Delivering on AI and data objectives is not an easy endeavor, and many companies stumble at one of the first (and trickiest) pitfalls: knowing what — and who — to look for when planning for and staffing initiatives.
In order to implement and scale successful AI projects, enterprises need to adopt a comprehensive approach to covering each step of the data science and machine learning lifecycle — starting from project scoping and data prep, and going through all the stages of model building, deployment, management, analytics, to full-blown Enterprise AI.
Generally, every AI or data project lifecycle encompasses three main stages: project scoping, design or build phase, and deployment in production. Let's go over each of them and the key steps and factors to consider when implementing them.
.jpg?width=933&name=ifographic-The%20Data%20Science%20Lifecycle-01%20(1).jpg)
1. AI Project Scoping
The first fundamental step when starting an AI initiative is scoping and selecting the relevant use case(s) that the AI model will be built to address. In this phase, it's crucial to precisely define the strategic business objectives and desired outcomes of the project, select align all the different stakeholders' expectations, anticipate the key resources and steps, and define the success metrics. Selecting the AI or machine learning use cases and being able to evaluate the return on investment (ROI) is critical to the success of any data project.
2. Building the Model
Once the relevant projects have been selected and properly scoped, the next step of the machine learning lifecycle is the Design or Build phase, which can take from a few days to multiple months, depending on the nature of the project. The Design phase is essentially an iterative process comprising all the steps relevant to building the AI or machine learning model: data acquisition, exploration, preparation, cleaning, feature engineering, testing and running a set of models to try to predict behaviors or discover insights in the data.
Enabling all the different people involved in the AI project to have the appropriate access to data, tools, and processes in order to collaborate across different stages of the model building is critical to its success. Another key success factor to consider is model validation: how will you determine, measure, and evaluate the performance of each iteration with regards to the defined ROI objective?
3. Deploying to Production
In order to realize real business value from data projects, machine learning models must not sit on the shelf; they need to be operationalized, or deployed into production for use across the organization. Once again, ROI is a key consideration in this step: it's important to recognize that not all AI projects can or should be operationalized. Sometimes the cost of deploying a model into production is higher than the value it would bring. Ideally, this should be anticipated in the project scoping phase, before the model is actually built, but this is not always possible.
Another crucial factor to consider in the deployment phase of the machine learning lifecycle is the replicability of a project: think about how this project can be reused and capitalized on by other teams, departments, regions, etc., than the ones it's initially built to serve.
But Wait...There's More!
While traditionally scoping, building, and operationalizing AI projects are considered as the three main AI lifecycle stages, the work doesn't end with deploying the model. In order to achieve true Enterprise AI, it’s critical to have systems for monitoring models once they're in production and to be able to quickly introduce, test, train, and implement new models in order to shift strategies or adapt to changing environments on a dime — the data science and machine learning lifecycle stage most commonly referred to as MLOps.
Finally, in order to implement a successful AI strategy, you need individuals with different skillsets to support each step of the AI lifecycle. Therefore, you need to be able to dissect each part of the AI models' lifecycle, translate it into concrete organizational resources and needs, and then map those needs to the different data profiles available. Learn more about assessing the machine learning lifecycle stages and staffing for AI projects in this guidebook.