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4 Ways to Overcome Deep Learning Challenges in 2024

Deep learning presents great opportunities for businesses with powerful applications such as image classification, anomaly detection, and voice recognition. However, only 16% of companies have taken deep learning projects beyond the pilot stage. Applying deep learning models to business use cases poses some challenges, and it is essential to be aware of them to avoid spending resources on failed projects. We explore 4 significant challenges of deep learning applications and how you can overcome them:

Ensure you have enough and relevant training data

Developing successful deep learning models requires large volumes of data. Depending on the project, the cost and time requirements of collecting and labeling such data can be prohibitive. Moreover, having a large dataset is not sufficient as the performance of deep learning models depends heavily on the quality of training data. Biases, incorrect labels, and omitted values are some issues that affect the quality of datasets used in deep learning training.

Make sure that you have enough data to implement a deep learning application. If you don’t have enough data, you can benefit from data augmentation techniques such as synthetic data generation to increase data size.

If you have the data, you need to validate it to ensure that it is ready for a deep learning model. Implementing data labelingcleaning, and debiasing best practices is important to attain a good quality dataset.

Optimize computing costs depending on the number and size of your DL models

Developing deep learning models requires strong GPUs and is an iterative process. State-of-the-art deep learning models can have up to billions of parameters and more data also means a need for more computational power. According to OpenAI, the computational power used to train popular AI models has doubled every ~3.5 months since 2012. Moreover, the process involves lots of trial and error such as trying different training datasets, models, or model hyperparameters to reach optimal results.

Deploy smaller models and use a cloud service provider if you are getting started with deep learning in your business. However, if the number and size of your deep learning projects are going to increase, on-premise infrastructure with specialized hardware such as high-performance GPUs and large data storage devices can be a more cost-saving option in the long run. Figure 1 provides a cost comparison of several cloud services vs. building on-premise deep learning infrastructures.

Figure 1. Cost comparison of cloud services and on-premise DL infrastructure

Source: Determined AI

Give traditional interpretable models priority over DL

Deep learning models tend to be black-box models. It is hard for developers and users to explain why these models come up with specific results. This can pose a problem for businesses that are operating in regulated industries, such as banking or insurance, that need to explain their actions to auditors.

Establish a clear business objective before embarking on a deep learning project. Not every business problem needs deep learning. If it is not strictly necessary, use more explainable techniques such as rule-based approaches or linear regression. If there is a need for more sophisticated methods, there are explainable AI methods, such as tree-based algorithms, that could fit to your business use case.

Use privacy-protecting data security techniques

Customer privacy and data security are important challenges of collecting large volumes of data for a deep learning model. Most business applications require access to sensitive customer data. This raises privacy concerns, and some regulations limit businesses from collecting and storing such data. This can also limit the use of cloud services.

Use privacy-protecting techniques such as cryptographic algorithms, data maskingsynthetic data, or differential privacy. These methods can both protect your raw data and prevent attackers from obtaining information about the data from the results of your deep learning algorithms. Feel free to check our articles on privacy-enhancing technologies and data security best practices for a more comprehensive account.

You can also check our sortable/filterable list of deep learning software providers.

If you have other questions regarding deep learning and its enterprise applications, we can help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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