3 Reasons why companies need to invest in machine learning engineering

The artificial intelligence (AI) market was valued at USD 59.67 Billion in 2021 and Mckinsey estimated that AI has the potential to create between $3.5 trillion and $5.8 trillion in value annually across multiple industries.

Wanting their share of this enormous growth, companies have invested and are investing ever more capital in AI, and AI solutions.

However, these capital and human efforts are struggling to live up to the potential growth estimated by Mckinsey. In a recent survey, 76% of executives say they are having trouble scaling the implementation of AI applications across multiple functional areas, with most executives saying that the success of AI applications depends on both data quality and deployment.?

Furthering the challenge of building AI applications, companies have over-invested in the data science domain at the expense of scalability, maintainability, and automation capabilities, i.e. machine learning engineering.

Here are 3 additional reasons why it is important that you invest in machine learning engineering:

Reason 1: Productionised machine learning (ML) models generate more revenue; Failures lead to a loss of revenue.

There are hundreds of use cases that can be solved using machine learning, statistics or optimisation models in data-driven enterprises.

Retailers, for example, can have a variety of models, including recommendation systems, logistic models, expenditure models, demand forecasting, etc. Telecommunication companies, on the other hand, require models to predict churn, develop tailored marketing campaigns, and determine how to spend the company's capital budget in the most effective manner.

Each model creates business value by solving a problem or taking advantage of an opportunity for the organisation.

If these opportunities or problems remain unresolved, companies lose incremental revenues or savings on a monthly basis. This represents a significant opportunity cost. Consider, for example, the case in which your organisation has a recommendation model that generates additional revenue of R 10 million each month. The organisation not only loses R 10 million every month for every month that such a model is not implemented, but also loses the opportunity to apply those funds to improving the model and rolling out new models.

In the case of Amazon, whose recommendation engine contributes to 35% of their revenue, a loss of 1% drop could result in millions of dollars in lost revenue.

By not investing in machine learning models your organisation is losing out on additional revenues and savings.??

Then by not implementing the models properly in production and building reliable data pipelines you risk the business value generated.? Once a valuable machine learning solution is identified, it is crucial that it reliably runs, delivering on the promise or benefit that the model represents, every month.? Delivering these valuable models to production while ensuring that they remain robust against disruptions and failures is the essence of machine learning engineering.?

Reason 2: Getting ML models to production is hard.

From scaling models to developing additional tooling for the machine learning process, there are a variety of problems that machine learning engineers must deal with on a daily basis. However, one of the main problems that machine learning engineers solve is the ability to transform a prototype or proof-of-concept into a service that other people and systems can use.??

Deploying a machine learning model into production is not an easy task, so much so that Venturebeat has reported that nearly 90% of data science solutions never come to fruition.??

Firstly, this is due to the complexity involved.? For example, a machine learning engineer not only focuses on delivering code, they must take into account the data, the code, the final model, the system where the model and model outputs need to be integrated and where and how the model is required to run.

Second, data is a physical entity that requires management and poses a variety of additional challenges.

As an example, system failures can result in the loss of data. Schema changes in the source system can cause the data pipeline to break. With time, the data distribution can change, degrading model performance. To ensure a model continues to perform well, all of these and more issues must be addressed and monitored when the model is deployed to production.

Thirdly, most models use multiple datasets. In a recent interview with Therese Nieuwenhuizen , a machine learning engineer at Spatialedge, she said it is possible to have as many as 30 data sources that are used in the feature store. That is a considerable number of dependencies to track.

Lastly, the models can be used for a variety of dashboards, decision applications, and integration with existing systems. This implies that their effectiveness over time is crucial to a number of business functions.

Machine learning engineers solve a majority of these problems.? From writing the code to get a model production-ready to developing capabilities that can speed up and support the productionisation process.

Reason 3: They allow you to scale your AI efforts.

Machine learning engineers enable organisations to build and maintain more machine learning models, more frequently.

From the source system to the final model, having metrics to monitor the entire pipeline is important.? Especially, when running models in production with both automated retraining and scoring. Proactive monitoring will enable your team to quickly identify the source of an issue and automate the alerting and rectification processes.? Without it, they can spend days, sometimes weeks tracking down and resolving an issue.

In fact, MLEs often automate a lot of the repetitive and mundane tasks throughout the whole analytics pipeline.? The result is that a relatively small team can maintain 100s of models in production.?

Additionally, they create templates, infrastructure, and tools to make data scientists' jobs easier.? Therefore, data scientists are not only free to focus on business problems, explore and develop multiple experiments, but they also benefit from the tools MLEs create.

Conclusion

As a big data company, machine learning engineers are critical to scaling efforts, deploying models to production, and reducing failure rates.? However, it is difficult to find great machine learning engineers.? It is difficult to build a team of MLE specialists who can solve technical problems and drive the process through the organisation to ensure delivery.

Working from first principles, Spatialedge has developed best practices, tools, and frameworks that have enabled our team of MLEs to deliver valuable results to our clients.

As an example, we partnered with a client's data science team and reduced the time it took to get a model from prototype to production from months to less than a week.? Correctly implementing MLOps pipelines has allowed us to manage a large number of models in production with minimal downtime.

If you are looking for a team that can accelerate your journey and solve your machine learning engineering problems, avoiding many pitfalls of doing it yourself, contact us today.

Thanks to Carl du Plessis and Byron Mitchell? for contributing and helping with editing this article.