In 2019, near 87% of data science projects did not get into production. However, due to COVID -19, most companies have scaled up their AI adoption and increased their AI investment.
In 2020, almost 50 % of enterprises employed an ML model. But to completely harness the power of AI, multiple models need to be created and deployed.
AI model development involves multiple stages that interconnect to each other.
Define the business problem you are trying to solve.
Once you have answers, then identify how you can solve the problem using AI.
Ask questions, such as.
Consider if your model will operate in real-time to determine if you need to create data pipelines to feed the model.
Consider what form of data is required:
This step is the most time-consuming, with ML engineers spending around 80% of the AI model development time in this stage. A significant amount of time is spent cleaning the data and transforming it into the required format.
Things to consider include:
At this step, all the requirements have been collected for the solution modelling to proceed.
ML engineers will define the features of the model, taking the following into account:
Once the features are defined, choose the most suitable algorithm.
While the model is trained and tuned using the training and validation data set, the model will behave differently when used in the real world, which is fine.
The main objective is to minimise the change in model behaviour when it is deployed. Three data sets are used when experiments are carried out: training, validation, and testing.
Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data.
When all the defined parameters are met, deploy the model into the intended infrastructure.
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