Every machine learning solution requires a dataset that encapsulates the business problem to be solved.

A machine learning system will ingest this dataset, learn its complex patterns/relationships, and output a set of business predictions that help solve a specific business problem.

This sounds great, but how do you acquire this dataset?

How do you build products that leverage machine learning?

Machine learning is using data to answer valuable business questions.

Answering these business questions should lead to the creation of tangible business value. This could be increased revenue, decreased costs, increased retention rate, increased operational efficiency, etc.

Therefore, always focus on the business impact of artificial intelligence when building AI/ML products.

What is a feature in machine learning?

A feature is a measurable property or characteristic of an event you want to predict.

But, what happens if you have missing or unknown features for the event you want to predict? What if these features are crucially important to make accurate predictions?

Let’s look at a concrete example:

Suppose your goal is to predict whether a pipe will break/collapse due to erosion.

You may have heard the term “bias” in artificial intelligence. It usually refers to machine learning algorithms that make biased predictions.

Biased predictions are a sign of underperforming machine learning models that were not trained with the proper datasets.

Most people know that the performance of a machine learning model is directly proportional to the quantity and quality of the dataset used to train it.

“Should we build everything from scratch, use third-party machine learning services, or a combination of both?”

When you identify AI/ML use cases for your company (or your company’s clients) that will lead to business value and ROI, you need to know the various implementation options.

Every company is different: Each has its own business objectives, customers, AI adoption roadmap, data strategy, AI monetization plan, timeline, and resources.

It’s important that you and your team choose the right tools for each use case. There is no absolute right or wrong answer, and you must be open to change if the initial tool selected turned out to be inappropriate for a given use case.

Everyone involved in the early stages of enterprise AI adoption wants to get ROI from AI/ML projects as fast as possible.

We all know artificial intelligence is the future, and organizations are rushing in to capitalize on this world-changing technology before AI disruption makes them irrelevant in the marketplace.

In a previous blog post, I covered how to get ROI from AI/ML projects. However, there are things that your organization needs to have in place before any ROI is realized.

What are three key factors to keep in mind when you are making 7-8 figure investment decisions for AI adoption and managing enterprise AI/ML projects?

It’s not the machine learning models, ETL processes, containerization, or scalable deployments in production.

It’s about the impact of AI adoption on the organization.

Here are 3 key factors to consider when managing AI/ML projects:

“I want to advance my career into the field of artificial intelligence, but I want to focus on strategy and leadership instead of coding.”

I hear this every day from people in multiple countries around the world.

The more I talk to product managers and other non-technical IT professionals, the more I realize how many people want to get into the field of AI.

Do most people want to become data scientists or machine learning engineers?