Cloud AI & TensorFlow on GCP Training in Chennai-Edubrights

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Cloud AI & TensorFlow on GCP Course in Chennai

Master cloud-based Artificial Intelligence and machine learning with Edubrights' Cloud AI & TensorFlow on GCP Course in Chennai. Designed for students, freshers, job seekers, and working professionals, this training program helps you develop practical skills in building, training, and deploying AI models using TensorFlow and Google Cloud Platform through hands-on learning and real-world projects.

Learn how organizations leverage Cloud AI and TensorFlow to develop intelligent applications, automate business processes, analyze large datasets, and create scalable machine learning solutions. Through practical assignments, industry-focused case studies, and project-based training, you will gain the expertise needed to work with modern AI technologies in cloud environments.

Key Highlights:

✅ Real-Time Cloud AI & TensorFlow Projects and Industry Case Studies

✅ Hands-On Training in TensorFlow Model Development and Deployment

✅ Google Cloud AI Services and Machine Learning Workflows

✅ Practical Exposure to Deep Learning, Neural Networks, and AI Applications

✅ Cloud-Based Model Training, Optimization, and Performance Monitoring

✅ Certification Guidance and Career Development Support

✅ Resume Building, Mock Interviews, and Placement Assistance

✅ Flexible Online, Classroom, and Weekend Training Options

Start your Cloud AI and TensorFlow journey with Edubrights and gain the practical skills, project experience, and industry knowledge needed to become a job-ready AI and Machine Learning professional in today's cloud-driven technology landscape.

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₹16000

₹20000

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Case Studies and Projects

Hours of Training

20+

Placement Assurance

100%

Expert Support

Yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

Course Objectives

1.What exactly Cloud AI & TensorFlow on GCP training cover?

Cloud AI & TensorFlow training covers building, training, and deploying machine learning models on Google Cloud Platform using TensorFlow and Keras. It teaches data processing, model building, scaling with Vertex AI, and applying deep learning techniques.

2. What are the main objectives of the Cloud AI & TensorFlow on GCP course for your development skills?

The main objectives are to enhance your skills in designing robust ML pipelines, working with large datasets, creating deep learning models, and operationalizing AI solutions at scale on Google Cloud.

Migrate Local Models to Cloud Cluster

Learn to use TensorFlow Cloud APIs to seamlessly shift your local Python and Keras scripts into distributed cloud training environments without code rewrites

Master Vertex AI End-to-End Workflows

Gain structural expertise in the unified Vertex AI platform to manage datasets, orchestrate training pipelines, and track model experiments.

Popular Techniques Covered in This Course

1.Cloud computing principles

2.TensorFlow and Keras modeling

3.Data pipeline creation with tf.data

4.Distributed training with Vertex AI

5.Model evaluation and tuning

6.Deep learning techniques

7.Model deployment and serving

8.Machine learning system architecture

9.Data augmentation and scarcity handling

10.Cost management and optimization

Get Hands-on Knowledge about Real-Time Projects

Project 1

1.Build and deploy a deep neural network

Build and deploy a deep neural network for image classification using TensorFlow and Vertex AI.

Project 2

2.machine learning pipeline

Create a machine learning pipeline for structured data including feature engineering, model training, and deployment.

Project 3

3. Serverless Demand Forecasting with BigQuery ML

Develop a scalable time-series forecasting model directly inside GCP's cloud data warehouse using structured SQL syntax. You will clean operational business data, evaluate model metrics, and export the resulting intelligence layers into operational data streams.

Project 4

4. Automated Hyperparameter Optimization Matrix

Design a self-tuning training pipeline utilizing Vertex AI Vizier to automatically discover optimal learning rates, dropout factors, and batch sizes. Your script will spin up parallel cloud compute workers to minimize training costs while finding the highest-accuracy model architecture.

Project 5

5. Production MLOps Pipeline for Text Summarization

Architect a resilient, automated workflow using Vertex AI Pipelines to handle data ingestion, model retraining, validation checks, and versioned endpoint updates. You will wrap your TensorFlow Natural Language Processing code in a secure container setup to protect system dependencies.

Key Features

Edubrights offers Cloud AI & TensorFlow on GCP Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 500+ Batches

Certification Guidance

Curriculum

Module 1: Introduction to Machine Learning on Google Cloud

Overview of AI/ML concepts
GCP ML infrastructure

Module 2: Exploring and Preparing Data

Using tf.data to build input pipelines
Data transformation and feature engineering

Module 3: Building Models with TensorFlow and Keras Sequential and Functional API models Model subclassing and regularization

Module 4: Training and Evaluation at Scale

Using Vertex AI for distributed training
Model evaluation and tuning

Module 5: Deploying and Serving Models

Model deployment on Vertex AI
Online and batch prediction

Module 6: Architecting Production ML Systems

Model orchestration and pipelines
Monitoring and management

Module 7: Advanced Deep Learning Concepts

CNNs, RNNs, transfer learning
Handling data scarcity and augmentation

Module 8: Labs and Real-World Use Cases

Hands-on with TensorFlow APIs
Building end-to-end ML workflows

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain knowledge from experts with practical Cloud AI & TensorFlow on GCP project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture and cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.

Succeed Our Resourceful Certification

Our certification validates your practical knowledge in building and deploying scalable AI/ML models using TensorFlow on Google Cloud. It demonstrates your capabilities in modern machine learning operations and enhances your career prospects in AI development.

Course FAQs

Q1: What is Cloud AI & TensorFlow on GCP?

It's a training program for building machine learning models on Google Cloud using TensorFlow.

Q2: Who is this course for?

Data scientists, ML engineers, and AI developers.

Q3: Are programming skills required?

Basic Python knowledge is recommended.

Q4: What tools will I learn?

TensorFlow, Keras, Vertex AI, tf.data.

Q5: Can I train models at scale?

Yes, using Vertex AI's managed services.

Q6: Does the course cover deep learning?

Yes, with CNNs, RNNs, and transfer learning.

Q7: Is deployment included?

Yes, for online and batch predictions.

Q8: Are there hands-on labs?

Yes, practical labs are included.

Q9: Is this course suitable for beginners?

Some ML background helps but beginners can learn too.

Q10: What jobs can I get after certification?

Machine Learning Engineer, Data Scientist, AI Developer.

Q11: What is the main difference between local TensorFlow and cloud-based implementations?

Local training is constrained by your physical hardware's VRAM and storage limits. Cloud-based implementations allow you to scale horizontally across hundreds of machine nodes instantly, enabling you to train massively complex models on massive datasets.

Q12: Can a traditional system administrator or IT engineer transition into this role?

Yes. IT and cloud engineers often find the cloud infrastructure side of this course highly intuitive. We provide structured training on the underlying machine learning logic to help you confidently bridge your existing skills into an AI Engineer role.