack of complete, accessible, clean and structured data could make it difficult to be used for ML. Theses can be avoided by doing more research prior to the development of the model.

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3 Evaluation issues

A third common pitfall of AI model development is failing to evaluate your models properly and objectively. Evaluation issues can include using inadequate metrics, baselines, or benchmarks, using inappropriate evaluation methods or techniques, or ignoring the ethical and social implications of your models. These issues can lead to misleading or incomplete assessment, overconfidence, or harmful outcomes. To prevent evaluation issues, you should always use relevant and reliable metrics, baselines, and benchmarks that reflect your problem and goals, use rigorous and robust evaluation methods and techniques that account for uncertainty and variability, and consider the ethical and social impact of your models.

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Raja Das

Technical Program Management | Azure, ITIL, AI, Digital Transformation
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Creating an ethics board prior to the development of AI model is important. We need to discuss all the possible outcomes and the board should ensure all the outcomes are following high ethical standards and are compliant with the moral values.

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4 Deployment issues

A fourth common pitfall of AI model development is encountering difficulties or errors when deploying your models to production or real-world scenarios. Deployment issues can include scalability, security, performance, or compatibility problems, or unexpected or undesirable behaviors or results. These issues can lead to poor user experience, customer dissatisfaction, or legal or reputational risks. To avoid deployment issues, you should always test and debug your models thoroughly before deployment, use appropriate tools and platforms for deployment, monitor and update your models regularly, and collect and analyze feedback and data from your users.

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5 Communication issues

A fifth common pitfall of AI model development is failing to communicate your models effectively and clearly to your stakeholders, clients, or users. Communication issues can include using jargon, technical terms, or acronyms that are not understood by your audience, using vague, ambiguous, or misleading language or visuals, or omitting or hiding important information or limitations of your models. These issues can lead to confusion, misunderstanding, or mistrust among your audience. To prevent communication issues, you should always use simple, clear, and concise language and visuals that suit your audience, use examples, analogies, or stories to illustrate your models, and be transparent, honest, and accountable for your models.

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Raja Das

Technical Program Management | Azure, ITIL, AI, Digital Transformation
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Communication issues can be prevented by understanding the background of your stakeholders. While you can use technical jargons to communicate with your team or other IT teams, it is always a good practice to avoid using technical jargons and making use of simple and clear language to communicate with clients and users just to avoid any confusions.

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Raja Das

Technical Program Management | Azure, ITIL, AI, Digital Transformation
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Communication issues can be prevented by understanding the background of your stakeholders. While you can use technical jargons to communicate with your team or other IT teams, it is always a good practice to avoid using technical jargons and making use of simple and clear language to communicate with clients and users just to avoid any confusions.

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6 Learning issues

A sixth common pitfall of AI model development is stopping or slowing down your learning and improvement process. Learning issues can include being satisfied with your current models, ignoring new developments or innovations in the field, or avoiding feedback or criticism of your models. These issues can lead to stagnation, obsolescence, or complacency of your models. To avoid learning issues, you should always seek to improve your models, explore new ideas or approaches, or learn from other experts or sources, and welcome feedback or criticism of your models as opportunities to grow and learn.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are common AI model development pitfalls?

1

2

3

4

5

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1 Data issues

2 Model issues

3 Evaluation issues

4 Deployment issues

5 Communication issues

6 Learning issues

7 Here’s what else to consider

Computer Science

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What are common AI model development pitfalls?

1

2

3

4

5

6

7

1 Data issues

2 Model issues

3 Evaluation issues

4 Deployment issues

5 Communication issues

6 Learning issues

7 Here’s what else to consider

Computer Science

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