Certs
GCP Data Engineering - Round 2!
Its been two years since my last post on the Professional Data Engineer certification AND it was time for renewal. Successfully renewed the certification exactly 2 years later I wanted to update some of my recommendations on what I used. Linux Academy Google Cloud Documentation for the services - Big Query, Dataflow, BigTable, Pub/Sub , Composer and other big data services Solution approach Migrating Apache Spark to Dataproc Building your datalake Data Lifecycle When to use Dataflow vs Dataproc, BigTable vs Spanner vs Datastore, ML APIs vs Automl, Composer vs Kubeflow , Transfer Service vs Appliance, Pub/Sub vs Kafka IAM Permissions for all the services Background - Hadoop and its components Wishing you the best of luck for your certification
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 @Han Qi - Blog @Dmitri Larko -Blog Special Thanks to Steve Walker, Sanjay Agravat, Fernando Sanchez, Amit Rai, Michael Ross, Jamin Solensky and Yogesh Tiwari
