Google Cloud Platform Big Data & Machine Learning Fundamentals Training
A fundamental overview of Google Cloud’s big data and machine learning tools is given via this program. It teaches learners how to design, develop, and implement machine learning models and data processing systems on Google Cloud by covering key services including BigQuery, Dataflow, Dataproc, and TensorFlow.
- 10+ Courses
- 30+ Projects
- 400 Hours
Google Cloud Platform Big Data & Machine Learning Fundamentals Training is suitable for the following target audiences:
Data engineers: Data engineers are professionals who wish to manage data processing chores in the cloud and provide scalable data solutions.
Data scientists: Data scientists want to create and train models using big datasets by utilizing cloud-based machine learning tools.
IT specialists: IT specialists, also known as cloud architects, are in charge of creating the platforms and infrastructure needed to enable big data and machine learning applications.
Professionals in business intelligence: BI managers and analysts who want to leverage Google Cloud’s data tools for more in-depth analysis and insights.
Developers: Programmers who want to use Google Cloud resources to incorporate machine learning skills into apps.
Data Engineer: Data engineers use Google Cloud products like BigQuery and Dataflow to design, implement, and manage data pipelines.
Machine Learning Engineer: Using TensorFlow and Vertex AI, create and implement machine learning models on Google Cloud.
Cloud Solutions Architect: Creating Google Cloud architectures for machine learning and big data solutions.
Data Scientist: Analyze and create models using Google Cloud’s machine learning technologies.
Business Intelligence Analyst: Using Google Cloud’s significant data capabilities to extract useful information and inform decisions.
These positions are highly sought after in the USA and Canada in sectors like technology, banking, healthcare, and e-commerce. Professionals with experience in Google Cloud’s big data and machine learning services should anticipate competitive pay and prospects for career progression, as cloud capabilities are essential.
- A look at the basics of cloud computing
- An introduction to the Google Cloud ecosystem
- A look at the services offered by Google Cloud Platform
- Creating a GCP account and project
- Getting the basics of cloud architecture and billing down
- An introduction to cloud storage options
- Using Google Cloud Storage
- Using Cloud SQL to structure data
- Cloud Bigtable is a NoSQL database.
- Best practices for data management and security
- Getting started with cloud-based big data analytics
- Using Big Query for data warehousing
- Cloud Datapost for processing data
- ETL pipelines and changing data
- Methods for optimizing queries
- Getting data into Cloud Pub/Sub
- Ideas for data streaming
- Processing data in batches vs. in real time
- Using Cloud Dataflow to make data pipelines
- Keeping an eye on and controlling data workflows
- A look at the ideas behind machine learning
- The process and life cycle of machine learning
- Using Vertex AI to train models
- Getting data ready for machine learning
- Ways to test models
- Models for supervised and unsupervised learning
- Using TensorFlow on GCP to train models
- Methods for feature engineering
- Tuning hyperparameters
- Basic steps for deploying a model
- AI services that you can use in Google Cloud Platform
- Processing language and analysing images
- Using Looker to show data
- Business intelligence apps
- Examples of machine learning in the real world
- A big data and machine learning project from start to finish
- Making a data pipeline on Google Cloud Platform
- Putting a machine learning model into use
- Help with writing a resume and getting certified
- Presentation of the final project
It is a class that teaches how to use Google Cloud Platform tools to work with big data and machine learning.
Students, data engineers, analysts, and IT professionals interested in learning more about cloud-based analytics should enrol in this class.
No, beginners can start learning the basics of Google Cloud Platform.
BigQuery, Cloud Dataflow, Cloud Dataproc, and Vertex AI are some of the tools.
Yes, students do hands-on labs and projects that are similar to what they would do in the real world, including data analysis, machine learning model development, and cloud resource management.
This course covers data processing in the cloud, building machine learning models, and doing analytics.
Data Analyst, Cloud Architect, Cloud Data Engineer, and Machine Learning Engineer.
Yes, many people use Google Cloud Platform for machine learning and processing big amounts of data.
The program is set up as an 8-week training course.
Yes, it helps you get ready for the Google Cloud Professional Data Engineer Certification and other similar certifications.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
You can register via our website https://checkmateittech.com/, or reach out to our support teams via phone, email, or WhatsApp. We’ll help you with batch schedules and payment options.
Email info@checkmateittech. Call Us +1-347-408-2054
- Submit Form
Job opportunities in USA and Canada
Data Engineer: Data engineers use Google Cloud products like BigQuery and Dataflow to design, implement, and manage data pipelines.
Machine Learning Engineer: Using TensorFlow and Vertex AI, create and implement machine learning models on Google Cloud.
Cloud Solutions Architect: Creating Google Cloud architectures for machine learning and big data solutions.
Data Scientist: Analyze and create models using Google Cloud’s machine learning technologies.
Business Intelligence Analyst: Using Google Cloud’s significant data capabilities to extract useful information and inform decisions.
These positions are highly sought after in the USA and Canada in sectors like technology, banking, healthcare, and e-commerce. Professionals with experience in Google Cloud’s big data and machine learning services should anticipate competitive pay and prospects for career progression, as cloud capabilities are essential.
