Early last year, I wanted to learn about Machine Learning Operations(MLOps).

MLOps refers to the whole processes involved in building and deploying machine learning models reliably.

A thread on the importance of MLOps and resources that I used 🧵

As you may have heard, models are a tiny part of any typical ML-powered application.

There is nothing that stresses that as this picture:

Source: Hidden Technical Debt in Machine Learning Systems, https://t.co/JDyAr1s3kc
There are lots of critical processes that are involved in MLOps such as:

- Data processes: collection, labeling, exploration, preprocessing
- Modeling processes: building, training, evaluation, testing
- Production processes - Serving, monitoring, and maintaining models
MLOps is a new topic for almost anyone. Maintaining models for a prolonged period of time is difficult.

Models are very prone to change. They drift over time. The world (that sources the data) changes, and so data change too.
MLOps is a huge topic. All I wanted was to have a reasonable understanding of it.

Here are 3 resources that I used:

- Machine Learning Engineering book by @burkov
- MLOps Specialization by @DeepLearningAI_
- Introducing MLOps book Oreilly
Here are links for those resources:

- ML Engineering book: https://t.co/L5trxHGAw1
- Introducing MLOps: https://t.co/de4vxdzA5P
- MLOps specialization: https://t.co/46fhFSyEno
I also wrote a couple of blog posts as I was learning it. You can find the blogposts on Medium

https://t.co/DFp6LwqxRV
If you would like to get started with MLOps, I recommend you take MLOps specialization along with one of those books, preferably ML Engineering book.
Also, @MadeWithML by @GokuMohandas contains many hands-on resources for building and productionizing machine learning models.

I can't recommend it enough too!

https://t.co/WjYQpeXcTX
If you have mainly been building models, learning MLOps might be the next good step for you. It's a useful skill to have!
Thanks for reading!

If you would like to see more machine learning content and useful resources, follow me at @Jeande_d.

You can also share the thread with others if you found it helpful. Sharing is caring :)

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@franciscodeasis https://t.co/OuQaBRFPu7
Unfortunately the "This work includes the identification of viral sequences in bat samples, and has resulted in the isolation of three bat SARS-related coronaviruses that are now used as reagents to test therapeutics and vaccines." were BEFORE the


chimeric infectious clone grants were there.https://t.co/DAArwFkz6v is in 2017, Rs4231.
https://t.co/UgXygDjYbW is in 2016, RsSHC014 and RsWIV16.
https://t.co/krO69CsJ94 is in 2013, RsWIV1. notice that this is before the beginning of the project

starting in 2016. Also remember that they told about only 3 isolates/live viruses. RsSHC014 is a live infectious clone that is just as alive as those other "Isolates".

P.D. somehow is able to use funds that he have yet recieved yet, and send results and sequences from late 2019 back in time into 2015,2013 and 2016!

https://t.co/4wC7k1Lh54 Ref 3: Why ALL your pangolin samples were PCR negative? to avoid deep sequencing and accidentally reveal Paguma Larvata and Oryctolagus Cuniculus?