Real-world End to End Machine Learning Ops on Google Cloud

About this course

Google Cloud Platform is gaining momentum in today's cloud landscape, and MLOps is becoming indispensable for streamlined machine learning projects

In the fascinating journey of Data Science, there's a significant step between creating a model and making it operational. This step is often overlooked but is crucial – it's called Machine Learning Ops (MLOps). Google Cloud Platform (GCP) offers some powerful tools to help streamline this process, and in this course, we're going to delve deep into them.

Topics covered in the course : CI/CD Using Cloud Build,Container and Artifact Registry

Continuous Training using Airflow for ML Workflow Orchestration: Writing Test Cases Vertex AI Ecosystem using PythonKubeflow Pipelines for ML Workflow and reusable ML components

Deploy Useful Applications using PaLM LLM of GCP Generative AI Why Take This Course? Tailored for Beginners with programming background: A basic understanding and expertise of data science is enough to start. We'll guide you through everything else.

Practical Learning: We believe in learning by doing. Throughout the course, real-world projects will help you grasp the concepts and apply them confidently.

GCP Professional ML Certification Prep: While the aim is thorough understanding and implementation, this course will also provide a strong foundation for those aiming for the GCP Professional ML Certification.

Your Takeaways

By the end of this course, you won't just understand the theory behind MLOps, you'll be equipped to implement it. The practical experience gained will empower you to handle real-world ML challenges with confidence.

The relevance of machine learning in today's

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