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AI/ML Product Success Metrics

When you are building an AI/ML product, it’s paramount that you define clear success metrics from the beginning.

These metrics will help guide the AI product development lifecycle and ensure that your team converges on the right product that solves business problems/user needs.

There are two ways to assess AI/ML product success:

1) Business outcomes

Business outcomes are the most important success metrics for AI products (and AI adoption in general). These are business objectives that result in tangible value created and captured by AI/ML products.

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The Most Important Element Of AI Adoption

What is the most important element that will determine the success or failure of an AI/ML project?

Most people, including technical professionals in the field, would think it’s the datasets: Quality, quantity, and a data engineering pipeline to produce them. This is because machine learning algorithms perform only as good as the data used to train them.

However, business leaders are quickly realizing that the most important element of AI adoption is actually defining the business problem(s) correctly.

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How To Scope Out A Dataset From Scratch (Enterprise ML)

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?

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AI/ML Product Management Fundamentals

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.

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How To Identify Unknown Features In Machine Learning

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.

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Bias In Artificial Intelligence

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.

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The 3 Layers Of The AI Stack

“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.

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3 Things You Need Before Getting ROI From AI/ML Projects

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.

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3 Key Factors When Managing AI/ML Projects

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:

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How To Break Into AI As A Non-Technical IT Professional

“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?

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