The practice of automating and streamlining the process of deploying, administering, and growing machine learning models is known as “machine learning operations,” or MLOps. As businesses look to harness the power of machine learning to create economic value, the discipline of MLOps, which mixes software development and data science, is growing in significance.
You’re in luck if you’re interested in MLOps training or attending course because there are lots of materials out there that can assist you in getting started. We’ll look at some of the simplest approaches to learn MLOps in this article.
1. Online MLOps Courses
Online classes are among the simplest ways to study MLOps. Coursera, Udacity, and edX are just a few of the platforms that provide courses in machine learning and MLOps. Many of these courses are self-paced, allowing you to learn at your own pace, and they often contain video lectures, readings, and assignments.
Some popular MLOps courses include:
- Introduction to Machine Learning Operations (MLOps) by igmGuru
- Deploying Machine Learning Models by Udacity
- Applied Data Science with Python Specialization by Coursera
- Machine Learning Engineering for Production (MLOps) Specialization by Coursera
These classes are an excellent way to learn the fundamentals of MLOps, and they frequently contain practical assignments that let you put what you’ve learned to use in a practical setting.
2. Open-Source Tools
Getting involved with open-source tools and platforms that are employed in the industry is another excellent method to learn MLOps. Several well-liked open-source MLOps tools are as follows:
- Kubeflow: An open-source platform for running machine learning workflows on Kubernetes
- TensorFlow Extended (TFX): An end-to-end platform for building and deploying production machine learning pipelines
- MLflow: An open-source platform for managing and deploying machine learning models
- PyTorch: A popular open-source machine learning framework that can be used for MLOps
You can get practical experience with MLOps and gain a deeper understanding of how machine learning models are deployed and managed in production by working with these tools and making contributions to open-source projects.
3. Attend Conferences and Meetups
Attending conferences and meetups is another great way to learn about MLOps and connect with others in the field. There are many conferences and events that focus on machine learning and MLOps, including:
- MLOps World Conference
- Machine Learning Ops Conference
- Google Cloud Next
By attending these events, you will get an opportunity to hear from experts in the field, learn about new tools and technologies, and connect with other people who are interested in MLOps.
4. Work on Personal Projects
Finally, one of the best ways to learn MLOps is to work on personal projects. By building your own machine learning models and deploying them in a production environment, you can gain hands-on experience with MLOps and develop a deeper understanding of the challenges and opportunities in the field.
There are many resources available that can help you get started with personal projects, including:
- Kaggle: A platform for data science competitions and projects
- GitHub: A platform for hosting and collaborating on open-source projects
- DataCamp: An online learning platform with courses and projects in data science and machine learning
By working on personal projects, you can gain experience with MLOps tools and platforms, develop your skills in data science and software development, and build a portfolio of work that can demonstrate your expertise to potential employers.
Conclusion
MLOps is an exciting and rapidly evolving field that offers many opportunities for learning and growth. By taking advantage of online courses, open-source tools, conferences and meetups, and personal projects, you can develop the skills and experience you need to succeed in MLOps. To learn more about MLOps, go through this MLOps tutorial for beginner.
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