What Does it Take To Build Enterprise-Grade AI Applications-A Brief Guide

Artificial Intelligence (AI) has often been touted as amongst the most revolutionary technologies of the current times. Self-driving cars, voice assistants and manufacturing robots have taken the world by storm. Having said that, building enterprise-grade AI applications is not easy by any means. More often than not, companies that aim to integrate AI into their existing systems fail to achieve the desired results. So, what does it take to build and deploy AI at scale? We will discuss this and a lot more in this blog.

What can Artificial Intelligence (AI) do for businesses?

A couple of years ago, McKinsey studied hundreds of use cases of AI across several industry verticals. Their findings revealed that the most valuable use cases for AI in a majority of businesses were the most mundane ones, and this is quite understandable. Most businesses need AI to improve efficiency, optimize costs, and boost ROI, and this is typically achieved by compounding incremental changes, instead of bringing about one-off revolutionary transformation.

Here are 3 major ways in which AI has been found to impact businesses:

1) Forecasting: AI-based forecasting models rely on historical data to predict future outcomes, be it stock prices or turnover for a time period.

2) Anomaly Detection: Detection of anomalous events or transactions helps businesses detect and prevent fraud.

3) Classification: Classification models help you decide which leads are the strongest or which customers are the most credit worthy.

All in all, AI helps businesses make informed decisions through reliable insights.

Data is the Key

One of the key factors that dictate the success of your AI initiative is the quantity, quality, and scope of the data available to you. AI algorithms are inherently data-hungry and require massive amounts of training data to deliver accurate results. But data volume alone isn’t enough. A thorough understanding of the data and preparing the data well are equally important. As a data scientist, you need to understand what the qualities of your data are and how it was acquired.

Once this has been done, it’s essential to determine if the data is suitable for AI. Here are a few things you need to look for in your data:

Characteristics: Does the model require supervised learning? If so, what is the nature of the target variable?

Gaps: Does the data have any gaps? Are these gaps significant or not? What is the source and nature of these gaps?

Patterns: Does the data appear to have any seasonality or time-based patterns? And how can these be exploited?

Correlations: Can any correlations be spotted in the data? If so, can a plausible explanation be given for these correlations?

External Factors: Can the data be affected by external factors?

A thorough evaluation of the potential of the data to address the intended problem can prevent the wastage of resources down the line.

Avoid Assumptions and Bias

All humans have preconceived notions and prejudices, and the same is likely to creep into the data collected for building an AI model. Needless to say, machine learning algorithms reflect the bias present in the data used to train them, and at times, amplify it too.

Even if the bias present in the data has been identified, the approach taken to address it may not help. For instance, if the data used to train the model has a gender bias, you may get biased results even after gender has been removed. This is because gender may affect other features in the data, and the model may infer gender from these features.

AI systems cannot be relied upon unless they are free of bias. It is, therefore, vital that data scientists proactively look for such traits by evaluating the incoming data as well as the final model outcomes.

Focus on Scalability Instead of Perfection

As in other life scenarios, chasing perfection in an AI model may not take you far. No AI model is 100% accurate and focusing on just one of its aspects can lead to several issues. First of all, it may slow down the progress of your AI program. Plus, it can affect the real-world performance of your model. What if you build a massive deep learning model that your production environment fails to support?

So, instead of trying to build a super-accurate AI model, aim for a model that’s good enough for the business problem at hand. And before deploying the model, ask yourself a few questions:

● Can the model run fast at scale?

● Can the model be deployed into your backend systems?

● How will the model outputs be accessed and utilized?

By adopting such an approach, you can possibly realize a better return on your investment and garner better support from higher management, which is essential for driving your AI program to completion.

In essence, an AI application should be treated just like any other critical software solution you need to build i.e. easy to scale and maintain.

Monitoring is Essential

Machine learning operations or MLOps is an approach that helps apply the principles behind DevOps to the field of machine learning. The objective behind adopting MLOps is to streamline the deployment of an AI application into production. In addition, MLOps also helps monitor and maintain the deployed solution. So, once your AI application has been deployed, you need to ensure it keeps performing as expected. There are a couple of other factors that require attention:

a) Data Integrity

Having accurate and reliable data is the key to building a high-performing AI application. Detection of poor data isn’t simple as your application may continue to deliver results inspite of the issues in your data. So, data scientists need to actively look out for issues such as too many missing values, type mismatch and range violations within the data.

b) System Health

AI applications are invariably data-heavy, which is why they become vulnerable to backend performance issues. Regular monitoring for deviations in CPU, disk, and memory usage trends is essential so that issues that keep your solution from becoming fully operational are detected and addressed well in time.

c) Model Drift

Even if your model is highly accurate at the outset, it may undergo performance degradation with time. This is because the model was trained with a given set of data collected under a given set of conditions that are likely to change with time. This is called model drift.

Mitigating a model drift requires you to identify the root cause of the drift. Specialized DevOps tools can assist with this. Once the root cause has been detected, you may work towards a solution for addressing the drift. This may involve retraining the data, labelling more data, or adjusting input features used in the model.

Treat AI as an end-to-end Problem

Many businesses tend to follow a piecemeal approach when dealing with AI. When they discover AI can help with a use case, they sign up for an AI platform. Then they try building a working AI model and employ data scientists only after they realize they need expert assistance. After a couple of months, they try to integrate the working model into production. After deployment, they leave the model to work on its own. And after a while, they realize that the model isn’t delivering business value any longer.

Long story short, building a successful AI solution entails an end-to-end approach. From the very beginning, you need to remember that the application you are building will be deployed at some point in time. So, it will need to get its data on a timely basis, merge seamlessly with your existing systems, run at scale, and deliver actionable insights to people who need them. At CellStrat, we take care of all pieces of AI solution development end-to-end and in-fact have shorter development life cycle against traditional style of development across the world thus saving clients huge costs, time and energy. We also manage and maintain the AI solutions for our clients for all times to come.

Make Sure Everyone is on Board

An essential yet often-overlooked requirement for a successful AI project is buy-in from the people in your organization. Despite having so many successful use cases and business applications, there is a wide mistrust of the technology. Many employees believe AI will take over their jobs, others fear that AI-based solutions won’t have as accurate outcomes as human-led systems. Companies should put in efforts to make their workforce understand that the aim behind deploying an AI application is to help them do their job better, not completely automate the jobs as all jobs cannot be automated. Employees should be taught about the use cases of AI and the risks of falling behind if they don’t catch the wave.

Then many people have unrealistic expectations with respect to how well AI can perform. Some tend to think AI can quadruple their productivity or halve their costs. In actuality, most companies experience a 10–20% change in such parameters when they implement AI. Of course, this small change can translate into a considerable increase in profitability.

The Final Word

Building and deploying a successful AI application is challenging. It requires you to treat AI just the way you treat any other software you try to build-an end-to-end problem. So, plan carefully, set realistic expectations, and keep making changes as required.

Need assistance with your existing or upcoming AI application? Connect with our experts at contact@cellstrat.com.