Course Description
Our Google Cloud Professional Machine Learning Engineer course is designed for data scientists and ML practitioners who want to build, operationalize, and maintain machine learning (ML) models on Google Cloud Platform (GCP). This certification validates your expertise in designing, building, and deploying scalable, reliable, and secure ML solutions, positioning you as a leading expert in the field.
Course Highlights
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End-to-End ML Workflow: This course covers the entire lifecycle of a machine learning project, from data ingestion and preparation to model training, deployment, and monitoring. You will learn to apply best practices for building robust and scalable ML pipelines.
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Deep Dive into Google Cloud AI/ML Services: We will explore a wide range of GCP’s powerful AI/ML services, with a strong focus on Vertex AI, Google Cloud’s unified platform for ML development. You will also learn about services for data handling like BigQuery, and for training and deployment like Cloud Storage and Cloud Functions.
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Hands-On, Project-Based Learning: This course is built around practical, hands-on labs and projects. You will get to work with real-world datasets, allowing you to build a portfolio of projects that demonstrate your ability to solve complex ML problems and operationalize your models.
What You’ll Learn
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Architecting ML Solutions: Master the art of designing scalable and cost-effective ML solutions on GCP, including choosing the right services for a given problem and integrating them into a cohesive architecture.
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Data Preparation and Processing: Learn to prepare, clean, and transform datasets for ML. You will use services like Dataflow and BigQuery to build efficient data pipelines.
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Model Development and Training: Gain hands-on experience in building and training ML models using frameworks like TensorFlow and scikit-learn. You’ll also learn to manage experiments and use services like Vertex AI Training to train models at scale.
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Model Deployment and Serving: Understand how to deploy your trained models to a production environment. You will learn to use Vertex AI Endpoints for online serving and to manage model versions and updates.
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ML Operations (MLOps): Learn the principles of MLOps to ensure the reliability and maintainability of your ML systems. This includes automating model retraining, monitoring model performance, and setting up alerts for model drift.
Who This Course Is For
This course is for ML professionals who are ready to specialize in the cloud, including:
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Machine Learning Engineers: Validate your skills and become a leading expert in building and operationalizing ML solutions on GCP.
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Data Scientists: Elevate your career by learning how to take your models from a notebook to a production environment.
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Solutions Architects: Gain the deep technical knowledge needed to design and implement end-to-end ML architectures on GCP.
By completing our Google Cloud Professional Machine Learning Engineer course, you will earn a certification that is highly sought after by employers and positions you as a leading expert in the most exciting field in technology today.
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