# Introduction: Priorities
What’s important to us? What are we trying to optimize for?
# Operations: How I learned to Stop Worrying and Love the O Word
## Operations: What Is It Good For?
If you’re thinking operations is about continuous delivery and production,
you’re thinking too small. Operations are “A process or series of acts
involved in a particular form of work” — it’s everything your company does to
actually make money or accomplish its mission, and it includes things like how
you develop code. Looking at operations holistically means you can (and SHOULD)
stop being afraid of the O Word.
## Operations Engineering
Every company spends some effort and resources on engineering the way it
performs its operations. Whether you want to think of this as a first-class
discipline or not, you will do this — but if you don’t think about Operations
Engineering you’re likely to do it less well. What do we think of as
Operations Engineering? How does this tie into Operational Excellence
principles? How does this actually manifest as the Operations Engineering group
at Netflix?
# Hierarchy and Velocity
## Cross-functional teams
Hierarchy is to a great degree irrelevant at Netflix because engineers are
encouraged to work directly with engineers in other groups to get their work
done. This work is not approved, negotiated, or mediated by managers.
### Examples
* We’ll discuss the creation of Python as a first-class language in the Netflix cloud environment as an unsanctioned project championed by one engineer and supported by other engineers with no need for management support;
* We’ll discuss Scryer, our Predictive Auto-Scaling Engine, its creation as a cross-functional collaboration between two engineers, and ongoing work on it;
## That Sounds Perfect!
Perhaps we should talk about the downsides, then:
* It’s more important to us that engineers are empowered to make decisions than that they consistently make the right decision. This means the threshold for intervention by a manager is typically high. Engineers SHOULD make more mistakes, because they should make many more decisions than in most environments. This has to be acceptable;
* Because a technology may be developed in any organization that wants to develop it, it may not be developed in the organization which would do it most efficiently; as a result, it’ll be more costly or take more time to develop than it would have otherwise.
We’ll also share examples of both of these.
