Google Professional Machine Learning Engineer

Master Machine Learning Skills for Real-World Impact

Master the essential skills to thrive as a Machine Learning Engineer with this comprehensive course. Designed to prepare you for the Google Cloud Professional Machine Learning Engineer certification exam, this program equips you with the expertise to design, build, and deploy machine learning models that address real-world business challenges using Google Cloud technologies.

Gain a deep understanding of ML production workflows, best practices, and the role of this certification in advancing your career.

Get Enrolled into this course

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Google Professional Machine Learning Engineer

• Discover Google Cloud’s data-to-AI tools.
• Explore generative AI applications.
• Compare AI project development options.
• Build ML models with Vertex AI.

Lesson 2: Launching into Machine Learning

• Improve data quality and analysis.
• Train AutoML models with Vertex AI and BigQuery ML.
• Optimize models using loss functions and metrics.
• Create scalable training and test datasets.

Lesson 3: Building ML Models with Keras

• Design TensorFlow data pipelines.
• Manipulate large datasets with tf.data.
• Create models using Keras APIs.
• Train and deploy ML models with Vertex AI.

Lesson 4: Feature Engineering on Google Cloud

• Learn Vertex AI Feature Store basics.
• Engineer features with BigQuery ML, Keras, and TensorFlow.
• Preprocess features using Dataflow and Dataprep.
• Transform features with tf.Transform.

Lesson 5: Machine Learning in the Enterprise

• Explore enterprise data strategies.
• Choose between Vertex AutoML, BigQuery ML, or custom training.
• Optimize models with Vertex Vizier.
• Set up pipelines for predictions and monitoring.

Lesson 6: Production Machine Learning Systems

• Compare static and dynamic training.
• Manage dependencies and optimize training setups.
• Distribute training for scalability.
• Export models for portability.

Lesson 7: Introduction to MLOps

• Use core MLOps technologies.
• Apply CI/CD practices to ML systems.
• Configure reliable MLOps architectures.
• Develop repeatable workflows.

Lesson 8: ML Pipelines on Google Cloud

• Understand TFX pipeline components.
• Prototype pipelines with TFX Interactive Context.
• Train models with TensorFlow, PyTorch, and XGBoost.
• Perform continuous training with Composer and MLFlow.

Key Benefits: What You Will Gain

Upon completing the Artificial Intelligence courses at Netsmartz School, learners will gain the following benefits:

Official Course Completion Badge/Certificate

Earn a prestigious course completion badge or certificate, available on the Netsmartz School and Microsoft, highlighting your proficiency in AI

Industry-Recognized Skills

Master key concepts and gain hands-on experience in Artificial Intelligence, making you well-equipped for real-world applications

Career Advancement

Enhance your career prospects by showcasing your expertise in AI to potential employers and staying ahead in today's rapidly evolving tech landscape

Why Netsmartz School

Experts-led Classroom Trainings

Our programs are crafted with insights from top industry minds and educators, ensuring you gain skills that truly matter in the real world.

Hands-On Experience on Real-World Projects

Take on projects that mirror the real challenges of the industry, work with real data, and build practical skills that lead to job-ready expertise.

Insights from Industry Leaders

Gain valuable insights from leading practitioners who bring industry use cases, case studies, and proven strategies into your classroom

Guidance and Support from Mentors

Stay on track with 24/7 access to mentors and a community of peers, all ready to help you overcome any challenges along the way.

AI Courses

CyberSecurity Courses

Google Professional Machine Learning Engineer

Master Machine Learning Skills for Real-World Impact

Get Enrolled into this course

Google Professional Machine Learning Engineer

• Discover Google Cloud’s data-to-AI tools. • Explore generative AI applications. • Compare AI project development options. • Build ML models with Vertex AI.

• Improve data quality and analysis. • Train AutoML models with Vertex AI and BigQuery ML. • Optimize models using loss functions and metrics. • Create scalable training and test datasets.

• Design TensorFlow data pipelines. • Manipulate large datasets with tf.data. • Create models using Keras APIs. • Train and deploy ML models with Vertex AI.

• Learn Vertex AI Feature Store basics. • Engineer features with BigQuery ML, Keras, and TensorFlow. • Preprocess features using Dataflow and Dataprep. • Transform features with tf.Transform.

• Explore enterprise data strategies. • Choose between Vertex AutoML, BigQuery ML, or custom training. • Optimize models with Vertex Vizier. • Set up pipelines for predictions and monitoring.

• Compare static and dynamic training. • Manage dependencies and optimize training setups. • Distribute training for scalability. • Export models for portability.

• Use core MLOps technologies. • Apply CI/CD practices to ML systems. • Configure reliable MLOps architectures. • Develop repeatable workflows.

• Understand TFX pipeline components. • Prototype pipelines with TFX Interactive Context. • Train models with TensorFlow, PyTorch, and XGBoost. • Perform continuous training with Composer and MLFlow.

Key Benefits: What You Will Gain

Official Course Completion Badge/Certificate

Industry-Recognized Skills

Career Advancement

Why Netsmartz School

Experts-led Classroom Trainings

Hands-On Experience on Real-World Projects

Insights from Industry Leaders

Guidance and Support from Mentors

What Students Say

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• Discover Google Cloud’s data-to-AI tools.
• Explore generative AI applications.
• Compare AI project development options.
• Build ML models with Vertex AI.

• Improve data quality and analysis.
• Train AutoML models with Vertex AI and BigQuery ML.
• Optimize models using loss functions and metrics.
• Create scalable training and test datasets.

• Design TensorFlow data pipelines.
• Manipulate large datasets with tf.data.
• Create models using Keras APIs.
• Train and deploy ML models with Vertex AI.

• Learn Vertex AI Feature Store basics.
• Engineer features with BigQuery ML, Keras, and TensorFlow.
• Preprocess features using Dataflow and Dataprep.
• Transform features with tf.Transform.

• Explore enterprise data strategies.
• Choose between Vertex AutoML, BigQuery ML, or custom training.
• Optimize models with Vertex Vizier.
• Set up pipelines for predictions and monitoring.

• Compare static and dynamic training.
• Manage dependencies and optimize training setups.
• Distribute training for scalability.
• Export models for portability.

• Use core MLOps technologies.
• Apply CI/CD practices to ML systems.
• Configure reliable MLOps architectures.
• Develop repeatable workflows.

• Understand TFX pipeline components.
• Prototype pipelines with TFX Interactive Context.
• Train models with TensorFlow, PyTorch, and XGBoost.
• Perform continuous training with Composer and MLFlow.