Six Benefits of Owning Your Machine Learning Code - Image by Markus Winkler from Pixabay Automated machine learning (autoML) has significantly impacted AI work practices across industries — simplifying, streamlining, and accelerating data science processes. A typical high-performing autoML tool offers data pre-processing, feature engineering, model development, and model evaluation functionalities, which can be executed with minimal human intervention. AutoML allows companies to easily integrate AI solutions into business processes, leading to greater efficiency and, ultimately, higher profitability.

However, automated machine learning inevitably has its own disadvantages. For instance, machine learning models built with automated tools can have lower precision in predictions in comparison to purpose-built solutions. Further, an often-unnoticed downside of autoML is the lack of ownership to complete model code and the complexities arising from it. This article discusses six benefits of your machine learning model code.

Improving transparency and explainability

Often machine learning models are opaque, where users have minimal knowledge of the decision-making process taking place within the model. This can be particularly challenging in industries that require strict regulatory compliance, such as healthcare and finance. Owning your model code gives you full access to it, which significantly improves transparency. Putting businesses in a better position to meet regulatory compliance.

Customisability and flexibility

Off-the-shelf solutions are often developed to meet a set of generic criteria. Most companies may feel the need to customise models to be sensitive to ever-changing business demands. Having complete ownership of the model code makes customisation feasible and painless. Users can also retrain the model if there are significant input data changes or new data emerges.

Integrating into available architecture and hardware

Organisations have varying system architectures with unique performance needs and hardware restrictions. Having end-to-end code allows engineering teams to easily customise and integrate these models with the rest of their data pipelines. It also allows for easy integration of existing monitoring and evaluation tools.

Ready-made machine learning systems may also require businesses to upgrade hardware, which comes at a significant financial cost. Having ownership of the model code lets companies scale their solutions to suit existing hardware.

Specialisation and extension

A machine learning-based solution cannot be left to stagnate upon deployment. Companies must continuously evolve the technology to stay relevant in the industry. They may even need to develop specialised solutions from the initial models. For instance, a financial firm that creates a cutting-edge machine learning-based trading tool may choose to further develop this solution and offer it as a specialised service. Such scenarios will only be possible if in-house teams have ownership of the model code.

Reducing dependencies

Developing and maintaining a data science pipeline involves many stakeholders, both within an organisation and outside. In such settings, individual delays can hamper the progress of the entire pipeline. Having complete ownership of model code generated by an autoML process helps to reduce dependencies in the data science pipeline.

For example, if a business outsources an AI project to a consultancy firm but doesn’t own the model code, then all changes to the model code would have to go through the consultancy firm. This process will cause delays and may introduce more costs over time.

Having full ownership of production-ready model code reduces the need to depend on the input of individual developers in deployment. With the ownership of the model code, companies can make independent decisions about their models to avoid delays caused by being over-dependent on external stakeholders involved in the autoML process.

Safeguarding intellectual property

AI is evolving at a remarkable speed. Legal and regulatory processes must progress in equal measure for companies to confidently incorporate AI into their work. In 2021 the UK government conducted a consultation to seek “evidence and views on a range of options on how AI should be dealt with in the patent and copyright systems”. These measures emphasise the value of legal and regulatory protection in the AI sphere. Businesses, especially those in the finance and trading industries, have a stronger need to protect their processes and output. Owning model code makes it easier for businesses to navigate the AI intellectual property space.

The bottom line

As seen throughout this article, there are several benefits to owning your own model code. Owning your own code provides businesses with more agility and flexibility when operating with AI. From increased transparency to avoid compliance issues, to specialising the code to create a new business offering. Owning the model code will provide tangible benefits that will enable businesses to truly reap the benefits of AI.

TurinTech is the leader in code optimisation for machine learning and other data-heavy applications, helping businesses become more efficient and sustainable by accelerating time-to-production and reducing development and compute costs.

Powered by proprietary AI research, TurinTech’s evoML platform empowers businesses to automatically 1) build efficient ML model code from raw data 2) optimise the performance of existing ML model code and 3) optimise the speed of generic code.

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