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index.qmd
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# Preface
In 1979, two Swedish bands released albums. One of these albums,
*Voulez-Vous, *was recorded by ABBA in Polar Studios, at its time, one
of the most advanced --- and expensive --- recording studios in the
world. The other album, *We\'re Only in it for the Drugs, *by the
Swedish Punk band [Ebba Grön
](https://en.wikipedia.org/wiki/Ebba_Grön) was recorded on a mobile
8-channel mixer in a closed industrial office. Both these albums are
great, but the resources available to create them were vastly different.
There is a prevalent perception that machine learning is only for those
with deep pockets. For example, in 2022, Google created a new Language
Model, PaLM, but the cost of training such a model is estimated to be
between \$9M to \$23M. As a result, discussions of machine learning
often focus on large tech companies because they are deemed to be the
only ones with the finances to develop and use this technology.
Machine learning, and in particular a branch of machine learning called
deep learning, has dramatically impacted a range of domains over the
past ten years. There has been a growing interest in using machine
learning in gallery, library, archive and museum (GLAM) institutions. A
barrier to further adoption of machine learning in the GLAM sector is
the perception that it requires extensive technical skill, data,
computing power and other resources.
In this book, we want to show that just as [*Ebba
Grön*](https://en.wikipedia.org/wiki/Ebba_Grön) was able to record a
seminal album without the resources available to ABBA, GLAM institutions
can use machine learning for practical work without the resources of
Google.