In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production.

However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.

According to the 2020 State Of The ML Report by Algorithmia, AI model development has become much more efficient. It reported that almost 50% of the enterprises deployed an ML model between 8 to 90 days.

This statistic shows the improvement in enterprise AI adoption. Yet, to completely harness the power of AI in your business, you need to build and deploy multiple models.

In this article, we will be discussing the steps in AI model development. We will also shed light on AI model development challenges and discuss how you can accelerate your enterprise AI adoption.

AI model development involves multiple stages interconnected to each other. The block diagram below will help you understand every step.

We will now break down each block in detail.

Step 1: Identification Of The Business Problem

Andrew Ng, the founder of deeplearning.ai always prefers seeing AI applications as a business problem. Instead of asking how to improve your artificial intelligence, he suggests asking how to improve your business.

So, in the first step of your model development, define the business problem you are looking to solve. At this stage, you need to ask the following questions.

  • What results are you expecting from the process?
  • What processes are in use to solve this problem?
  • How do you see AI improving the current process?
  • What are the KPIs that will help you track progress?
  • What resources will be required?
  • How do you break down the problem into iterative sprints?

Once you have answers to the above questions, you can then identify how you can solve the problem using AI. Generally, your business problem might fall in one of the below categories.

  1. Classification: As the name suggests, classification helps you to categorize something into type A or type B. You can use this to classify more than two types as well(called multi-class classification).
  2. Regression: Regression helps you to predict a definite number for a defined parameter. For example, predicting the number of COVID-19 cases in a particular period in the future, predicting the demand…

Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1067347

Source: www.datasciencecentral.com