I personally love to know and see what others are reading, and I assume many of you do as well.

So I want to quickly share eight of these books.

Now, I rarely buy physical books.

I read 98% of books on my Kindle or the Kindle app on my iPad.

I don't have some weird idea a physical page is smelling good or that having a physical book in my hand is some sort of magical feeling.

Content is content.

However, when it comes to some tech books, coding books, IT books, you just have to have the real thing.

The diagrams, the charts, the way you study the book, it just can't be done well digitally.

Now, I've worked as a full-stack dev.

I've worked in DevOps, site reliability engineering, developer relations, and all the while feeling like I'm kind of missing the big picture.

Like there was just a lot I didn't understand or didn't grasp very well, and I'm gonna fill those gaps this year with a more robust understanding of designing systems and computing and now AI concepts.

So again, I wanna share with you eight books that I'm diving deep into this year, books that can only be really digested well physically.

And I plan to finish out these eight throughout the year.

And if you're also into continuous learning or interested in book studies with other professionals, career development, or networking, then be sure to check out the Travis Media community.

It's currently only nine bucks a month to join.

Right now, full access, but we'll be going up again when we reach 200 members.

And we just started a new book that we're reading together called Algorithms to Live By, where we'll have biweekly calls to discuss what we're learning.

The content for this video was also posted there a week or two ago as well.

Anyway, if that interests you, check it out, link below.

So here are the books I'm lining up this year in no particular order.

Number one, Designing Data Intensive Applications.

So there's a very popular system design YouTube channel called System Design Fight Club.

This guy's worked at Amazon and he currently works at Apple as a senior developer.

And I saw him on a video recently on another channel where he was reviewing a lot of system design books.

And this book he stated was his top pick of all time.

And I looked into it and it seems to be everybody's top choice.

So I have to read it this year.

It's an O'Reilly book.

It's not new.

I'm always late to the game.

But if you haven't read it,

I think you should join me in doing so.

However, if you take a glance at this and it looks a bit over your head at the stage you're at, then he recommends in that video starting with the system design primer first, which is really good, but it's in the form of a GitHub repo with links to watch and read as you work through it.

I'll put a link to that below as well.

In fact, all of these will be listed below.

Number two, Building LLMs for Production,

Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG.

So I wanted a book this year that helps me understand the core concepts behind LLMs, which are pretty much solidified as a player in all things future.

Not really the machine learning, but the side of things that I think AI engineers or developers who are integrating AI into their apps need to know, like Transformers, Performance, RAG,

Leng Chen, Agents, Fine-Tuning, and most importantly, Deployment.

How do you go about deploying these apps in production?

This book has great reviews and has been recently updated, so I'll be working through this one this year as well.

Number three, System Design Interview,

An Insider's Guide, Volume One and Two.

Now, not everyone will need to undergo a systems design interview, but I think everyone would greatly benefit from the study of system design on its own.

How does software work as a system?

The components, the architecture, the data flow.

It would do wonders to be able to zoom out from your everyday code and understand and be able to plan or architect a system around it.

And these two volumes cover concepts like rate limiters,

URL shorteners, chat systems, notification systems, web crawlers, message queues, and more particular systems like hotel reservations,

S3 object storage, digital wallets, Google Maps.

I mean, all of these are systems, and I think there's great value in studying how to put these together.

And these two volumes have been noted as two of the best out there.

Now, the fourth book is called Code,

The Hidden Language of Computer Hardware and Software.

This is an updated version of a 20-year-old classic explaining the workings of digital computers.

It's been recommended to me by lots of people, and I think will be an informative read.

Number five, The Staff Engineer's Path,

A Guide for Individual Contributors

Navigating Growth and Change.

I'm not a staff engineer, but I like to read books that are well ahead of me.

In these types of books, anyone at any level of IT will learn about leadership, growth, dealing with change, thinking strategically, and all that good stuff.

There are great reviews here that speak of it being a very well-rounded book.

Number six, The Kubernetes Book.

This book looks to cover all the bases, not in detail, but more of a reference book.

Since I'm largely shifting my focus back to Kubernetes this year, with the goal of getting all the certifications,

I'm going to keep this one on my desk in reference sections as I study them.

If you want more information on this pursuit and why I'm learning this, or going this direction this year, check out my last video, link up here somewhere.

This book has been recently updated this year, has great reviews, and I think will be a great companion to the CodeCloud courses that I'll be taking.

I also want to note that I'm currently working through

Acing the Certified Kubernetes Administrator Exam book on the Manning website.

So I have a subscription, and I can read all the books digitally, meaning I got to log into their site and read them there.

But this particular resource has been phenomenal as well, especially with all the practical exercises at the end of each chapter.

So if you're looking to take the CKA this year, check this one out.

They also have a live project for the CKA as well.

All right, the seventh book is going to be Observability Engineering,

Achieving Production Excellence, First Edition.

So one thing I want to lean into this year is not only observability, but AI observability in particular, and specifically in the context of Kubernetes.

Thus, I want to read a good book on observability engineering, a camp that many people don't think a lot about, but is vital to any and every healthy system out there.

Now, this is also an O'Reilly book, and it's fairly new, and looks to be an essential read for any DevOps or site reliability engineer.

And finally, my eighth book that I'll be reading this year is Why Machines Learn, The Elegant Math Behind Modern AI.

So this is going to be a tough read for me as I'm not a big math nerd, but I want to be challenged in this field this year, especially in the context of AI.

I've seen this book recommended many places, and I'm going to give it a whirl.

So those are my eight physical, more in-depth books that I'm planning to read throughout this year.

What did I miss?

What can I add to this or swap out from this list?

What would you recommend?

Let me know down in the comments.

If you found this video helpful, give it a thumbs up.

If you haven't subscribed to the channel, consider doing so, and I'll see you in the next video.

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