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 functionalities in data preprocessing, feature engineering, model development, and model evaluation, which can be executed with minimal human intervention. AutoML gives companies the opportunity to integrate AI solutions into business processes with ease, 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. In this article, we discuss six benefits of having complete access to 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 model code 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, so most companies may feel the need to customise models to be sensitive to ever-changing business demands. Having complete access to model code makes customisation feasible and painless. With access to model code, users are also able to retrain the model in case there are significant changes to input data.
- 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 customise and integrate these models easily 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 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 would need to continuously evolve the technology to stay relevant in the industry. They may even feel the 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 complete 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.
On the one hand, having full access to production-ready model code reduces the need to depend on the input of individual developers in deployment. On the other hand, with access to model code, companies can make independent decisions about their models in the case of delays caused by external stakeholders involved in the autoML process. - Safeguarding intellectual property: AI is evolving at a remarkable speed. Legal and regulatory processes need to 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.
Build optimal ML models and own the code
evoML, the ML code generation and optimisation platform developed by TurinTech, speeds up the data science lifecycle by generating production-quality ML code in a fraction of the time it takes to go through a generic ML process. Inspired by the Darwinian theory of evolution, evoML uses evolutionary algorithms, meta-learning, and search-based software engineering to automatically find and tune the most suitable version of model code for less memory and energy usage, lower latency, and higher throughput. Users are also provided performance metrics and visualisations to make more informed decisions. A key feature of evoML is that the platform provides complete ownership of model code to users. With evoML, data scientists and engineers are able to improve their machine learning model building process, while also enjoying the benefits of code ownership.
About the Author
Malithi Alahapperuma | TurinTech Technical Writer
Researcher, writer and teacher. Curious about the things that happen at the intersection of technology and the humanities. Enjoys reading, cooking, and exploring new cities.