Course Overview
The Google Cloud massive data and machine learning tools and services that support the data-to-AI lifecycle are introduced in this course. It looks at the procedures, difficulties, and advantages of using Vertex AI to develop a large data pipeline and machine learning models on Google Cloud.
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Audience Profile
- Data Analysts
- Data Engineers
- Data Scientists
- ML Engineers who are beginners with Google Cloud
Learning Objectives
- Identify the critical outcomes of big data and machine learning, as well as the data-to-AI lifecycle on Google Cloud.
- With Dataflow and Pub/Sub, create streaming pipelines.
- Examine big data at scale with BigQuery.
- Determine many possibilities for constructing machine learning applications on Google Cloud.
- Describe the main phases in a machine learning workflow using Vertex AI.
- Build a machine learning pipeline using AutoML.
Content Outline
This section offers an introduction to the Big Data and Machine Learning Fundamentals course and a summary of its objectives.
The infrastructure of Google Cloud is examined in this section. Several big data and machine learning technologies and services that assist the data-to-AI lifecycle on Google Cloud are introduced here.
Google Cloud's streaming data management solution is described in this section. The article looks at a complete pipeline, including data ingestion using Pub/Sub, data processing using Dataflow, and data visualization using Looker and Data Studio.
This session introduces learners to Google's fully-managed, serverless data warehouse, BigQuery. Additionally, it looks at BigQuery ML and the procedures and essential commands for creating unique machine learning models.
The four alternatives for creating machine learning models on Google Cloud are examined in this section. Vertex AI, Google's single platform for developing and overseeing the lifespan of ML projects, is also introduced.
This section focuses on the three crucial steps in Vertex AI's machine-learning workflow: model training, model preparation, and data preparation. With AutoML, students get the chance to experience creating a machine-learning model.
The course material is reviewed in this section, along with additional resources for further study.
FAQs
A: Big data refers to vast amounts of data that traditional storage methods cannot handle. Machine learning is the ability of computer systems to learn to make predictions from observations and data. Machine learning can use the information provided by extensive data study to generate valuable business insights.
A: Big data analytics using machine learning algorithms is a logical next step for businesses trying to optimize the potential value of their data. Machine learning tools examine data sets using data-driven algorithms and statistical models, then conclude found patterns or make predictions based on them.
A: Our services are fast, scalable, and easy to use. Major Google applications use Cloud machine learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply).
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are onboarded in the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts any time your respective faculty.
Our trainers have patience and the ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: You need working desktops or laptops with the necessary specifications and a strong internet connection to access the laboratories to participate in the training session.
A: To practice, get immediate answers to your questions, and get the most out of your investment, we always advise you to go to the live session. Radiant Techlearning will assist you with the recorded session from that day, but if, for any reason, you must miss the class. However, such recorded sessions are NOT intended for commercial use or distribution; they are also NOT designed for personal use.
A: Radiant Techlearning offers a data center with a virtual training environment for participant hands-on practice.
Participants can effortlessly access these laboratories over the cloud with a remote desktop connection.
You have the freedom to learn from anywhere in the globe and in any time zone, thanks to radiant virtual labs.
A: The learners will be enthralled as we engage them the real-world and industry Oriented projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you greatly in future tasks and assignments.