Google Cloud ML Engineer
Empowering ML Engineers with Google Cloud
- Gain expertise in Google Cloud ML technologies
- Learn to build and deploy ML models
- Develop skills in data engineering and processing
- Enhance career prospects in ML engineering
- Prepare for Google Cloud ML Engineer certification
Start Date
23rd June 2025
Course Duration
360 Hrs
Course Overview
Master the end-to-end lifecycle of AI and Machine Learning solutions using Google Cloud. This program empowers learners with in-demand skills in data processing, model training, deployment, and MLOps, leveraging powerful tools like TensorFlow, Keras, Vertex AI, and BigQuery ML.
Key Features
- Comprehensive coverage of Google Cloud ML technologies
- Hands-on labs and projects
- Expert instruction and guidance
- Preparation for Google Cloud ML Engineer certification
- Access to Google Cloud resources and tools
- Focus on practical application and real-world scenarios
Skills Covered
- End-to-end AI/ML pipeline development using Vertex AI
- TensorFlow & Keras for data modeling and training
- Data preprocessing and feature engineering with BigQuery ML and Dataflow
- Hyperparameter tuning, model monitoring, and optimization
- MLOps implementation including CI/CD, version control, and distributed training
Next cohort starts on 23rd June
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Training Options
Associate
Foundation Track
- Master Python basics to OOP
- Learn HTML, CSS, and scripting
- Build logic with core Java skills
Batch starting from:
23rd June
₹20,000 ₹25,000
Enroll Now
Professional
Career Accelerator Track
(Everything in Associate+)
- Create responsive sites with Bootstrap & JS
- Develop backend with Spring Boot & JSP
- Learn basics of AI and machine learning
Batch starting from:
23rd June
₹25,000 ₹30,000
Enroll Now
Course Curriculum
Eligibility
- Basic programming knowledge (preferably Python)
- Familiarity with cloud computing concepts is helpful but not mandatory
- Suitable for graduates, professionals, and tech enthusiasts with an interest in AI/ML
Course Content
- Recognize the data-to-AI technologies and tools offered by Google Cloud.
- Use generative AI capabilities in applications.
- Choose between different options to develop an AI project on Google Cloud.
- Build ML models end-to-end by using Vertex AI.
- Design and build a TensorFlow input data pipeline.
- Use the tf.data library to manipulate data in 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.
- 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.
- 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
- Compare static versus dynamic training and inference
- Manage model dependencies
- Set up distributed training for fault tolerance, replication, and more
- Export models for portability
- Identify and use 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.
Course Certificate
Salary Scale
Maximum
35 LPA
Average
18 LPA
Minimum
8 LPA
Job Role
- Senior ML Engineer
- AI/ML Solutions Architect
- ML Ops Engineer
- Data Scientist (Cloud-based)
- Deep Learning Engineer
- Applied AI Researcher
Tools & Technologies
Why Join this Program
Industry-Aligned Curriculum
Designed with input from Google Cloud experts to match real-world job demands.
Mentorship & Doubt Support
1:1 sessions with professionals and dedicated guidance throughout.
Learn by working on real-world problems
Capstone projects involving real world data sets with virtual labs for hands-on learning
Career Support
Resume building, mock interviews, and placement assistance to land your dream ML role.
Google Cloud ML Engineer FAQs
This course is ideal for software developers, data scientists, AI/ML enthusiasts, IT professionals, and organizations seeking to upskill in cloud-based machine learning using Google Cloud tools.
No. While prior experience helps, this course is beginner-friendly and starts with foundational concepts. Basic programming knowledge (especially Python) is recommended.
You’ll work with cutting-edge tools such as Vertex AI, TensorFlow, Keras, BigQuery ML, Dataflow, Dataprep, tf.Transform, and Vertex Feature Store.
Absolutely. This certification equips you to:
Offer ML solutions as a freelancer
Build scalable AI products
Optimize workflows using GCP in startup environments
Absolutely. The program includes hands-on labs and real-world projects using Google Cloud tools to simulate industry-relevant AI/ML workflows.
Yes. The course modules closely follow Google Cloud’s official learning tracks and cover practical applications to help learners transition into certification and real-world implementation.
Yes. Basic to intermediate knowledge of Python is essential, especially for writing ML models, data pipelines, and using SDKs.