Advance your career as a Cloud ML Engineer
- Google Cloud Big Data and Machine Learning Fundamentals
- Feature Engineering
- ML Pipelines on Google Cloud
- Machine Learning Operations (MLOps): Getting Started
- Production Machine Learning Systems
- Machine Learning in the Enterprise
- Launching into Machine Learning
- How Google does Machine Learning
- TensorFlow on Google Cloud
Implement solutions using Google Kubernetes Engine, or GKE, including building, scheduling, load balancing, and monitoring workloads, as well as providing for discovery of services, managing role-based access control and security, and providing persistent storage to these applications.
- Google Cloud Platform Fundamentals: Core Infrastructure
- Architecting with Google Kubernetes Engine: Foundations
- Architecting with Google Kubernetes Engine: Workloads
- Architecting with Google Kubernetes Engine: Production
What is machine learning, and what kinds of problems can it solve?
- How Google does Machine Learning
- Launching into Machine Learning
- Intro to TensorFlow
- Feature Engineering
- Art and Science of Machine Learning
- Google Cloud Platform Fundamentals: Core Infrastructure
- Computing, Storage and Security with Google Cloud Platform
- Configuration Management on Google Cloud Platform
- Designing Highly Scalable Web Apps on Google Cloud Platform
- Google Cloud Platform Fundamentals: Core Infrastructure
- Essential Cloud Infrastructure: Foundation
- Essential Cloud Infrastructure: Core Services
- Elastic Cloud Infrastructure: Scaling and Automation
- Getting Started with Google Kubernetes Engine
- Google Cloud Platform Big Data and Machine Learning Fundamentals
- Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
- Introduction to Cloud Identity
- IBM Cloud: Deploying Microservices with Kubernetes
- Building Containerized Applications on AWS