Summary
1. Summary
Congratulations! You’ve made it to the end of the MLOps Fundamentals course. Throughout the course, you’ve been introduced to the MLOps concept and the considerations behind it. In the first section of the course, Employing Machine Learning Operations, you explored ML models from an operational perspective. You first examined the challenges that ML practitioners currently face when they operationalize ML models and make them available for production. Then, you were introduced to the concept of DevOps in ML and the phases of the ML lifecycle. After you explored the main phases of an ML lifecycle, you learned how these phases map to tasks within MLOps. In the second part of the course, the Vertex AI and MLOps on Vertex AI, you explored what Vertex AI is and why a unified platform is useful. Then you learned about the MLOps capabilities of Vertex AI and how Vertex AI helps with the MLOps workflow. In the hands-on lab at the end, you worked on a high-value, real-world use case: predictive customer life value. You started with a local BigQuery and TensorFlow workflow and then progressed toward training and deploying your model in the cloud with Vertex AI. This course is just the beginning of your machine learning operations journey. Stay tuned for future machine learning operations courses with Google Cloud. For more training and hands-on practice with ML and AI, please explore the options available at cloud.google.com/training/machinelearning-ai And if you’re interested in validating your expertise and showcasing your ability to transform businesses with Google Cloud technology, you might consider working toward a Google Cloud certification. You can learn more about Google Cloud’s certification offerings at cloud.google.com/certifications. Thanks for completing this course. We’ll see you next time!2. Let's practice!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.