This week, I spent two days in systems engineering workshops. Systems engineers are concerned with designing products and solutions including mechanical, electronic and software components. Systems engineers and architects address all requirements of a system, including regulatory constraints, such as functional safety, customer-facing functionality, such as the features that the customer uses on a daily … Read moreWho manages your system architecture?
One of the sayings that almost everyone in business has taken to heart is “data is the new oil” by Clive Humby. There are constant discussions about data ownership between end customers and product providers, as well as between OEMs and their suppliers. The first start-ups are now trying to advise companies on how to … Read moreHow clean is your data?
With data- and AI-driven development taking over the world, it may easily seem that the cloud is the place where everything happens. This is where the data is stored and analyzed, where the machine- and deep-learning models run and where all the value resides. The perspective of people living in this world is that all … Read moreLiving on the edge
For decades now, I’ve been in workshops with a number of companies that seek to change some aspect of their business. Reflecting on the more recent workshops, however, made me recognize patterns that seem to reappear frequently or typically. As we all know, change is hard. For individuals and even more so for organizations. However, … Read moreDon’t start from where you are
During the last few weeks, I’ve worked with several companies and identified a pattern that, in hindsight, I have seen many times before: a team gets stuck midway a change process and refuses to let go of the old ways while adopting new ways of working. In that way, the organization gets the worst of … Read moreThe worst of both worlds
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
Many companies that I work with are in the process of adopting continuous deployment of software – or DevOps. As part of that process, the notion of product variability comes up frequently because there often are multiple product variants out in the field. The software for each variant used to be created in a mostly … Read moreVariability and DevOps
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
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
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