What are the biggest challenges in training machine learning models for R&D?

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Machine learning (ML) is a powerful tool for research and development (R&D) that can help discover new insights, optimize processes, and generate novel solutions. However, training ML models for R&D is not a trivial task. It requires overcoming several challenges that can affect the quality, efficiency, and reliability of the results. In this article, you will learn about some of the biggest challenges in training ML models for R&D and how to address them.

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Use external data sources:

Leverage existing or external datasets to supplement your own. This can save time and resources, ensuring your ML models have enough data to learn effectively.### *Balance complexity and efficiency:Regularly evaluate your model's complexity against its performance metrics. Using techniques like regularization can help maintain accuracy without overburdening computational resources.

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Vu Dang

CDO is just OCD sorted in the correct…
Jose A Garcia-Regueiro

Retired at IRTA

1 Data availability

One of the most common challenges in training ML models for R&D is the lack of sufficient and relevant data. Data is the fuel for ML, and without it, the models cannot learn effectively or generalize well. R&D often involves exploring new domains or problems that may not have existing or accessible data sources. Moreover, data collection and labeling can be costly, time-consuming, and prone to errors or biases. To overcome this challenge, you need to design your data strategy carefully, leverage existing or external data sources when possible, use data augmentation or synthesis techniques, and apply quality control and validation methods.

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Jose A Garcia-Regueiro

Retired at IRTA
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To establish the best choice between induction and deduction on the basis for "universal" or local validation of the proposed conclusion and explanation of experimental model design and hypothesis.

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2 Model complexity

Another challenge in training ML models for R&D is the trade-off between model complexity and performance. Model complexity refers to the number of parameters, layers, or features that a model has, and it affects how well the model can capture the patterns and nuances of the data. However, increasing model complexity also increases the computational resources, training time, and risk of overfitting or underfitting. To overcome this challenge, you need to balance your model complexity with your data size and quality, use regularization or pruning techniques, and evaluate your model performance using appropriate metrics and benchmarks.

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Sagarnil Bose

Data Science Consultant at Daimler Truck Innovation Center India | Ex-MBRDI
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One of the most relevant principles from 14th century called the Occam's Razor is a principle that states if you have two competing ideas to explain the same phenomenon, you should prefer the simpler one and is applicable even without certain benchmarks which is likely to be the case for a R&D topic. Many other people before and after the friar, including Albert Einstein and Isaac Newton, have used similar principles for scientific conclusion. It is a very relevant principle that should be applied to complex problems in scientific research like the the trade-off between model complexity and performance in machine learning.

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3 Model interpretability

A third challenge in training ML models for R&D is the lack of model interpretability. Model interpretability refers to the ability to understand how and why a model makes certain predictions or decisions, and it is important for R&D because it can help validate, explain, and improve the model outcomes. However, many ML models, especially deep learning models, are often considered as black boxes that are difficult to interpret or explain. To overcome this challenge, you need to use interpretable or explainable ML techniques, such as feature importance, partial dependence plots, or counterfactual examples, and communicate your model results clearly and transparently.

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4 Model robustness

A fourth challenge in training ML models for R&D is the need for model robustness. Model robustness refers to the ability of a model to maintain its performance and accuracy under different or changing conditions, such as noise, outliers, or adversarial attacks. This is important for R&D because it can ensure the reliability and validity of the model results and applications. However, many ML models are sensitive or vulnerable to various sources of uncertainty or perturbation that can degrade or compromise their performance. To overcome this challenge, you need to use robust ML techniques, such as data cleaning, anomaly detection, or adversarial training, and test your model performance under different scenarios and assumptions.

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5 Model ethics

A fifth challenge in training ML models for R&D is the consideration of model ethics. Model ethics refers to the ethical implications and consequences of using ML models for R&D, such as fairness, privacy, accountability, or social impact. This is important for R&D because it can affect the trustworthiness and acceptability of the model results and applications, as well as the reputation and responsibility of the researchers and developers. However, many ML models can have unintended or undesirable effects or outcomes that can harm or discriminate against certain groups or individuals, or violate certain norms or values. To overcome this challenge, you need to use ethical ML techniques, such as fairness metrics, privacy-preserving methods, or human-in-the-loop approaches, and follow ethical principles and guidelines for your R&D projects.

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6 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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Vu Dang

CDO is just OCD sorted in the correct order ;)
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For industry R&D problems as opposed to individual ML applications, generalizability is hard: Firstly there are few public datasets for each problem to benchmark on. Secondly different instances of the same problem are hardly comparable because the ML pipelines (training, inference, labeling etc) depend heavily on the context of production. Thus unlike in academic research, superior performance on gold standard datasets is only a starting point and even well-developed solutions often take significant effort to tune up on a new problem instance.

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M.Jin Tan (陳眠槿)

Digital | Sustainability | Mindfulness | Compassion
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All of the factors mentioned are crucial to the ethical implementation of AI, not just for R&D use cases but in all AI use cases in general. There are more Ethical AI frameworks emerging that will deal with the intricacies of the numerous use cases. If there are frameworks governing human and animal testing, it is only intuitive that a governance framework be adopted for AI testing too.

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What are the biggest challenges in training machine learning models for R&D?

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1 Data availability

2 Model complexity

3 Model interpretability

4 Model robustness

5 Model ethics

6 Here’s what else to consider

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