DataOps: the key to operational AI

One of the things that keep surprising me over and over again is how much effort companies spend on processing, cleaning, converting and preparing data. For the companies that I work with, the data science teams easily spend 90-95 percent of their time just preparing data for use in machine learning/deep learning deployments. This is … Read moreDataOps: the key to operational AI

It’s not what AI can do for you

Virtually any company that I work with is exploring its data sets and business processes to identify opportunities for productivity improvements, higher accuracy or lower cost. The constant question that these companies struggle with is how can artificial intelligence, and specifically machine learning (ML) and deep learning (DL), support existing processes and ways of working. … Read moreIt’s not what AI can do for you

How to develop software

In earlier posts (such as here), I have discussed different approaches to software development and one of the key models for this is the HoliDev model where we combine requirements driven development, outcome-driven development (e.g. A/B testing) and AI-driven development. In the figure below, the HoliDev model is shown graphically. It may easily seem that … Read moreHow to develop software

AI is not about data sets

As I’m spending an increasing amount of time in the AI field with a variety of companies, I’ve noticed an interesting misconception in the ML/DL space. Many have a tendency to focus on data sets, experimenting with different models using a specific set of data and, finally, deploying a model in a specific context. This … Read moreAI is not about data sets

You Think You Know, But You Don’t

Data-driven decision making frequently requires experimentation as a mechanism to acquire the necessary data. When working with companies in transitioning from opinion-based to data-driven decision making, however, I frequently run into push back. The typical comment is that we already know the answers to the specific questions at hand and since we already know this … Read moreYou Think You Know, But You Don’t

Becoming a Data-Driven AI Company

During the last week I was reflecting on the change processes ongoing in several of the companies that I work with. Although I don’t want to generalize too aggressively, it seems to me that most companies are on a similar journey. Starting from a situation where the key value proposition of the company was expressed … Read moreBecoming a Data-Driven AI Company

Machine & Deep Learning: Non-Critical Deployment

In last week’s article, I outlined the first phase of adopting machine & deep learning (ML/DL) which is concerned with experimentation and prototyping. In the figure below, we show the steps that companies typically evolve through while adopting AI,ML and DL solutions. As shown in the figure, the next step is the careful use of … Read moreMachine & Deep Learning: Non-Critical Deployment

Machine & Deep Learning: Experimentation Stage

This week I got the opportunity to speak at the initiative seminar organized by the Chalmers AI Research center (CHAIR). The key message in my presentation was that working with artificial intelligence (AI) and specifically machine & deep learning (ML/DL) constitutes a major software engineering challenge that is severely underestimated by companies that start to … Read moreMachine & Deep Learning: Experimentation Stage