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MLOps are essential to the success of machine learning systems in production environments. Therefore, it's no surprise that organizations are looking for skilled MLOps engineers. But what does an MLOps engineer do?
The role of an MLOps engineer is fluid and varies from organization to organization. However, it is both compelling and naive to think that MLOps engineers are more end-to-end than data scientists. In other words, their work goes beyond building machine learning models, and is responsible for building, deploying, and monitoring models.
This article is a compilation of Google's MLOps courses. This will help you learn the basics of production machine learning systems, with a focus on Google's Vertex AI platform.
let's start!
1. Machine learning system in production environment
To understand and evaluate MLOps, it is important to first understand how machine learning systems operate in production. The Machine Learning Systems in Production course teaches you how to implement machine learning systems in production, with a focus on:
- Static, dynamic and continuous training
- Static and dynamic reasoning
- Batch and online processing
Here are some of the key modules in this course:
- Designing a production ML system
- Designing adaptive ML systems
- Designing high-performance ML systems
- Building a hybrid ML system
Link: Machine learning systems in production
2. Machine Learning Operations (MLOps): Introduction
The Machine Learning Operations (MLOps): Getting Started course is an introduction to machine learning operations. Thus, you can learn how to deploy, test, monitor, and evaluate machine learning systems in a production environment.
Learn about Google's Vertex AI platform Learn about MLOps tools and best practices. The modules in this course are:
- Adopting machine learning operations
- Vertex AI and MLOps on Vertex AI
Link: Machine Learning Operations (MLOps): Introduction
3. Machine Learning Operations (MLOps) with Vertex AI: Managing Features
The Machine Learning Operations (MLOps) with Vertex AI: Managing Features course helps you further your knowledge of running MLOps on the Google Cloud platform with a focus on the Vertex AI feature store.
Thus, you can become familiar with deploying, monitoring, and operating ML systems on Google Cloud. Introducing the Vertex AI feature store and its key capabilities.
Link: Machine Learning Operations (MLOps) with Vertex AI: Managing Features
4. ML pipeline on Google Cloud
This course, ML Pipelines on Google Cloud, is an in-depth course focused on building and orchestrating ML pipelines on Google Cloud Platform. This course has multiple modules covering the following key topics:
- Build and orchestrate ML pipelines using TensorFlow Extend (TFX), Google's production ML platform
- CI/CD for machine learning
- Automate your ML pipeline
- Orchestrate a continuous training pipeline using Cloud Composer
Link: ML pipelines on Google Cloud
5. Build and deploy machine learning solutions with Vertex AI
In the Build and Deploy Machine Learning Solutions with Vertex AI course, you will work through real-world use cases to train and deploy machine learning solutions.
In this course, you will explore the following enterprise ML use cases:
- Retail customer lifetime value prediction
- Mobile game withdrawal prediction
- Visual auto part defect identification
- Fine-tuning BERT for review sentiment classification
Along the way, you'll also learn how to leverage AutoML.
Link: Build and Deploy Machine Learning Solutions with Vertex AI
summary
We hope that by working through these courses and the labs that are part of them, you will have a good understanding of building and deploying machine learning solutions using Vertex AI.
If you're looking for a comprehensive bootcamp to learn MLOPs, check out DataTalks.Club's MLOps Zoomcamp. To learn more about this bootcamp, check out “The Only Free Course You Need to Become a Professional MLOps Engineer.”
Rose Priya C I'm a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her interests and expertise include DevOps, data science, and natural language processing. She loves reading, writing, coding, and coffee. Currently, she is committed to learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion articles, and more. Bala also creates engaging resource summaries and coding tutorials for her.
