Steps to a successful AI implementation
Tags: Artificial Intelligence, Intelligence, Machine Learning, Technology, Revolution

Implementation Of Machine Learning

Organizations across every sector will continue to embrace ML and AI technology over the upcoming years, transforming their business models and core business processes to take benefit of machine learning systems for greater cost efficiencies and enhanced operations. It is necessary for the business leaders to remember that the road to ML (Machine Learning) and AI adoption is a journey, rather than a race, as the leaders draw up strategies and plan to make the best use of technology. Organizations should consider the following steps.

1. Define a use case

It is necessary for the project managers and business leaders to start by investing time in articulating and clearly defining the challenges or specific problems they would like AI to solve; the more particular the goal is, the more chance of success for their implementation of AI.

For example, stating that the enterprises would like to ‘rise online sales by 10%’, is not much specific. Instead, a statement such as ‘aiming to raise online sales by 10% by observing the demographics of site visitors’ is much useful in articulating the goal and ensuring that it is properly understood by the stakeholders.

2. Verify the availability of information

The second step is to make sure that the systems and processes are capable of tracking and capturing the information required to perform the needed analysis.

An amount of effort and time is invested in data wrangling and ingestion; thus, organizations should make sure that the right data is being captured with the right variables or features in sufficient volumes. The features that should be kept in mind are gender, age, ethnicity, but the most important is the quality of the information for a successful outcome.

3. Carry the basic information exploration

It might be tempting for an organization to leap into a model building exercise, but it is very important that it first handles a data exploration exercise in which it can verify its data understanding and assumptions.

This exercise will help the organizations to understand what the features could be, and the type of data categorizations that shall be generated for use as input for any models.

4. Define model-building methodology

It is necessary to concentrate on the hypothesis itself rather than focusing on the end goal the hypothesis might achieve. For determining the most significant variables or features tests should be run that could improve the execution or validate the hypothesis.

Continuous feedback of business and domain experts must be involved as the feedback is important for validation and for ensuring the stakeholders to be on the same page. A subject expert will always be useful for deriving better features for the success of any ML model.

5. Define model-validation methodology

The performance measures will assist the comparison, evaluation, and analysis of results from several algorithms which will help in refining specific models. Let us take an example, in classification accuracy, a superior performance measure will be when the number of correct predictions made is divided by the total number of predictions made and then multiplied by 100.

6. Production and Automation rollout

The model must be rolled into production, as soon as it is built and validated. Starting with a limited rollout of a few months, upon which the business users can give continuous feedback on the outcome and behavior, it can further be rolled out to the wider audience.

Data ingestion should be automated with the correct tools and platforms, to spread results to the audiences. The platform must give various interfaces to account for several degrees of knowledge among the businesses’ end-users.

7. Continue updating the model

The model must be continuously monitored, as it once gets published and deployed for use. Models can be out of date for many reasons. To predict future outcomes, the models are built on historical data, and thus, the performance of the model can be deteriorated. It is necessary to ensure that those processes must be followed which keeps the model up to date.

Enterprise Artificial Intelligence is quickly moving beyond hype and is fixed to have an impact on various business operations and efficiencies. Taking time in planning its implementation will put the businesses in a stronger position to enjoy its benefits further.