Machine Learning And MLOps Roadmap - Learning MLOps In 16-20 Week Plan #machinelearning #mlops

What is MLOps, and more importantly, how can you learn more about it quickly?

I put together a roadmap for MLOps.

If you want to learn more about machine learning and you need a course, then check out DataCamps Machine Learning Course

So What Is MLOps?

A group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. This paper pointed out a growing problem that many companies might have been ignoring.

Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. [D. Sculley, Gary Holt, etc]

Another way the researchers put this in a follow-up presentation was that launching a rocket was easy, but ongoing operations afterwards was hard. Back then, the concept of DevOps was still coming into its own, but these engineers and researchers realized that there were many more complications in terms of deploying a machine learning model vs. deploying code.

This is when the popularity of machine learning platforms began to rise. Eventually, many of these platforms adopted the term MLOps to explain the service they were providing.

Article:

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What Skills Do Data Engineers Need?

Data Engineering Project Ideas

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0:00 - Intro
3:00 - Math Review Resources
5:41 - Machine Learning Review
8:00 - Kubernetes
9:58 - Kubeflow
13:45 - Azure ML
18:54 - Datarobot
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Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consults on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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[matthewnigh]

Great content and thanks for such detailed content. I’d love to see a data engineering manager roadmap as well - recently moved into that role myself.

[johnrobert]

I am a Data Scientist that just moved to Data and ML engineering, I have been following your content and I must say they are incredibly amazing. You are an inspiration for me and I will like to have similar skills as you. I may not be able to take all these coursed because I do a 9 -5 and I am more hands-on. Please can you share a hands-on approach as you did for data engineering?

[ninjaturtle]

Thanks for the video. This video has stuff nobody will teach you. Knowing what you don't know is great power.
Do you have any updates on this? What do you think is ABSOLUTELY necessary for someone trying to do this?

[SeattleDataGuy]

1. Cracking The FAANG Interview - 3-6 Months Prior
2. Data Engineering Project Part 2 - I am not super happy with it right now, so I might refilm some of it.

#dataengineering #mlops

[joeeeee]

Thanks for the details. Great content. (I just wonder if you should actually do the courses before reccomending them, but great explanation nonetheless)

Request: please do a data engineering project. That would elevate your channel as there are not many in YT with explanations like yours

[charlysimo]

Is there an update on this video? As in what was your experience going through the roadmap etc

[sidharthbolar]

Great roadmap
Did u finish this content ? Wouldnt it be a better idea to have one mini project in the end covering all the topics?

[MrPersononutube]

Should we know this for DataEngnineerign aswell ? Consider it supplemental or not required ?

[InsideFact]

Mlops and machine learning engineer are different?

[Chiefnice]

Nice video!! Does this mean you are preparing to switch roles to an ML engineer? Or is it just for your consulting? And if you consciously chose data engineering over ML engineering up until this point, what were your reasons for doing so?

[shauryajain]

How much time it takes to get ready as de, given I have good experience with python and sql?