Abstract
In this chapter, we will discuss foundations for MLOps systems by breaking down the topic into fundamental building blocks that you will apply in future chapters. While we will discuss programming nondeterministic systems, data structures and algorithmic thinking for data science, and how to translate thoughts into executable code, the goal is not to give a fully comprehensive introduction to these areas in a single chapter but instead provide further resources to point you in the right direction and answer an important question: Why do you need to understand mathematics to develop and deploy MLOps systems?
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
An introduction to linear algebra can be found in Hoffman, K. A. (1961). Linear Algebra.
- 2.
For a full introduction to algorithmic thinking and computer programming, the reader is directed to Abelson, H. and Sussman, G. J. (1996). Structure and Interpretation of Computer Programs, second edition. MIT Press.
- 3.
Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods. In Springer texts in statistics. Springer International Publishing. https://doi.org/10.1007/978-0-387-92407-6.
- 4.
Semantic versioning 2.0.0 can be found at https://semver.org/.
- 5.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms. MIT Press.
- 6.
Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
- 7.
McElreath, R. (2015). Statistical Rethinking: A Bayesian Course With Examples in R and Stan. Chapman & Hall/CRC.
- 8.
Hastie, T., Tibshirani, R., & Friedman, J. (2013). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
- 9.
Halmos, P. (1993). Finite-Dimensional Vector Spaces. Springer.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature
About this chapter
Cite this chapter
Sorvisto, D. (2023). Foundations for MLOps Systems. In: MLOps Lifecycle Toolkit. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9642-4_2
Download citation
DOI: https://doi.org/10.1007/978-1-4842-9642-4_2
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-9641-7
Online ISBN: 978-1-4842-9642-4
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)