Postingpad - Abhay's portfolio and blogs

We have discussed a few aspects of defining a prediction end point. Here is one more. Always work with your business users to understand...

While defining the prediction rule end point, I have often seen the spec designed such that the data science behind it becomes logic...

The end result of a requirement gathering exercise for AI is often the end point definition for the desired prediction rule. Defining the...

A question I have been asked often, “Should we aim for 70% or 80% or 90% accuracy with our prediction rule?” Like most questions in life,...

So you have the prediction rule available. The model has been trained, and you have reasonable amount of confidence in it’s estimated...

Often AI is a high compute environment. At prediction time, the need for computational horsepower might be high, specially for...

So the data science is done, programming completed and the prediction API ready. It might also give you an estimate of expected accuracy...

My previous post was about selecting a meaningful training dataset. How do you know that the dataset once selected is good? Let me offer...

Supervised learning needs a training dataset to learn from. It is important to understand the nuances though. A meaningful training...

So how will you measure success of AI? As you start planning your AI implementation, ensure that there is enough thought given to it’s...

If Business Process Re-engineering (BPR) is new to you, here is a quick summary. Michael Hammer and James Champy were proponents of BPR...

He who never had a destination in mind often wandered. It is important to have a highly desired “to-be” state of AI. This state would be...

Implementing AI brings in change. I would broadly classify AI related changes into three types. The most obvious relates to processes....

As AI becomes mainstream, you can expect a lot of uptake of AI across multiple use cases in your organization. It is not unusual to find...

The role of envisioning and documenting “to-be” state for your application’s UX in an AI implementation is extremely important. At a very...

There is always an art of the possible. Possibilities exists in every discipline. Sure AI can do wonders to your processes or products....

So let’s say you are tasked with an AI implementation. The use case is known. There is a good understanding of people and processes...

It’s simple. Prediction rules learn from past data. If your data is bad, so will your prediction rule be. What then makes data bad? A few...

A common hurdle in implementation of AI is not whether AI works as needed, but the fear of unknown. These fears range from “I need to...

Our evolution as humans has followed a certain path. We were first very instinctive. Just like a primal instinct would suggest, the cave...