WHY THIS GUIDE?
A few years ago, this bold call-to-action statement appeared in a Harvard Business Review article: “Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t” (Brynjolfsson and Mcafee, 2017).
This call is so timely because by now, you probably have already witnessed many companies rushing to start using Machine Learning (ML) in their projects. According to International Data Corporation (2021), companies spent almost USD 342 billion on AI solutions globally in 2021. Despite this significant investment, however, many Machine Learning projects fail, which in itself doesn’t have to be an issue. The problem is that most ML projects fail too late, that is, when companies have already invested enormous resources to preprocess data, write code, run computations and build prototypes.
The goal of this book is to discard 80% of your Machine Learning project ideas already in the conceptualization phase. Our thinking is in line with the lean startup approach (Ries, 2011): we strongly believe in testing problem hypotheses with the customer as early as possible. However, you don’t need something as elaborate as a prototype or a minimum viable product (MVP) for the conceptual evaluation of ML project ideas. Rather you simply need to ask the right questions early on to gain some fundamental insights.
As our readers, you might be involved in the scoping process in one of various roles with different responsibilities. For example, as an executive you need...
To make sure we are all on the same page, let’s remember that Machine Learning, a subfield of Artificial Intelligence, designs software that learns to perform computational tasks from patterns in data instead of being explicitly programmed by humans. While a couple of decades ago, companies started using robotic hardware to scale manual labor, today, companies have started using robotic software to scale mental labor (Davila-Chacon, 2019).
Some (business) people new to Machine Learning think that the technology can perform magic tricks. However, even the greatest magicians simply demonstrate excellent craftsmanship that is replicable once you know what to do and when. So, even if some of the ML stuff seems to be magical, it all comes down to key principles that one needs to follow to successfully deploy an ML product (Ameisen, 2020).
Internal solutions require you to design, implement and maintain the required models and infrastructure, but in return, you will own the entire platform and benefit from a long-term cost advantage and a technological edge in the market. In this section, we provide an overview of key aspects to consider when designing internal Machine Learning solutions.
In this section we focus on finding the best pre-built solution from external providers. In their book Prediction Machines (2018), Agrawal et al state that “prediction is the process of filling in missing information”. Accurate predictions are great, but external providers are often black boxes that we need to make at least partially transparent to properly compare them with each other. Let’s start with a categorical framework to better understand the landscapes of Machine Learning providers.
Founded in 2018, the company provides advice, skills and technical solutions in all machine learning playing fields for the European, American and Asian markets.
Olaf Erichsen has ample experience in identifying, scoping, evaluating and executing all kinds of machine learning projects for a very diverse set of organisations. Since 2016, Olaf has evaluated more than 300 AI providers globally on behalf of industry leaders, institutional as well as private investors hence providing an excellent overview of what is promised and actually held in this field.
Dr. Jorge Davila-Chacon has ample experience in Data Science projects involving text, audio and visual data. He has designed Reinforcement Learning models that learn by direct customer interaction and generative models for the translation of image domains. Recently, he collaborated with the project Convergence which won the top prize for 2021 at the Prix Ars Electronica, "the world’s most time-honored media arts competition".
From 2018, Olaf has strategically advised and operationally helped US and European startups to shape and grow significantly, where one startup was acquired as a top technology M&A deals of 2020. Olaf is Lecturer at the Hamburg School of Business Administration (HSBA) and is co-founder of Heldenkombinat Technologies GmbH, where he serves as CEO and business advisor to clients and joint ventures.
Olaf holds a Diploma in Business Administration and Management from the University of Hamburg (Germany) and has successfully passed numerous advanced training courses in the field of artificial intelligence and machine learning, e.g. at the Universities of Stanford, Washington and Toronto (all via Coursera) as well as the AI Engineering Nanodegree and the Deep Reinforcement Learning Nanodegree (both Udacity). Olaf is a member of the ICT committees at the Chamber of Commerce Hamburg and at the national DIHK organisation in Germany.
Jorge is Lecturer at the Hamburg School of Business Administration (HSBA) and is co-founder and CTO of Heldenkombinat Technologies GmbH, where he designs custom Al solutions for the industry. Jorge holds an MSc in Artificial Intelligence from the University of Groningen (The Netherlands) and a PhD from the University of Hamburg (Germany). Additionally, he has participated in robotics and neuroscience programs with the Massachusetts Institute of Technology (MIT), Tsinghua University (China), the Stem Cell and Brain Research Institute (CNRS, France), the Speech, Music and Hearing Institute (KTH, Sweden) and the University of Hyderabad (India).
He is an invited reviewer for the Journal Robotics, for the Journal of Computer Speech and Language, for IEEE Transactions on Audio, Speech, and Language Processing, for the International Conference on Multisensor Fusion and Integration for Intelligent Systems, and was an organizing committee chair for ICANN 2014, the European flagship conference for artificial neural networks.