Jul 26

Ensuring ROI on your machine learning project

Through all of our conversations with customers of all sizes and backgrounds, the number one concern that crops up time and time again relates to ensuring adequate return on investment (ROI) from machine learning (ML) projects. The ROI of your project is frequently translated to the overall positive outcome, and is the number one success metric cited by Project Managers and C-level Executives alike. In an industry where increasing your budget just won’t cut it, here we outline our top 5 tips for ensuring excellent ROI on your ML projects.

cash money and acceleration diagram with accelerating arrow ROI in AI machine learning network

Competitive timeline

The age-old adage rings true: time is money. Your project timeline should be realistic and sustainable but also aggressive. Factors to enhance success here include planning ahead while ensuring flexibility, which is why we recommend implementing an agile iterative approach as priority.

Accurate data

The accuracy of the data used to train your algorithms has a direct impact on the ROI when you consider the difference in efficiency to get the best results from your algorithm. The higher the accuracy in your input data, the more smoothly your algorithm will function. Accurate data ensures less bias and less drift over time. It can be worth the investment to set up regular quality assurance at this stage in your project.

Solid data processing operations

As mentioned above, high levels of accuracy are key to ensuring a solid ROI, but the operations you put in place around the processing of the data are also vital. On average 80% of the project time is spent on processing the data – use this time wisely and consider using some of it to research faster or more accurate methodology that will enhance your operations in the long-run.

Ensuring availability of reliable data

The type of data required varies depending on the project, and some types prove relatively complicated to source. Others require specialist knowledge during the processing stages. For Natural Language Processing (NLP) projects it can be difficult and time-consuming to source and process data in low-resource languages. Low availability of reliable data puts extra time pressure on the team and can use a surprisingly high investment of resources in order to yield appropriate quantity and quality.

dollar money investment network ROI diagram

Planning for success – introducing the StageZero Project ROI Calculator

As part of our continued commitment to your success, we’re offering our expert’s guidance free of charge to assist you to improve on the ROI of your project: introducing the StageZero Project ROI Calculator.

Follow this link to answer a few short questions, and based on your answers we’ll email you our tips and tricks on how to augment your specific project’s ROI. Our tips to you are based on benchmarking tests we’ve carried out in the industry, current industry metrics, and our learning’s and expertise as some of Finland’s most qualified NLP experts. Try it yourself here.

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