GCP Fundamentals Big Data & Machine Learning
Duration: 1 Day (8 Hours)
GCP Fundamentals Big Data & Machine Learning Course Overview:
In this one-day GCP Fundamentals Big Data & Machine Learning class, participants will be introduced to the big data capabilities of Google Cloud Platform. Through a combination of demos, presentations, and hands-on labs, attendees will gain an overview of the platform as well as a detailed understanding of data processing and machine learning capabilities. The training highlights the flexibility, ease, and power of big data solutions available on Google Cloud Platform.
Intended Audience:
- Data scientists, Data analysts, Business analysts who are getting started with Google Cloud Platform.
- Individuals who are responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
- IT decision makers and executives evaluating Google Cloud Platform to be used by data scientists.
Course Objectives:
- Find the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
- Effectively use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
- Employ ML APIs
- Train and use a neural network using TensorFlow.
- Employ BigQuery and Cloud Datalab to bring out interactive data analysis.
- Choose between completely different data processing products on the Google Cloud Platform.
1. Introducing Google Cloud Platform
- Google Platform Fundamentals Overview.
- Google Cloud Platform Big Data Products.
2. Compute and Storage Fundamentals
- CPUs on demand (Compute Engine).
- A global filesystem (Cloud Storage).
- CloudShell.
- Lab: Set up an Ingest-Transform-Publish data processing pipeline.
3. Data Analytics on the Cloud
- Stepping-stones to the cloud.
- Cloud SQL: your SQL database on the cloud.
- Lab: Importing data into CloudSQL and running queries.
- Spark on Dataproc.
- Lab: Machine Learning Recommendations with Spark on Dataproc.
4. Scaling Data Analysis
- Fast random access.
- Datalab.
- BigQuery.
- Lab: Build machine learning dataset.
5. Machine Learning
- Machine Learning with TensorFlow.
- Lab: Carry out ML with TensorFlow
- Pre-built models for common needs.
- Lab: Employ ML APIs.
6. Data Processing Architectures
- Message-oriented architectures with Pub/Sub.
- Creating pipelines with Dataflow.
- Reference architecture for real-time and batch data processing.
7. Summary
- Why GCP?
- Where to go from here
- Additional Resources
GCP Fundamentals Big Data & Machine Learning Course Prerequisites:
- Basic knowledge with common query language such as SQL.
- Developing applications using a common programming language such Python.
- Experience with data modeling, extract, transform, load activities.
- Experience with machine learning and/or statistics.
Q: What is the “GCP Fundamentals Big Data & Machine Learning” course?
A: The “GCP Fundamentals Big Data & Machine Learning” course is a comprehensive training program that introduces participants to the fundamental concepts and tools for working with big data and implementing machine learning solutions on Google Cloud Platform (GCP). The course covers key GCP services and technologies used for big data processing and machine learning tasks.
Q: Who is this course suitable for?
A: This course is suitable for data analysts, data engineers, data scientists, and individuals interested in leveraging big data and machine learning on Google Cloud Platform. It is designed for both technical and non-technical professionals who want to gain a foundational understanding of big data processing and machine learning concepts on GCP.
Q: What topics are covered in this course?
A: The “GCP Fundamentals Big Data & Machine Learning” course covers a range of topics, including an introduction to big data and machine learning, overview of GCP services for big data processing, data ingestion and storage with Google Cloud Storage and BigQuery, data processing with Google Cloud Dataproc and Dataflow, and implementing machine learning models with Google Cloud ML Engine.
Q: Are there any prerequisites for this course?
A:
- Basic knowledge with common query language such as SQL.
- Developing applications using a common programming language such Python.
- Experience with data modeling, extract, transform, load activities.
- Experience with machine learning and/or statistics.
Q: What are the key skills and knowledge gained from this course?
A: By completing this course, participants will gain a solid understanding of big data processing and machine learning concepts on Google Cloud Platform. They will learn how to ingest and store data using Google Cloud Storage and BigQuery, process data with Google Cloud Dataproc and Dataflow, and implement machine learning models with Google Cloud ML Engine.
Discover the perfect fit for your learning journey
Choose Learning Modality
Live Online
- Convenience
- Cost-effective
- Self-paced learning
- Scalability
Classroom
- Interaction and collaboration
- Networking opportunities
- Real-time feedback
- Personal attention
Onsite
- Familiar environment
- Confidentiality
- Team building
- Immediate application
Training Exclusives
This course comes with following benefits:
- Practice Labs.
- Get Trained by Certified Trainers.
- Access to the recordings of your class sessions for 90 days.
- Digital courseware
- Experience 24*7 learner support.
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