How Successfully Scale AI and Machine Learning for your Business

What to put in place to achieve tangible success using machine learning and artificial intelligence technologies.

AI can provide a host of benefits for the enterprises and organizations of today. From understanding customer behavior to fraud detection, visualizing analytical sentiments, and predicting machine failure. Machine learning, if implemented efficiently, promises to deliver game-changing value to businesses across many industries.

Your business may have scratched the surface with machine learning (ML) or AI in a pilot project or two. But have you managed to utilize these technologies to provide tangible benefits? If the answer is no, read on; most of your peers face similar setbacks.

Gartner predicts that over the next twelve months:

“80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.”

In the past, many organizations have failed as they rush to embark on AI and ML projects. Some set up innovation labs, employing ‘AI Experts’ to cast the spells mentioned by Gartner, only to realize that there’s no rabbit inside their hat. The problem here is that most businesses that dip their toes in the halcyon waters of AI and ML struggle to operationalize the models that they create into real-life business processes.

Only fully operational ML models will deliver any kind of ROI or business value. So, this begs the question, How can your business succeed at operationalizing your AI and ML models at scale? The four points below should be considered:

Ensure Stakeholders are Aligned

Many AI projects have failed due to a lack of mutual agreement between company stakeholders . Once the use case has been identified for your project, map out which stakeholders need to get involved. To calculate this, you’ll need to plan how the output of the ML model — classification, prediction, detection, recommendation, or segmentation — will be used and by whom.

It would be pointless to build an ML system that investigates consumer behavior patterns if the information gained is inaccessible, unusable, or not a planned part of the companies overarching business strategy. Plus, the marketing experts would need to be at hand to put the data to good use, which I’ll touch on in my next point.

Note that it’s essential to have a solid plan in place as to how the predictions will be delivered to and accessed by the downstream people/tools/processes.

Hire Staff With the Right Skillsets

Although it’s apparent that there is a shortage in data science talent on the job market, and hiring for this type of role can be challenging, AI and ML success requires much more than the skills of a data scientist. I’m talking about model building, data prep, training, and interference. If you’re serious about scaling and reaping the benefits that AI and ML have to offer, you should be looking to work with ML architects, data engineers, and operations managers. This piece goes into much more detail about how to structure your data science team.

The next challenge is to organize and scale your team effectively. Do you have staff trained with the necessary skills in-house to move this project from concept to completion? Do you build these skills through retraining and hiring? Or will you contract a team to help in completing this project in a pre-determined amount of time?

Building up your current team’s skillsets will help you to scale on a long-term basis. Whereas, third-party contractors will help to get your project off the ground with speed and efficiency.

Clearly Define Business Objectives

Let’s be clear here, most ML/AI projects fail to deliver because of grandiose expectations of what AI can achieve. Therefore, before you start an AI initiative, the goals of the project must be identified.

Begin with the business goals — what metrics would you like to improve? Are you trying to decrease fraud? Improve marketing practices? Save time on manual tasks? Learn more about your target market or existing customers? Monetize the data that you have already acquired from such customers?

From day one, it’s crucial that you identify the use case with absolute clarity, benchmark current performance, define measurable goals, and find realistic KPI’s that will determine success criteria.

Identify, Invest-in, and Use the Correct Technology and Tools

Over and over again, data science projects struggle because somebody has failed to plan out the technology required for success. Acquiring the correct tech and tools for developing and building models is one thing; however, the production deployment and operationalization aspects are often the hardest challenges for an AI or ML project.

You’ll need to consider:

Tools

There are hundreds of tools available across the ML, AI, and data science ecosystem . To decide which ones to implement often depends on the use case. Yes, Keras and Theano are fantastic, but they can’t solve all your problems. The machine learning space is in a state of continuous evolution, so your technology stack should support multiple frameworks.

Additionally, the architecture of your project must allow for cross-team collaboration between different professionals. This includes software engineers, ML architects , data engineers, and anyone else that will be involved in your project.

Data

The correct data is required for all use cases. For example, if you are creating and training a machine learning model to predict customer behavior, you’ll need substantial amounts of customer data, and AI systems that can employ algorithms to break down this data and turn it into actionable insight .

Infrastructure

Although public cloud services are advantageous, the cloud is not the safest place for huge-scale Ml/AI projects in large organizations. Recently, many enterprises use a hybrid cloud approach. This combines on-premises storage with cloud infrastructure depending on where the data that their project requires is located.

This approach allows businesses to leverage the tech and data that they have on their premises while utilizing the elasticity and agility that public cloud services offer.

How is your business currently implementing, scaling, and measuring ML or AI-based projects? What problems have you faced, and what measures have you take to overcome them?