Course Details
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
Course Info
Promotion Code
Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
TBD
Hardware: Window or Mac Laptops
Job Roles
- Data Scientist
- Machine Learning Engineer
- AI Engineer
- Data Analyst
- Software Engineer
- Cloud Solutions Architect
- Research Scientist
- Application Developer
- Big Data Engineer
- Business Intelligence Developer
- Robotics Engineer
- Quantitative Analyst
- Systems Analyst
- Product Manager
- Technical Program Manager
Trainers
Ajay B : Ajay is a ACLP certied trainer. Ajay is a vendor neutral cloud consultant and training expert on Cloud , with several Private cloud deployments in India and cloud migration knowledge .He is a Cloud and DevOps enthusiast with consulting, deployment and training expertise on OpenStack, AWS, Google Cloud ,Azure, Jenkins, and Docker
Ajay has 18 + years Industry experience as IT entrepreneur and 9 years in Cloud and Devops technical consulting, implementation and training area, currently working in capacity of Vice President – Cloud and Devops services handling singapore and India
Anil Bidari: Anil is a ACLP certified trainer. He is an Enterprise Cloud and DevOps Consultant , responsible for helping clients to move Virtual data centre to Private Cloud based on OpenStack and Public Cloud ( AWS, Azure and Google cloud) . Consulting and training experience on Devops tool chain like github , Jenkins, Sonarqube, Docker & kubernetes, Cloud foundry, Openshift, Ansible and SaltStack. Lot of my Role is involved design and implementation of a solution and training
Customer Reviews (2)
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment