Machine Learning Ops
Raison D'être
The reason of the existence of this newsletter: I have been reading and following a number of venues to keep up to date for machine learning and systems engineering. This newsletter is going to be providing a curated list of items as well as commentary on those items. I am also planning to write every month a long form piece to outline where I see the industry going. It is called MLOps as it combines both machine learning, systems and operations. All the good stuff that enables machine learning to be useful.
Who is it for?
This newsletter will be a good use of your time if you are interested in systems engineering, machine learning and distributed systems. It will not be a good use of your time if you are only interested in the next exciting research in machine learning. Not that I will not be sharing my opinions around machine learning research, it will not be focus of this newsletter. Otherwise, who can ignore transformers and how much it has changed the industry and ramifications of deployment and usage of models.
Seminal paper from Google mentioned that “Change Anything Changes Everything(CACE)”, I strongly believe this principle especially in machine learning systems. As nothing can be thought in isolation, both machine learning research has impact on the systems(machine learning for systems), but also systems has impact on machine learning(systems for machine learning). For MLOps, the focus will be in the intersection of these two areas but we will still be plugged in both areas very strongly. This area is also my strength area as I have been doing this for close to a decade in my previous experience.
Big Audacious Goal
This field is just getting started(MLOps) and my aim with this newsletter to contribute how this industry will be shaping over the years.
Sign up now so you don’t miss the first issue.
In the meantime, tell your friends!
Raison D'être
The reason of the existence of this newsletter: I have been reading and following a number of venues to keep up to date for machine learning and systems engineering. This newsletter is going to be providing a curated list of items as well as commentary on those items. I am also planning to write every month a long form piece to outline where I see the industry going. It is called MLOps as it combines both machine learning, systems and operations. All the good stuff that enables machine learning to be useful.
Who is it for?
This newsletter will be a good use of your time if you are interested in systems engineering, machine learning and distributed systems. It will not be a good use of your time if you are only interested in the next exciting research in machine learning. Not that I will not be sharing my opinions around machine learning research, it will not be focus of this newsletter. Otherwise, who can ignore transformers and how much it has changed the industry and ramifications of deployment and usage of models.
Seminal paper from Google mentioned that “Change Anything Changes Everything(CACE)”, I strongly believe this principle especially in machine learning systems. As nothing can be thought in isolation, both machine learning research has impact on the systems(machine learning for systems), but also systems has impact on machine learning(systems for machine learning). For MLOps, the focus will be in the intersection of these two areas but we will still be plugged in both areas very strongly. This area is also my strength area as I have been doing this for close to a decade in my previous experience.
Big Audacious Goal
This field is just getting started(MLOps) and my aim with this newsletter to contribute how this industry will be shaping over the years.
Onwards!