Are you curious about how your job title, gender, state, age, or education impact your salary? Want to determine whether a particular cloud certification is worth going for? Wonder how many of your peers are working remotely--and what that means for salaries? Or do you just want to see how your skills and salary compare to others in your field? Get the answers to your salary questions in the O'Reilly 2022 Cloud Salary Survey report.
Get a clear picture of the salaries and bonuses data science professionals around the world receive, as well as the tools and cloud providers they use, the tasks they perform, and how interpersonal ("soft") skills might affect their pay. The fifth edition of O'Reilly's online Data Science Salary Survey provides complete results from nearly 800 participants from 69 different countries, 42 different US states, and Washington, DC. With five years of data, the survey's results are consistent enough to reliably identify changes and trends. The survey asked specific questions about industry, team, and company size, but also posed questions such as, "How easy is it to move to another position?" or "What is your next career step?" You can plug in your own data points to the survey model and see how you compare to other data science professionals in your industry. With this report, you'll learn: Where data scientists make the highest salaries-by country and by US state Tools that respondents most commonly use on the job, and tools that contribute most to salary Activities that contribute to higher earnings How gender and bargaining skills affect salaries when all other factors are equal Salary differences between those using open source tools vs those using proprietary tools How the increase in respondents outside of the US signal a rise in international companies starting and growing data organizations Participate in the 2018 Survey: Spend just 5 to 10 minutes and take the anonymous salary survey here: https://www.oreilly.com/ideas/take-the-​data-science-salary-survey .
The 2022 International Conference on Information Economy, Data Modeling and Cloud Computing (ICIDC 2022) was successfully held in Qingdao, China from June 17 to 19, 2022. Under the impact of COVID-19, ICIDC 2022 was held adopting a combination of online and offline conference. During this conference, we were greatly honored to have Prof Datuk Dr Hj Kasim Hj Md Mansur from Universiti Malaysia Sabah, Malaysia to serve as our Conference Chairman. And there were 260 individuals attending the conference. The conference agenda was composed of keynote speeches, oral presentations, and online Q&A discussion. The proceedings of ICIDC 2022 cover various topics, including Big Data Finance, E-Commerce and Digital Business, Modeling Method, 3D Modeling, Internet of Things, Cloud Computing Platform, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication. Data modeling allows us to obtain the dynamic change trend of various indicator data, so how to use big data information to model and study the development trend of economic operation plan is of great significance. And that is exactly the purpose of this conference, focusing on the application of big data in the economic field as well as conducting more profound research in combination with cloud computing.
With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
