Google Cloud Certified Professional Machine Learning Engineer Training

Topic 1 Google Cloud Big Data and Machine Learning Fundamentals

• Data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
• Design streaming pipelines with Dataflow and Pub/Sub and dDesign streaming pipelines with Dataflow and Pub/Sub.
• Options to build machine learning solutions on Google Cloud.
• Machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.

Topic 2 How Google does Machine Learning

• Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing any code
• Best practices for implementing machine learning on Google Cloud
• Leverage Google Cloud tools and environment to do ML
• Responsible AI best practices

Topic 3 Launching into Machine Learning

• Improve data quality and perform exploratory data analysis
• Build and train AutoML Models using Vertex AI and BigQuery ML
• Optimize and evaluate models using loss functions and performance metrics
• Create repeatable and scalable training, evaluation, and test datasets

Topic 4 TensorFlow on Google Cloud

• Create TensorFlow and Keras machine learning models and describe their key components.
• Use the tf.data library to manipulate data and large datasets.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation.
• Train, deploy, and productionalize ML models at scale with Vertex AI.

Topic 5 Feature Engineering

• Describe Vertex AI Feature Store and compare the key required aspects of a good feature.
• Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
• Discuss how to preprocess and explore features with Dataflow and Dataprep.
• Use tf.Transform.

Topic 6 Machine Learning in the Enterprise

• Describe data management, governance, and preprocessing options
• Identify when to use Vertex AutoML, BigQuery ML, and custom training
• Implement Vertex Vizier Hyperparameter Tuning
• Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI

Topic 7 Production Machine Learning Systems

• Compare static versus dynamic training and inference
• Manage model dependencies
• Set up distributed training for fault tolerance, replication, and more
• Export models for portability

Topic 8 Machine Learning Operations (MLOps)

• Core technologies required to support effective MLOps.
• Adopt the best CI/CD practices in the context of ML systems.
• Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
• Implement reliable and repeatable training and inference workflows.
• ML Pipelines on Google Cloud

Final Assessment

• Written Assessment (SAQ)
• Practical Performance (PP)