Google Professional Machine Learning Engineer

Course Overview The Google Professional Machine Learning Engineer course by Stalwart Learning is tailored for professionals aiming to design, build, and deploy scalable machine learning models on Google Cloud. This…

Created by

Stalwart Learning

Category

Date & Time

Price

Duration

5 Days / 40hrs

Location

https://stalwartlearning.com/

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Course Description

The Google Professional Machine Learning Engineer course by Stalwart Learning is tailored for professionals aiming to design, build, and deploy scalable machine learning models on Google Cloud. This course delves into advanced ML concepts, including data preprocessing, feature engineering, model training, deployment, and monitoring. Participants will explore tools like Vertex AI, TensorFlow, and BigQuery ML while gaining hands-on experience. The course also prepares learners for the Google Professional Machine Learning Engineer certification exam, equipping them to succeed in high-impact machine learning roles.

40 hours (5 days)

  • Proficiency in Python and familiarity with machine learning frameworks such as TensorFlow.
  • Basic understanding of Google Cloud services and AI workflows.
  • Knowledge of data analysis and statistics is recommended.
  • Overview of machine learning concepts and workflows
  • Introduction to Google Cloud’s AI and ML ecosystem
  • Key tools: Vertex AI, BigQuery ML, and TensorFlow
  • Preprocessing data for machine learning models
  • Performing feature selection and extraction
  • Managing datasets using Cloud Storage and BigQuery
  • Designing models with TensorFlow and Keras
  • Training models at scale using AI Platform and Vertex AI
  • Exploring hyperparameter tuning and distributed training
  • Deploying models using Vertex AI Model Serving
  • Implementing APIs for real-time predictions
  • Monitoring model performance and managing drift
  • Evaluating models using metrics like accuracy, precision, and recall
  • Using Explainable AI for transparency and bias mitigation
  • Optimizing models for performance and cost efficiency
  • Implementing custom training jobs and pipelines
  • Working with AutoML for automated model building
  • Exploring transfer learning and pre-trained models
  • Ensuring data security with IAM and encryption techniques
  • Managing model governance and compliance standards
  • Addressing ethical considerations in AI development
  • Certification exam structure, domains, and objectives
  • Hands-on labs and real-world case studies
  • Mock exams and tips for exam readiness

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