Course Overview
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write training models with custom estimators.
This is the second Advanced Machine Learning on Google Cloud series course. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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Prerequisites
Basic SQL, familiarity with Python and TensorFlow
Audience Profile
- Data Engineers and programmers who want to learn how to apply machine learning in practice.
- Is anyone interested in learning how to leverage machine learning in their enterprise?
Learning Objectives
- Compare static versus dynamic training and inference
- Manage model dependencies
- Set up distributed training for fault tolerance, replication, and more
- Export models for portability
Content Outline
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
This module explores what else a production ML system needs to do and how to meet those needs. You review how important, high-level design decisions around training and model serving need to be made to get the right performance profile for your model.
In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
In this module, you identify performance considerations for machine learning models.
Machine learning models are not all identical. For some models, you focus on improving I/O performance; for others, you focus on squeezing more computational speed.
Understand the tools and systems available and when to leverage hybrid machine learning models.
PDF links to all modules
FAQs
A: The Machine Learning Engineering for Production (MLOps) Specialization covers conceptualizing, building, and maintaining integrated systems that continuously operate in production. Contrary to standard machine learning modeling, production systems need to handle constantly evolving data.
A: A cognitive architecture called a "production system" is used to construct search algorithms and simulate human problem-solving skills. This knowledge on how to solve problems is kept in the system as products, which are tiny quanta. It consists of two components: rules and actions.
A: Machine learning is a sub-field of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
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 at any time from your respective faculty.
Our trainers have the patience and 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: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We would always recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participants' hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
A: The learners will be enthralled as we engage them in the natural 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 a lot in your future tasks and assignments.