What you would learn in the Google Cloud Machine Learning Engineer Certification Prep course?
The Machine Learning Engineer position is a lucrative job that is in high demand and is becoming more critical for organizations that are moving their building data-intensive services into the cloud. The Google Cloud Professional Machine Learning Engineer certification is among the most well-known credentials in the field. This course will assist you in taking and passing the test. In particular, this course will assist you in understanding the specifics of:
Implementing and building models of machine learning to address business problems by using Google Cloud services and the best methods for machine learning
Models for machine learning: aspects of design, data pipelines structure, and optimization, as well being ability to monitor model performance in the production
Model development fundamentals as well as technology management for infrastructures, the engineering of data, and data governance
Data preparation, optimization of storage formats, exploratory data analysis, and handling missing data.
The use of feature engineering, data augmentation, and feature encoding to improve the chances of creating successful models
Be aware of the importance of responsible AI during the ML development process and use proper controls and oversight to ensure fairness and integrity in the machine-learning models.
At the end of this course, you'll be able to make use of Google Cloud services for machine learning. Moreover, you will be able to comprehend how machine learning works and the techniques required to utilize these services efficiently.
Course Content
- Learn to use Google Cloud tools to create, deploy and manage machine-learning models in production
- Make use of Vertex AI BigQuery, Cloud Dataflow, and Cloud Dataproc in ML pipelines.
- Training in tuning and servicing pipelines
- Select the appropriate infrastructure, such as virtual containers, machines, GPUs and TPUS
- How can you secure data during ML operations while also protecting the privacy?
- Watch machine learning models in production and be aware of when to Retrain models.
- Look through data to discover and fix issues, such as the imbalance in class and lack of data.
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