But what exactly do we mean by the machine learning (ML) lifecycle? What end-to-end process do we have in mind?<\/p>\n
At a high level, the lifecycle, shown in the figure directly below, comprises three main components: model research and development (R&D), the retraining cycle and management and infrastructure. The first is key for taking your model from an abstract idea to a tangible, final product, the second for maintaining the final product, and the third for enabling the former two as effectively and efficiently as possible.<\/strong> While the outlined steps in the above diagram may seem straightforward, in a complex arena like data science<\/a>, the devil is in the details. In this article, we will unpack the steps underlying the first component \u2013 model research and development \u2013 where cutting corners in the name of getting your minimum viable product out there can put you at the particular risk of jeopardizing the value proposition of your initiative entirely.<\/p>\n The below figure provides a \u201czoom in\u201d of the \u201cModel Development\u201d component of the ML lifecycle, shown previously above.<\/p>\n You can conceptually think of model research and development (R&D) in linear terms \u2013 sequential in nature with iterations between select steps. We are going to assume, for the purposes of this discussion, that you have already decided on an ML model to work on, presumably via an intake process by your team. Suffice it to say, though, that intake and model ideation are processes that warrant additional discussion as they relate to the management of your ML lifecycle.<\/strong><\/p>\n In other words, in the context of this blog, your organization has a mature demand intake process for resolving business and functional challenges by leveraging artificial intelligence and machine learning <\/a>(AIML) solutions.<\/p>\n Model R&D comprises the following steps.<\/p>\n First, you need to determine what the model is specifically going to do. This might seem straightforward once you\u2019ve identified the use case, but it can get complicated quickly. It\u2019s one thing to have a model for solving a business problem, but how exactly are you going to apply it? Oftentimes, there are different ways to think about and quantify an outcome. This is where it is important to work closely with your end user to understand not only what they say they want to be done but to distill from that what they really are trying to accomplish.<\/strong><\/p>\n That means understanding how they intend to use the model in practice. For example, are you trying to predict product demand, forecast weather impact on yield, or do you want to gauge the impact of a global conflict on your supply chain and associated prices? This is especially crucial in applications where current KPIs are not telling the full story. What on the surface might sound like a use case for a linear regression forecasting problem may, upon further refinement, amount to a classification problem. In addition, this may be a good time to think about how you will ultimately deliver your model to the end users for consumption.<\/p>\n Once you define your model target, you need to take an inventory of what data you may want to use to develop your model. This means working with the business and your IT partners to identify what source datasets they already have readily on hand and what additional data you could bring in, either from public or proprietary sources.<\/p>\n![\"MLOps](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2022\/04\/MLOps-Image.png\")
<\/a><\/p>\nResearching and Developing your Machine Learning Model<\/h2>\n
![\"Machine](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2022\/08\/MLOps-ladder-1024x911.png\")
<\/a><\/p>\nModel Target<\/h3>\n
R&D Data<\/h3>\n