Define the business problem you are trying to solve.
Once you have answers, then identify how you can solve the problem using AI.
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AI model development involves multiple stages that interconnect to each other.
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:
Ask questions, such as.
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...
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 ...
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.
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:
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The Function Accountability Chart can be used to measure success and define who is responsible for what. Find out about your company’s functions. Then, let each of your executive team members fill in who is responsible for each function and what KPIs can be used ...
Soon after we are introduced to the world of the story, we realize that there is trouble in paradise. At first, you might not be able to pinpoint the problem, you just have a general sense that something is off.
What this means for you: There can be no adventure if you don’t have somethi...
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