Deep Learning Software

Business and management teams need to align on the level of granularity they will discuss to understand model output. This level can be individual results, result patterns that cover 10 cases, 100 cases or 1000 cases. Aligning this at the beginning of the exercise minimizes unncessary discussions between the teams. This is important because deep learning's lack of an easy mechanism for explainability can lead to long discussions between technical and business teams as they try to understand why specific errors take place.

As in any PoC, it is helpful to have a list of goals/assessment areas with quantifiable values. This enables different PoCs to be be compared and PoC to be useful during vendor selection.

In case of deep learning software, PoC should be mainly focused on assessing usability and model accuracy. The best way to assess usability is to have the team that will use the product to build a project with it. After the project, they should assess the software using the list they prepared in advance which should allow for objective assessment of different software.

To assess model accuracy, the team assessing the product needs to identify business value of different model outputs. Read our comprehensive guide on machine learning accuracy to learn more and to be able to assign a numeric value to different machine learning models.

As with any software implementations, benefits should be measured and ROI should be cross-checked against targets so teams understand if their initial estimates were accurate. This helps teams understand and improve the accuracy of their estimates.

There are 3 criteria specific to choosing a deep learning solution: Support for deep learning techniques, flexibility and model accuracy in areas where your company needs deep learning models.

• Support for deep learning architectures: There are many deep learning architectures explored in research with varying levels of accuracy in case of different datasets. Deep learning software needs to support these deep learning structures so your company can implement them with ease
• Flexibility: Deep learning is still an ongoing area of research. Software should be flexible enough so users can implement new algorithms suggested in recent research.
• Model accuracy: Deep learning software should enable your company to generate models that create value for the business. Measuring business value of machine learning models is explored in detail in our article on the topic.

On top of these, typical tech procurement best practices should be followed to ensure that an economical and effective solution is chosen.

A machine learning model can replace a deep learning model for predictions. After all, deep learning is a subset of machine learning with its pros (i.e. better predictions) and cons (i.e. data hungry, lacks explainability). Finally, manual processes can replace models for predictions though this is unlikely to be economical.

When data is limited or when explanation needs to be provided for predictions, other machine learning techniques can be more successful. Especially explanability is a concern for businesses. Since deep learning architecture results in a complex net of artificial neurons, the reasons for its predictions are not obvious. Other machine learning techniques such as decision trees may not produce results that are as accurate as deep learning but are easy to explain as they show the exact factors that lead to each prediction

Finally, manual solutions are always usable as an alternative however, given the high cost of manually analyzing data and making predictions, this is unlikely to be an economical alternative

Built-in support for building latest deep learning structures/architectures is important to ensure that technical personnel spend minimum time implementing solutions based on such architectures.

Data ingestion and processing speed are important features of deep learning software and depend on the hardware the system is running on. Ideally different software should be run on the company's hardware/software stack to see if there are significant performance differences.

Visualization support both for visualizing error rates and the network itself, help developers while building models. While not all solutions support visualizations, some leading ones such as TensorFlow offer such functionality

Once a model is chosen, the aim should be to deploying the model to production systems and make it a part of the decision making process. In deployment, engineers have to ensure that model runs fast enough.

Integrations to databases such as Apache Hadoop and Spark are useful for deep learning software as deep learning models rely on data and integrations make it easier to pull data from company's systems.

Industries with the most data are likely to benefit the most from deep learning models. Some example industries are:

• Most B2C industries such as banking, utilities etc. have time series data on customers useful for deep learning models
• In B2B, models are helpful in prioritizing leads
• Manufacturing, especially companies leveraging Industrial Internet of Things (IIoT) have access to copious amounts of data which can power deep learning models