SAI: New Year's Special
Let's look into what is brewing for Year 2023
👋 This is Aurimas. I write the weekly SAI Newsletter where my goal is to present complicated Data related concepts in a simple and easy to digest way. The goal is to help You UpSkill in Data Engineering, MLOps, Machine Learning and Data Science areas.
Today’s episode will be light on Technical topics. We will look into:
What is in store for SwirlAI in year 2023.
5 must read books for a MLOps/ML Engineer of 2023.
Year 2022 in review and plans for 2023.
2022 was a rollercoaster for me:
➡️ At the beginning of May I decided to quit a Leadership role for a hands-on sabbatical - I joined an amazing company as a MLOps Engineer.
➡️ Two months in I posted my first regular Linkedin post at the 1.300 follower mark.
➡️ Continued to post regularly for the past 6 months - two days ago I woke up to realize that I reached a milestone of 30.000 followers. Few days ago I won the award for Biggest Influencer of the year 2022 by MLOps Community.
➡️ Almost 3 months ago I released the first episode of SwirlAI Newsletter which now has 11 episodes and 3.500 Subscribers.
Here is what is brewing for 2023:
➡️ I will be creating a Youtube channel where I will host videos revolving around MLOps, DataEngineering and MachineLearning topics. I believe that this is how I will be able to express my ideas even more clearly and become closer with the community.
➡️ I will be expanding the SwirlAI Newsletter by introducing new sections:
👉 SAI: This section remains the same - it will be sent out weekly and contain the content I create on Linkedin.
👉 SwirlAI Curated: Currently the content of the Newsletter is all over the place as I am not following specific order when posting on Linkedin. In this section I am going to curate the content by grouping it into topics and expanding on them and diving deeper. Posts will read more like a book - think a book on Spark, a book on Kafka, MLOps etc. I will also be fixing any errors I make while writing for Linkedin while SAI will remain static and not change.
👉 SwirlAI Long Form: This is where I will put my thoughts into long form content. Topics will vary.
👉 SwirlAI Learning: This is the one I am really excited about. I plan to launch a hands-on course on Data Engineering and MLOps.
❗️ The Newsletter will have more content compared to Linkedin going forward and sometimes the topics will be released before I post on Linkedin.
Naturally, it might happen that not all of the goals will be achieved or not every one of them will eventually make sense so I will be dynamically adapting while listening to your feedback.
5 books you must include in your 2023 reading list as a ML/MLOps Engineer.
[IMPORTANT]: I believe that ML and MLOps Engineer is not an entry level role so you should already have somewhat solid understanding of Python and general Machine learning concepts before considering this career path.
Having this out of the way - here is the list. I propose reading the books in this specific order:
1️⃣ “Designing Machine Learning Systems“ - A gem of 2022 in Machine Learning System Design. It will introduce you to the entire Machine Learning Lifecycle and prepare you for further deep dives.
2️⃣ “Accelerate“ - ML and MLOps Engineers are meant to bring Software Engineering practices to the Data Science world. After reading this book you will understand DevOps practices in and out.
3️⃣ “Machine Learning Design Patterns“ - The book introduces you to 30 Design Patterns for Machine Learning. You will find 30 recurring real life problems in ML Systems, how a given pattern tries to solve them and what are the alternatives. Always have this book by your side and refer to it once you run into described problems - the book is gold.
4️⃣ “Team Topologies“ - As ML or MLOps Engineer you might be placed into different types of teams. It could be ML Platform Team, Stream Aligned cross-functional Team or an Enabling Team. After reading this book - you will understand the purpose of these different Team Types and what the most efficient communication patterns between them are.
5️⃣ “Infrastructure as Code“ - This is the area that I see under-looked most when it comes to ML and MLOps Engineers. Most of the time you would be working in the cloud and your day-to-day would include a lot of IaaC, especially if you are part of a ML Platform Team. Learning IaaC will give you the edge in today's competitive markets.
