Machine Learning Ops on Google Cloud:Real World Data Science

From Model Development to Deployment: Streamlining Machine Learning Workflows on Google Cloud

What you'll learn
Comprehensive understanding of Google Cloud Platform's suite for MLOps, diving deep into tools like Airflow,Cloud Build, Google Container and Artifact Registry
Hands-on proficiency in orchestrating, deploying, and monitoring machine learning workflows using GCP Composer/Airflow and Vertex AI services.
Best practices and methodologies to ensure scalable, reproducible, and efficient machine learning pipelines on the cloud.
Insights and techniques tailored to help in preparation for the GCP Professional ML Certification exam, bolstering your credentials in the cloud ML domain.
Requirements
Basic experience in developing Data science models ,concepts and terminologies
Working knowledge of Python, as the course will involve hands-on coding and scripting
Prior basic understanding and experience on using Google Cloud platform
Desire to expand and deepen skill sets in MLOps and cloud-based machine learning solutions
Description
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 Python
Kubeflow 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 world is undeniable, and with the rise of its importance, there's an increasing demand for professionals skilled in MLOps. This course is designed to bridge the gap between model development and operational excellence, making ML more than just a coding exercise but a tangible asset in solving real-world problems.
So, if you're eager to elevate your ML journey and understand how to make your models truly effective on a platform as powerful as Google Cloud, this course awaits you. Dive in, explore, learn, and let's make ML work for the real world together!
Who this course is for:
Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform.
Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCP's suite of tools.
Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively.
Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics.
Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices.


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