Michael and Andreas are the authors of the Azure Data Scientist Associate Certification Guide title. We got the chance to have a chat with them and find out more about their experience of writing with Packt.
Q: What are your specialist tech area(s)?
Authors: We are both enjoying architecting robust solutions that fit our customers’ needs. Currently, we are focusing on the Data platform and Artificial Intelligent areas, always keeping the end-to-end solution in mind. Some of the tech areas where we are considered specialists are the following:
Advanced Analytics Architectures and Implementations / Machine Learning Architectures and Implementations / Data Overall Architectures and Implementation / Enterprise Integrations Architectures and Implementation.
Q: How did you both become authors for Packt? Tell us about your journey. What was your motivation for writing this book?
Authors: Michael was reviewing a book on big data for Packt when asked if he was interested in writing a book around Azure Machine Learning. Michael reached out to Andreas to check if he would be interested in writing the book together, knowing that it would have been too much effort for Michael to write the whole book by himself. Once Andreas accepted the challenge, we both signed up with Packt. We have always enjoyed teaching, sharing knowledge, and helping people become successful with technology. This passion was the main driver for committing to the book. We have been assisting customers in ramping up and running their data science projects in Azure for a long time. This book is an excellent opportunity to share our experiences with anyone in the world. Writing the book was a great learning experience. It requires a lot of time, which is hard to find when you have a family and a full-time job. We initially estimated that we would write two pages per day, something that was not feasible. The bulk volume was written during weekends and holidays. Even then, we had to write during the night after the family was asleep. To be fair, Andreas wrote most of the book’s chapters, and if we ever write a book again, Michael will have to take a sabbatical to match Andreas’s late-night authoring habit.
Q: What kind of research did you both do, and how long did you spend researching before beginning the book?
Authors: Both of us are working on Azure, and we are learning new things daily. While writing this book, we had to deep dive into Azure Machine Learning (AzureML) components that were still in preview and will potentially become generally available after the book is published. Moreover, we had to develop easy-to-understand examples to help the readers grasp key Machine Learning concepts and how they apply within AzureML. That was an iterative process with a lot of coding and testing before writing the actual chapter.
Q: Did you face any challenges during the writing process? How did you overcome them?
Authors: Azure Machine Learning (AzureML) services is a rapidly evolving product within Azure. New features appear every week. Writing a book about such an agile product is very tricky, especially if you want to have all figures accurately represent the current state of the product. To address this constant evolution of the product, we decided to invest some pages explaining the fundamental concepts behind the platform. These concepts will allow readers to adapt their knowledge to newer product features quickly. Moreover, we will use the book’s GitHub repository to provide regular updates to the readers.
Q: What are both of your takes on the technologies discussed in the book? Where do you see these technologies heading in the future?
Authors: Azure Machine Learning (AzureML) services is a platform that allows all types of users to work with Machine Learning in Azure. It provides no-code features that enable power users to create Machine Learning models with only a couple of mouse clicks. AzureML offers a rich SDK for the more advanced users to programmatically control the end-to-end data science process (collecting, preparing, using, modeling, and managing a model’s lifecycle). On the other hand, AzureML is very flexible, allowing you to keep your existing tooling and benefit from the cloud scalability it offers without rewriting anything. We strongly believe that AzureML is already a great platform to perform Machine Learning experiments on a cloud-scale. It will keep evolving, providing features and capabilities for all types of users.
Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Authors: When we started writing this book, there were no books related to the DP-100 exam. Even now, there are only a few exam questions answering books. Our approach is different. Instead of giving you the exam questions, we explain how Azure Machine Learning is working and how to make use of it in your day-to-day job. Through that process, you will learn all the skills needed to be an Azure Data Scientist, which you can verify when you pass the DP-100 exam.
Q. What are the key takeaways you want readers to come away from the book with?
Authors: The DP-100 exam is a mix of infrastructure and data science questions. For us, there are four key takeaways you will have to learn to pass the DP-100 exam with flying colors:
1. How to manage the Azure resources within the AzureML services.
2. How to run experiments and train models through the designer and the AzureML SDK.
3. How to deploy and operationalize machine learning models.
4. How to implement responsible machine learning solutions.
Q. What advice would you give to readers learning tech? Do you have any top tips?
Authors: We think that the top 10 study tips you will find on Youth Central apply to “learning” in general.
Q. Do you have a blog that readers can follow?
Authors: Andreas is blogging in his blog at https://botsikas.blogspot.com/ about various technical topics he encounters.
Q. Can you share any blogs, websites, and forums to help readers gain a holistic view of the tech they are learning?
Authors: We have added various links to the book. The starting point is the Azure Machine Learning documentation in Microsoft Docs. (https://docs.microsoft.com/azure/machine-learning/)
If you are interested in catching up with the latest Azure-related updates in Machine Learning, you can follow the https://azure.microsoft.com/blog/topics/machine-learning/ blog.
Jason Brownlee Ph.D. is blogging in https://machinelearningmastery.com/, a great resource explaining various data science-related terms and providing great coding examples.
You can also read various opinions regarding data science on the https://towardsdatascience.com/ platform.
Q. How did you organize, plan, and prioritize your work and write the book?
Authors: We were working on weekends and vacations or during long nights.
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
Authors: Working with the Packt team was excellent. We started with an outline of the book, our north star, while writing the entire book. Once the book idea got approved, we were given resources to ramp up on how to write the book. We then did a single chapter with the constant guide of our editor, where we got some hands-on experience on using the word styles and structuring the content. Once we got a good grasp of writing a chapter, the content development followed an iterative approach with insightful editorial comments and excellent suggestions every time we finished a chapter. The technical reviewers helped us validate that the content was complete and provided all necessary explanations. If you are an aspiring author, we definitely recommend Packt as the editorial team.
Q. Do you belong to any tech community groups?
Authors: We are part of the Microsoft tech community at https://techcommunity.microsoft.com. We will be glad to see you there as well.
Q. Do you belong to any tech community groups?
Q. What is the one writing tip that you found most crucial and would like to share with aspiring authors?
Authors: Stick to the delivery plan of your chapters and keep the writing pace. During the pandemic, we had to stop writing the book for some months. Getting back into the flow was very difficult and time-consuming.
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You can find the book on Amazon by following this link click.