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MartinY Tech
United States
เข้าร่วมเมื่อ 22 พ.ค. 2022
6 HOURS OF MLOPS MASTERY: THE ONLY TUTORIAL YOU NEED!
Dive into the world of MLOps with our comprehensive video series designed to transform your understanding of Machine Learning Operations. From foundational concepts to advanced deployment strategies, each episode is packed with insights and practical tools to help you master the MLOps lifecycle. Whether you're a data scientist, ML engineer, or simply curious about the industry, this playlist will equip you with the knowledge you need to excel in the fast-evolving landscape of AI and data science.
0:00:49 - 1 Introduction to MLOps
0:13:20 - 2 MLOps Lifecycle Toolkit
0:24:05 - 3 Introduction to MLOps
0:32:36 - 4 Foundations for MLOps Systems
0:48:55 - 5 Introduction to MLOps and Foundations
0:56:44 - 6 Tools and Techniques for Implementing MLOps
1:06:00 - 7 Introduction to Infrastructure for MLOps
1:17:53 - 8 Containerization for Data Scientists
1:26:47 - 9 Introduction to Building Training Pipelines
1:36:51 - 10 Experiment Tracking with MLFlow
1:45:29 - 11 Introduction to Inference Pipelines
1:58:55 - 12 Advanced Concepts in Inference
2:09:39 - 13 Introduction to the Spiral MLOps Lifecycle
2:20:38 - 14 Deployment Models in Data Science
2:33:17 - 15 Introduction to Data Ethics
2:42:39 - 16 Advanced Considerations in Data Ethics
2:51:21 - 17 Applications of Data Science Across Industries
3:03:01 - 18 The Evolving Role of Domain Experts in Data Science
3:14:07 - 19 Introduction to Machine Learning Engineering
3:24:27 - 20 What does an ML solution look like?
3:34:51 - 21 Overview of ML Engineering
3:44:44 - 22 Setting Up Development Processes
3:54:09 - 23 Defining the model factory
4:03:07 - 24 Building the model factory with pipelines
4:16:09 - 25 Overview of Machine Learning Solutions
4:24:01 - 26 Best Practices for Packaging Python Code
4:33:51 - 27 Introduction to Machine Learning Deployment
4:43:15 - 28 Introduction to MLOps and Model Deployment
4:53:36 - 29 Packaging and Deploying Models as a Service
5:04:49 - 30 Managing Model Versions and Rollbacks
5:11:56 - 31 ntroduction to MLOps and Model Deployment
5:21:59 - 32 In-depth Review of Monitoring and Logging
5:33:26 - 33 Cost Optimization Strategies in OpenShift
5:42:15 - 34 Introduction to Machine Learning with Red Hat OpenShift
5:51:46 - 35 End-to-End Workflow for Face Detection
Business contact: mpyanev@gmail.com
0:00:49 - 1 Introduction to MLOps
0:13:20 - 2 MLOps Lifecycle Toolkit
0:24:05 - 3 Introduction to MLOps
0:32:36 - 4 Foundations for MLOps Systems
0:48:55 - 5 Introduction to MLOps and Foundations
0:56:44 - 6 Tools and Techniques for Implementing MLOps
1:06:00 - 7 Introduction to Infrastructure for MLOps
1:17:53 - 8 Containerization for Data Scientists
1:26:47 - 9 Introduction to Building Training Pipelines
1:36:51 - 10 Experiment Tracking with MLFlow
1:45:29 - 11 Introduction to Inference Pipelines
1:58:55 - 12 Advanced Concepts in Inference
2:09:39 - 13 Introduction to the Spiral MLOps Lifecycle
2:20:38 - 14 Deployment Models in Data Science
2:33:17 - 15 Introduction to Data Ethics
2:42:39 - 16 Advanced Considerations in Data Ethics
2:51:21 - 17 Applications of Data Science Across Industries
3:03:01 - 18 The Evolving Role of Domain Experts in Data Science
3:14:07 - 19 Introduction to Machine Learning Engineering
3:24:27 - 20 What does an ML solution look like?
3:34:51 - 21 Overview of ML Engineering
3:44:44 - 22 Setting Up Development Processes
3:54:09 - 23 Defining the model factory
4:03:07 - 24 Building the model factory with pipelines
4:16:09 - 25 Overview of Machine Learning Solutions
4:24:01 - 26 Best Practices for Packaging Python Code
4:33:51 - 27 Introduction to Machine Learning Deployment
4:43:15 - 28 Introduction to MLOps and Model Deployment
4:53:36 - 29 Packaging and Deploying Models as a Service
5:04:49 - 30 Managing Model Versions and Rollbacks
5:11:56 - 31 ntroduction to MLOps and Model Deployment
5:21:59 - 32 In-depth Review of Monitoring and Logging
5:33:26 - 33 Cost Optimization Strategies in OpenShift
5:42:15 - 34 Introduction to Machine Learning with Red Hat OpenShift
5:51:46 - 35 End-to-End Workflow for Face Detection
Business contact: mpyanev@gmail.com
มุมมอง: 167
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Can you please tell the pre requisites
Lol nice experiment
Thank you!
woooow thank you !! please do you have a document or slides to follow along?
I usually do not save the slides after the video is generated, but I can do that in the future if helpful.
You are doing a great job Bro
Cheers!
your natural voice would've been good to hear then Ai
I’m agree. We are improving. We are working on making it more natural. Thanks for the comment it help us improve.
HI Sir.. can you make vidoe on APEX coding please
You got it!
Hi there! Thank you for your content! I'm not 100% sure, but I think there was a great long video on Python + Pytest + Playwright + etc made by you, and it got disappeared from YT. Is there any way to watch it? Or is this the one? I thought it was much longer than 1h 13m
I will have it added in this channel soon. I had to report that video as someone else uploaded it. It will be back soon better than before ;)
@@martinytech Great news! Looking forward to it! That's a whole lotta work, I'm sure the community will appreciate it!
Oh I was wondering when I deleted it from my downloads and was so worried cos I was building a project from it. Glad you will bring it to your channel @@martinytech
Keep doing such! We are watching...
Thank you. That topic you would like to learn?
Love it!! Can you do a similar long course for kubernetes services and crossplane?
Yes, coming soon!
Do you have plan to go more depth with all 10 hrs in console or with codes ? This one is just seems you just talking
Certainly, the course is 90% console inputs and coding.
Thank you very much. Very helpful.
You are welcome!
good
Best
So I'll ask. What was up with the screaming in the middle of Kubernetes Networking Model?
Thanks for noticing should be fixed in a few hours.
Great video Marto, it's pleasure for me to watch and write every single video. I wish to be a student where you teaching.Keep up the great work.
Thanks! 😃
woahhhhhh thank you
Welcome!!
Thanks a lot!!!
We need more advanced deep dive into this DevSecOps....i have subscribed and looking forward
I want to learn to code. I need someone to teach me write code
thx man!
You are welcome
can u please provide images for MontyHall game
Hi you can access all images and the code from the links in the description.
@@martinytech I can't see the link that you add into the description.
I am advancing my knowledge in Python and boom your video popped up , i am subscribing and liking this video as well to learn more
Was this for a class of 2nd years?
free now ? thanks very much.
that's really great sir..nice work
Thank you! Cheers!
Thnx
Thanks
Excellent
Thank you so much 😀
thanks , very helpful
Glad to hear that!
This is definitely a great tutorial. However, it will be highly appreciated if you could share the link for templates and static files so as to code along
thank you very much sir
its very nice course regarding devsecops.but here i want to give a suggestion that you miss here CICD part, like integration of SAST and DAST in application security
can we get ppt?
excellent job, really helpful and full of information
Glad it was helpful!
It would be really appreciable if you covered the HTML aspect.since all backend data is being linked through html it really is a connundrum to figure how HTML is to be structured and more, especially how ve you structured it out!! Since video is already made and you can barely do anything now , it would be great if you could share your github repo!!
Noted
Thank you for the course
No problem ;)
I really enjoyed this class!
Glad you enjoyed it!
I'll study and send feedback
Hello Martin, please take a beer everywhere you want and send me the bills....thanks so much for this amazing content
Deal!
The course isnť available (17.9.2024) -> please fix it or take me another course, thanks
Noted
Thanks sir it's help me lot
Most welcome
Great
Ur channel has amazing videos but u don't have managed them in playlists.. Please manage the series in playlists Like devops backend etc
Garcia Donna Lopez Paul Jackson Cynthia
Where are the resources that contain html and css files you instructed to import from?
Coming soon
Wilson Jennifer Walker Brenda Perez Kevin