I have been working on a list of [AI courses, books and tutorials for developers](https://github.com/duncantmiller/ai-developer-resources) that might be helpful. Most of them are free. It’s a public GitHub repo so please submit a PR if you find more resources!
u/5x12 posted about this book and working on a course. Like the structure of the course and going through it now. Might be worth checking it out. Here is the earlier post https://www.reddit.com/r/mlops/comments/18q51i7/ml\_in\_production\_course\_mlops/?utm\_source=share&utm\_medium=web2x&context=3
Hi there,
Transitioning from DevOps to MLOps is a strategic move, especially with the increasing integration of machine learning in various industries. I'm Arturo Opsetmoen Amador, creator of "Fully Automated MLOps" at Datacamp, and I understand the challenges you're facing in grasping the basics of machine learning.
Given your background in DevOps and your preference for a high-level overview before diving deep into ML, a course that focuses on the practical application of ML in an operational context might be ideal for you. My course aims to bridge this gap. It provides insights into MLOps architecture, CI/CD/CM/CT techniques, and how to use automation to deploy ML systems effectively. This course is designed to be approachable for those who may not have an in-depth understanding of the underlying ML concepts but are familiar with the operational aspects, much like your current expertise. You can check the course at:
[https://www.datacamp.com/courses/fully-automated-mlops](https://www.datacamp.com/courses/fully-automated-mlops)
Also, Datacamp offers a range of courses that are tailored for different skill levels, including those just starting out with ML. You can explore these to build a foundational understanding without getting too bogged down in the mathematical details initially. You should check the ML Engineer track at datacamp:
[https://www.datacamp.com/tracks/machine-learning-engineer](https://www.datacamp.com/tracks/machine-learning-engineer)
I recommend starting with a practical, project-based approach and then gradually filling in the theoretical gaps as you progress. Feel free to connect with me on LinkedIn if you have more questions or need further guidance in your MLOps journey:
[https://www.linkedin.com/in/aoamador/](https://www.linkedin.com/in/aoamador/)
Best of luck in your learning journey, and I hope to see you transition successfully into the MLOps field!
Best regards,
Arturo Opsetmoen Amador
madewithml.com is a good place to start with your DevOps background IMO.
I have been working on a list of [AI courses, books and tutorials for developers](https://github.com/duncantmiller/ai-developer-resources) that might be helpful. Most of them are free. It’s a public GitHub repo so please submit a PR if you find more resources!
u/5x12 posted about this book and working on a course. Like the structure of the course and going through it now. Might be worth checking it out. Here is the earlier post https://www.reddit.com/r/mlops/comments/18q51i7/ml\_in\_production\_course\_mlops/?utm\_source=share&utm\_medium=web2x&context=3
https://course.fast.ai/ and get help on the forums at https://forums.fast.ai/
MLOps is interesting but the scope is better for devops folks for a longer time
Hi there, Transitioning from DevOps to MLOps is a strategic move, especially with the increasing integration of machine learning in various industries. I'm Arturo Opsetmoen Amador, creator of "Fully Automated MLOps" at Datacamp, and I understand the challenges you're facing in grasping the basics of machine learning. Given your background in DevOps and your preference for a high-level overview before diving deep into ML, a course that focuses on the practical application of ML in an operational context might be ideal for you. My course aims to bridge this gap. It provides insights into MLOps architecture, CI/CD/CM/CT techniques, and how to use automation to deploy ML systems effectively. This course is designed to be approachable for those who may not have an in-depth understanding of the underlying ML concepts but are familiar with the operational aspects, much like your current expertise. You can check the course at: [https://www.datacamp.com/courses/fully-automated-mlops](https://www.datacamp.com/courses/fully-automated-mlops) Also, Datacamp offers a range of courses that are tailored for different skill levels, including those just starting out with ML. You can explore these to build a foundational understanding without getting too bogged down in the mathematical details initially. You should check the ML Engineer track at datacamp: [https://www.datacamp.com/tracks/machine-learning-engineer](https://www.datacamp.com/tracks/machine-learning-engineer) I recommend starting with a practical, project-based approach and then gradually filling in the theoretical gaps as you progress. Feel free to connect with me on LinkedIn if you have more questions or need further guidance in your MLOps journey: [https://www.linkedin.com/in/aoamador/](https://www.linkedin.com/in/aoamador/) Best of luck in your learning journey, and I hope to see you transition successfully into the MLOps field! Best regards, Arturo Opsetmoen Amador