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GCP Machine Learning Engineer Certification with an Extra Boost Course.

Empower Your Cloud Journey with GCP Excellence.

Dynamic Online Learning with Virejetech Certified Instructors.
Interactive Skill-building and Educational Assistance
Interview Readiness Guide + Mock Sessions
Placement Assistance Guide

Holistic Learning Pathway

"Advance your career with our holistic App Development Program, meticulously designed to cultivate the skills of a modern app developer. This sought-after course covers key modules, including App Development Foundations and Techniques, providing extensive exposure to diverse domains. Empower yourself with cutting-edge tools for Visualization and Insights, ensuring a well-rounded skill set in the dynamic field of app development."

25 hrs

Learning content

Google Cloud Platform Machine Learning Engineer

1.Introduction to Machine Learning

2 Quizzes

1 Project

1.1What is machine learning?.
Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn from data and improve their performance
1.2Types of machine learning (supervised, unsupervised,reinforcement learning)
Supervised learning involves labeled data for training predictive models, unsupervised learning deals with unlabeled data to discover patterns, and reinforcement learning focuses on agents learning optimal actions through trial and error in interactive environments.
1.3Machine learning workflow
Cloud DevOps accelerates development cycles, enhances collaboration, and optimizes resource utilization through automation, scalability, and continuous integration on cloud platforms.
1.4Data preprocessing and feature engineering
Data preprocessing involves cleaning and transforming raw data to make it suitable for machine learning, while feature engineering focuses on creating relevant input variables (features) to enhance model performance

2.Python for Machine Learning

v2.1 Python basics for ML engineers
Python basics for ML engineers include mastering fundamental concepts such as variables, data types, control structures (if statements, loops), functions, and libraries like NumPy, pandas
2.2 Popular libraries
Python basics for ML engineers include mastering fundamental concepts such as variables, data types, control structures (if statements, loops), functions, and libraries like NumPy, pandas
2.3 Jupyter Notebooks for experimentation
Jupyter Notebooks are interactive, web-based environments allowing ML engineers to conduct experimentation and analysis seamlessly by combining code, visualizations, and documentation in a single platform, fostering iterative development and collaborative exploration in machine learning projects.

3.Data Collection and Cleaning

3.1 Data sources and acquisition.
Git is a distributed version control system that enables collaborative software development, while GitHub and GitLab are web-based platforms facilitating Git repository hosting, collaboration, and project management.
3.2 Data cleaning and preprocessing
GCP Repos is a version control service in GCP DevOps that provides Git repositories for source code management. It allows teams to collaborate on software development by hosting and versioning code
3.3 Handling missing data
Google Cloud Storage services provide scalable and secure cloud-based storage solutions, including object storage, file storage, and specialized options for mobile applications and archival needs.
3.4 Data visualization for analysis
Data visualization for analysis involves using graphical representations like charts and graphs to gain insights, identify patterns, and communicate trends within datasets, facilitating a more intuitive understanding of the data's characteristics and aiding .

4.Model Building

4.1 Supervised learning algorithms
Supervised learning algorithms are models that learn from labeled training data to make predictions or classifications on new, unseen data
4.2 Unsupervised learning algorithms
Unsupervised learning algorithms operate on unlabeled data to discover inherent patterns, structures, or relationships. Examples include K-means clustering for grouping similar data points
4.3 Neural networks and deep learning
Exercise: Scaling Git for Enterprise DevOps involves hands-on activities to address challenges associated with large-scale Git repositories, multiple teams, and complex development workflows
4.4 Model evaluation and selection
Model evaluation and selection encompass the process of assessing machine learning models' performance using metrics like accuracy, precision, recall, and F1 score, among others,

5.Cloud Computing Fundamentals

5.1 Introduction to cloud computing platforms
Google Cloud Storage offers various options tailored to diverse storage needs, including Standard Storage for frequently accessed data
5.2 Setting up cloud accounts and access management.
Implementing and managing YAML pipelines in Azure DevOps involves defining build and release pipelines using YAML syntax.
5.3 Overview of cloud services (EC2, S3, GCP, Azure ML, etc.)
Implementing and managing GitHub Actions involves defining workflows using YAML files directly within a GitHub repository. GitHub Actions automate various tasks, including building

6.Deploying ML Models on Cloud

6.1 Containerization (Docker)
Introduction to CI/CD (Continuous Integration/Continuous Delivery) involves adopting practices that automate and streamline the software development lifecycle
6.2 Kubernetes orchestration
Implementing CI/CD pipelines involves creating automated workflows that integrate and deliver code changes seamlessly from development to production.
6.3 Model serving with cloud services (AWS SageMaker, GoogleAI Platform, Azure ML)
Managing application configuration and secrets in CI/CD involves securely handling sensitive information, such as API keys or connection strings, during the deployment process
6.4 Serverless computing for model deployment
Implementing CI/CD pipelines involves creating automated workflows that integrate and deliver code changes seamlessly from development to production.

7.Scaling and Optimization

7.1 AutoML tools and techniques
Infrastructure as Code (IaC) principles involve treating infrastructure configurations as code, emphasizing declarative syntax, idempotency, version control, reusability, automatione
7.2 Hyperparameter tuning
Implementing ARM (Azure Resource Manager) templates involves using JSON-based scripts to define and deploy Azure infrastructure resources. ARM templates provide a declarative way to specify the desired state of resources
7.3 Model optimization and performance improvement.
Infrastructure provisioning with Terraform involves using HashiCorp's Terraform tool to define and manage infrastructure as code
7.4 Monitoring and debugging in production.
Implementing ARM (Azure Resource Manager) templates involves using JSON-based scripts to define and deploy Azure infrastructure resources. ARM templates provide a declarative way to specify the desired state of resources

8.Data Pipelines and ETL

9.1 Building data pipelines on cloud platforms
Configuration management automates and maintains consistent infrastructure and software configurations, ensuring reliability, version control, and idempotent application of desired states.
9.2 Extract, Transform, Load (ETL) processes
Azure Key Vault is a cloud service that enables secure storage and management of sensitive information such as secrets, encryption keys, and certificates.
9.4 Data storage and warehousing
Exercise: Managing application configuration and secrets involves practical tasks to secure and handle sensitive information within an application using tools like Azure Key Vault

10.Big Data and ML

10.1 Integration of big data tools (Hadoop, Spark) with machinelearning
To configure database authentication using platform and database tools, utilize tools such as Azure Portal, SQL Server Management Studio (SSMS), or Azure Data Studio to manage authentication settings
10.2 Distributed computing for large-scale data processing
To configure database authorization using platform and database tools, leverage tools like Azure Portal, SQL Server Management Studio (SSMS), or Azure Data Studio to manage permissions
10.3 Training models on large datasets
To implement Azure AD-based security, integrate Azure Active Directory (Azure AD) with your database to enable authentication using Azure AD credentials, ensuring centralized identity management

11.Advanced Topics

11.1 Reinforcement learning and its applications
To monitor and troubleshoot SQL Server performance, employ tools like SQL Server Management Studio (SSMS), Performance Monitor, and Dynamic Management Views (DMVs) to analyze query execution
11.2 Natural language processing (NLP) for ML engineers
To monitor and troubleshoot Azure SQL Database performance, use Azure Portal, Azure Monitor, and Query Performance Insight. Utilize metrics, performance recommendations
11.3 Computer vision and image recognition
To monitor and troubleshoot SQL Server performance, employ tools like SQL Server Management Studio (SSMS), Performance Monitor, and Dynamic Management Views (DMVs) to analyze query execution
11.4 Time series forecasting
To monitor and troubleshoot Azure SQL Database performance, use Azure Portal, Azure Monitor, and Query Performance Insight. Utilize metrics, performance recommendations

12.Ethical and Responsible AI

12.1 Bias and fairness in machine learning
Reviewing query plans involves analyzing the execution plans generated by the database engine to understand how queries are processed
12.2 Privacy and data security considerations
To monitor and troubleshoot Azure SQL Database performance, use Azure Portal, Azure Monitor, and Query Performance Insight. Utilize metrics, performance recommendations

13.Capstone Project

13.1 Apply the knowledge and skills learned throughout the course toa real-world project
Configuration management automates and maintains consistent infrastructure and software configurations, ensuring reliability, version control, and idempotent application of desired states.
13.2 Develop, deploy, and optimize a machine learning model on acloud platform
Azure Key Vault is a cloud service that enables secure storage and management of sensitive information such as secrets, encryption keys, and certificates.

13.Scalability and Load Balancing

13.1 Auto-scaling Applications
To configure database authentication using platform and database tools, utilize tools such as Azure Portal, SQL Server Management Studio (SSMS), or Azure Data Studio to manage authentication settings
13.2 Load Balancing Strategies
To configure database authorization using platform and database tools, leverage tools like Azure Portal, SQL Server Management Studio (SSMS), or Azure Data Studio to manage permissions

14.Collaboration and Communication Toolst

14.1 Communication and Documentation Tools
To monitor and troubleshoot SQL Server performance, employ tools like SQL Server Management Studio (SSMS), Performance Monitor, and Dynamic Management Views (DMVs) to analyze query execution
14.2 Agile and Scrum Principles
To monitor and troubleshoot Azure SQL Database performance, use Azure Portal, Azure Monitor, and Query Performance Insight. Utilize metrics, performance recommendations

15.DevOps Culture and Best Practices