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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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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:

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

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

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

Google Professional-Data-Engineer Exam Syllabus Topics: