GCP Machine Learning engineer!

Extremely excited in achieving ML Engineering certification as I embark on my AI/ML journey with Google Cloud. There is not a lot of material available while preparing for this certification as the exam is released less than a month ago.

Study guide has most of the content on what to focus on. This exam is for ML Engineers. I would suggest anyone planning to take the exam first pursue the Data Engineering Exam(Prep content here) as the focus is more on data.

Key topics to focus on

  • Orchestration of the Machine Learning life cycle
  • Data Pre-processing, preparation and options
  • Feature Engineering strategies
  • Training and Deploying techniques , options - Pros and Cons
  • ML Ops and its workflow
  • Spectrum of options available - API, Auto ML , DIY and AI Platform
  • Understanding Gradient Descent, Loss , Regularization
  • High level understanding of Regression, Classification, Clustering, CNN, DNN, RNN

I used the below preparation contents. Hope it is of some help to you. Good luck with your exam. And thank you to the folks who helped me in identifying the key areas to focus on.

Machine Learning Crash Course - Google - MUST read even if you are not taking the exam

Have a good understanding of Google Cloud Services - Data prep, Data fusion, Data flow, Composer, API, Auto ML, AI Platform (all features), Bigquery ML

AI Platform documentation

Google Cloud Solutions - Wealth of content with architectures

Data Pre-processing

Data Life Cycle Platform

Analyzing and validating data at scale for machine learning with TensorFlow Data Validation

Building production-ready data pipelines using Dataflow: Deploying data pipelines

Data preprocessing for machine learning: options and recommendations

Data preprocessing for machine learning using TensorFlow Transform

Considerations for sensitive data within machine learning datasets

Machine learning with structured data: Data analysis and prep

Training and Prediction

Comparing ML models for Predictions using Dataflow pipelines

Best practices for performance and cost optimization for Machine Learning

Minimizing real time prediction serving latency in Machine Learning

Optimizing Tensorflow models for serving] (@Lukman Ramsey)

MLOps

MLOps: Continuous delivery and automation pipelines in machine learning

Setting up MLOps Environment on Google Cloud

Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build

Coursera content - Good Refresher

(Valentine Fontama, Valliappa Lakshmanan)

Production ML Systems

End to End ML with Tensorflow

Other blog posts with relevant content

Special Thanks to Steve Walker, Sanjay Agravat, Fernando Sanchez, Amit Rai, Michael Ross, Jamin Solensky and Yogesh Tiwari