AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition: The ultimate guide to passing the MLS-C01 exam on your first attempt

I’m just starting to get into AI/ML and I’ve already been using AWS for a while. I decided to check out this book, even though I have no plans to take the test, but just to get an idea of AI/ML on AWS. I was blown away by the content of this book. It gets real in depth of how AI/ML works on AWS and is perfect for me as I dont’ always have so much time to go through Amazon’s vast documentation to find the services I need.I highly recommend those who plan to either take the test or use AI/ML on AWS to check this book out.

I am pursuing a master’s level certificate in machine learning and recently acquired this book to complement my studies. I find the book remarkably impressive as it aligns closely with the content covered in my classes. In hindsight, I wish I had discovered it earlier, as it not only effectively delves into various topics but also simplifies them better than my professor does. Additionally, the book provides comprehensive guidance on implementing these models within the AWS environment, highlighting the additional models available with AWS. Overall, for anyone interested in delving into machine learning, this book proves to be an invaluable resource.

This book covers the four essential topics (data engineering, EDA, modeling, and deployment) in appropriate detail. This book is suitable for experienced ML practitioners since the authors go into great detail on each topic while avoiding jargon, making it accessible to novice practitioners. Apart from this, the authors also make it a point to familiarize users with the questions expected in the certification exam.

With cloud technology being so focused on machine learning, I decided to start putting some focus on learning more about the topic. I ran across this book and wanted to see if it would be helpful with enhancing my knowledge of machine learning. I am new to this area of technology, so I was eager to see what I could learn from a topic that to me, seems extremely intimidating.I found this book to be informative, to say the least. I appreciated the book commencing with a fundamentals breakdown of concepts that I wasn’t aware of. For example, the author, Nanda, explains the difference between machine learning (ML), deep learning (DL), and artificial intelligence (AI), as well as the difference between supervised, reinforcement, and unsupervised learning, which I found to be helpful. As I read through the chapters, I gained insight into how AWS services are used with machine learning. The chapters were thorough, and the author pointed out useful tips on using AWS services when implementing your machine learning projects. I also appreciated the chapter where he provides thorough explanations on choosing which model metrics you want to use when building your machine learning projects. I was also happy to see the book provide an analysis of the AWS ML/AI applications commonly tested in the AWS Machine Learning specialty certification.The end of the book provides a nice overview for different deployment options for your ML applications using AWS services. The steps provided will give anyone the confidence to try different deployment options. The steps are straightforward, and the author also put a strong emphasis with focusing on being mindful of costs, setting up your permissions correctly, and securing your applications. He stepped through all of this competently, and I feel this will be helpful with me when I sit for the Machine Learning specialty certification, because AWS always tests with these three objectives in mind for every test question.Other gems I found to be helpful in the book include:• “Tips” and “Important Notes” dotted throughout the book to help emphasize concepts that we need to remember• Summaries at the end of each chapter giving a quick overview of the important takeaways for each chapter• Exam Readiness Drills at the end of each chapter to help reinforce the skills emphasized for each chapter• Helpful QR codes to scan once logged in to the Packt website leading to the chapter review questions, which I like, because I prefer to do review questions in an online format, personally• Once logged into the Packt site, you can also access the online resources for the book as well, including the flashcards, which are helpful for memorizing concepts from the book, as well as exam tips for the AWS ML specialty certification• Pictures in the book are in color, which help to replicate the drillsOverall, I appreciated the depth of knowledge, laid out thoroughly by the author. I do plan to continue re-reviewing this book because I feel confident it will prepare me well for the exam. I suggest if you’re looking for a book that will help you be successful with not only passing the exam, but also to help increase your knowledge and competency working with ML applications, then this book will be extremely helpful with achieving those goals.

This book provides an overview of machine learning concepts tailored for experienced ML practitioners, aligning with the expectations of the MLS-C01 exam. While it does not delve into exhaustive details of ML, this approach is suitable given the exam’s target audience.The book includes complete commands for utilizing AWS services, which, although useful for practical applications, might detract from the primary goal of preparing for an exam focused on conceptual understanding. Users might find these commands beneficial for real-world AWS applications, despite them being somewhat peripheral to the exam’s conceptual focus.Structurally, the book resembles a collection of blog posts with numerous tutorials on using AWS, which contributes to its practical utility, though this format might differ from traditional academic resources.Overall, the book serves as a useful resource for those looking to apply AWS services in practice and for experienced ML practitioners preparing for the MLS-C01 exam, though it may not cater as well to beginners needing a more thorough conceptual grounding in ML.(revised by chatgpt)

This comprehensive guide on machine learning frameworks and AWS services is a practical resource for anyone looking to deepen their understanding of building and deploying ML solutions. The book adeptly covers the identification of ML frameworks for specific tasks and the application of the CRISP-DM methodology to construct robust ML pipelines. Readers will find detailed explanations on integrating various AWS services to enhance AI/ML projects. The text is particularly strong in presenting data transformation techniques, including one-hot encoding, binary encoding, ordinal encoding, binning, and text transformations. Visualization strategies for data relationships and distributions are well-explained, providing valuable insights for effective data analysis. The sections on using AWS for both batch and real-time data processing are clear and informative. Furthermore, the book excels in detailing the creation and management of training and inference pipelines using SageMaker, along with efficient strategies for deploying ML models in production environments. This is an essential read for practitioners eager to leverage AWS in their machine learning workflows.

If you are someone looking to crack the AWS Certified Machine Learning Speciality exam, you have the most complete book at your hand in this copy. The authors did a fantastic job at building up a solid foundations of machine learning and AWS Services required before diving deep into the world of Machine learning using AWS services. It is useful for both Machine Learning practitioners as well as other engineers who are new to the field of machine learning.As a data scientist or a machine learning partitioner, you can skip the beginning of the chapters and head directly to the application content but I recommend reading it end to end. The authors have provided code used for examples via Github links to follow along.It has coverage of all areas of machine learning from Storage to building datasets to inference. It can be used as a machine learning reference book even if you do not wish to take the exam.