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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Ingesting appropriate data
• Impact of dependencies of machine learning models
• Distributed vs. single machine
• Choosing the appropriate training and serving infrastructure
• Operationalizing machine learning models
• Common sources of error (e.g., assumptions about data)
• ML APIs (e.g., Vision API, Speech API)
• Measuring, monitoring, and troubleshooting machine learning models
• Leveraging pre-built ML models as a service
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Deploying an ML pipeline
• Continuous evaluation
• Hardware accelerators (e.g., GPU, TPU)
• Use of edge compute
• Conversational experiences (e.g., Dialogflow)
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)

Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Prebuilt ML Models APIs for Unsaturated Data
• Performing Spark on Cloud Dataproc
• Production ML Pipelines and use of Kubeflow
• Creating a Data Lake
• Serverless Messaging Using Cloud Sub/Pub
• Big Data Analytics with Cloud Al Platform Notebook
• Building a Data Warehouse
• Introduction to Building Batch Data Pipelines
• Cloud Dataflow Streaming Features
• Introduction to Processing Streaming Data
• Custom Model building Utilizing Cloud AutoML
• Bigtable Streaming Features and High-Throughput BigQuery
• Introduction to Data Engineering
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Advanced BigQuery Performance and Functionality
• Serverless Data Processing with Cloud Dataflow
• Custom Model building Using SQL in BigQuery ML

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

Reference: https://cloud.google.com/certification/data-engineer