> For the complete documentation index, see [llms.txt](https://playbooks.equalexperts.com/mlops-playbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/not-taking-into-consideration-the-downstream-application-of-the-model.md).

# Not taking into consideration the downstream application of the model

### <mark style="color:blue;">You will only get value from your investment in machine learning when it has been integrated into your business systems. Whilst it can be technically straightforward to provide an output, integrating into the business processes can take some time due to usability needs and security constraints.</mark>&#x20;

Technical incompatibility or unrealistic accuracy expectations, if not addressed at the beginning of the project, can lead to delays, disappointment and other negative outcomes. For example, it is common to apply ML to tasks like ‘propensity to buy’ - finding people who may be interested in purchasing your product. If you did not take this downstream application into account from early on in the development, you might well provide the output in a form which is not usable such as an API endpoint, when a simple file containing a list or table supplied to an outbound call centre is all that is needed. Taking our recommendation to [Create a Walking Skeleton/ Steel Thread](/mlops-playbook/practices/create-a-walking-skeleton-steel-thread.md) is a great way to avoid this.


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