Professional Data Engineer Certification Course (Rough Draft)

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Description of Professional Data Engineer Certification Course (Rough Draft)

Professional Data Engineer Certification Course: Harness the Power of Google Cloud

This course equips you with essential skills to excel as a data engineer using Google Cloud technologies. Dive into data processing systems design, implementation, machine learning model operation, and solution quality assurance. Learn key SQL features for handling, querying, and performing operations on structured data in Google Cloud.
Course Outline
Section 1: Mastering Design of Data Processing Systems

Storage Technology: Choosing the Right Option
    Data Pipeline: The Backbone of Data Flow
    Data Processing Solution: Designing for Efficiency
    Data Warehousing & Processing Migration: Seamless Transitions

Section 1 Videos

Introductory Thoughts on Data Engineering
    Welcome to the Course!
    Deep Dive into BigQuery and Prompt Engineering
    Creating Pipelines with BigQuery and Colab
    Exploring Data with Google BigQuery
    Getting Onboard with Google Cloud Platform

Section 2: Building & Operationalizing Data Processing Systems

Storage System: Implementing for Accessibility
    Pipeline Construction & Operationalization: Keeping the Data Flowing
    Processing Infrastructure: Building the Foundation of Your System

Section 2 Videos

Course Introduction to Building and Operationalizing Systems
    Deep Dive into Google Cloud Analytics Services
    Understanding Data Engineering Pipelines
    Strategic Planning for Google Cloud Storage
    Overview of Google Cloud Storage Options
    Optimizing Database Solutions in Google Cloud Platform

Section 3: Operationalizing Machine Learning Models

Pre-built ML Models as a Service: Easy and Effective Machine Learning
    ML Pipeline Deployment: Bringing Your Models to Life
    Training & Serving Infrastructure Selection: Laying the Groundwork for ML Success
    ML Model Measurement, Monitoring & Troubleshooting: Ensuring Peak Performance

Section 3 Videos

Welcome to Operationalizing ML Models
    The Five Whys of Machine Learning
    Load Testing Demonstrations
    MLOps on Google Cloud Platform
    Using Google Courses for Success
    Harnessing TensorFlow with Google Colab
    Natural Language Processing with Google Cloud
    Deploying Pretrained Models with PyTorch
    Running Pretrained PyTorch Models in Rust
    Understanding TPUs: Your Guide to Google's Tensor Processing Units
    The Technology Transition of TPUs
    Getting Started with VertexAI
    Using the CLI for Google's Vision API

Section 4: Ensuring Solution Quality

Security & Compliance Design: Protecting Your Data
    Scalability & Efficiency Assurance: Preparing for Growth
    Reliability & Fidelity Assurance: Trust in Your System
    Flexibility & Portability Assurance: Building for the Future

Section 4 Videos

Introduction to Ensuring Solution Quality
    Energy Efficiency: Comparing Python and Rust
    Integrated Data Security: A Closer Look
    What is Distroless?
    Build & Deploy: Rust Microservice Cloud Run Demo
    App Engine Rust Deployment Demo
    Rust Crate Audits: Ensuring Security
    Rust: Secure by Design
    Enhancing Productivity with Bard
    Using Copilot with Rust
    Continuous Integration: Rust with GitHub Actions
    Unit Test: Rust Demo

Master the design, build, and operationalization of data processing systems that meet business requirements, system requirements, and industry best practices.
    Develop scalable, efficient, and secure solutions with Google Cloud technologies to manage and analyze data at scale.
    Grasp the key syntax and features of the SQL language for handling, querying, and performing operations on structured data in Google Cloud.