Applied Machine Learning and High-Performance Computing on AWS
Accelerate the development of machine learning applications following architectural best practices
Mani Khanuja
Farooq Sabir
Shreyas Subramanian
Trenton Potgieter
BIRMINGHAM—MUMBAI
Applied Machine Learning and High-Performance Computing on AWS
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Contributors
About the authors
Mani Khanuja is a seasoned IT professional with over 17 years of software engineering experience. She has successfully led machine learning and artificial intelligence projects in various domains, such as forecasting, computer vision, and natural language processing. At AWS, she helps customers to build, train, and deploy large machine learning models at scale. She also specializes in data preparation, distributed model training, performance optimization, machine learning at the edge, and automating the complete machine learning life cycle to build repeatable and scalable applications.
Farooq Sabir is a research and development expert in machine learning, data science, big data, predictive analytics, computer vision, and image and video processing. He has over 10 years of professional experience.
Shreyas Subramanian helps AWS customers build and fine-tune large-scale machine learning and deep learning models, and rearchitect solutions to help improve the security, scalability, and efficiency of machine learning platforms. He also specializes in setting up massively parallel distributed training, hyperparameter optimization, and reinforcement learning solutions, and provides reusable architecture templates to solve AI and optimization use cases.
Trenton Potgieter is an expert technologist with 25 years of both local and international experience across multiple aspects of an organization; from IT to sales, engineering, and consulting, on the cloud and on-premises. He has a proven ability to analyze, assess, recommend, and design appropriate solutions that meet key business criteria, as well as present and teach them from engineering to executive levels.
About the reviewers
Ram Vittal has over 20 years of experience engineering software solutions for solving complex challenges across various business domains. Ram started his career with mainframes and then moved on to building distributed systems using Java technologies. Ram started his cloud journey in 2015 and has helped enterprise customers migrate, optimize, and scale their workloads on AWS. As a principal machine learning solutions architect, Ram has helped customers solve challenges across areas such as security, governance, and big data for building machine learning platforms. Ram has delivered thought leadership on big data, machine learning, and cloud strategies. Ram holds 11 AWS certifications and has a master’s degree in computer engineering.
Chakravarthy Nagarajan is a technical evangelist with 21 years of industry experience in machine learning, big data, and high-performance computing. He is currently working as a Principal AI/ML Specialist Solutions Architect at AWS in Bay Area, USA. He helps customers to solve real-world complex business problems by building prototypes with end-to-end AI/ML solutions on cloud, and edge devices. His specialization includes computer vision, natural language processing, time series forecasting, and personalization. He is also a public speaker and has published multiple blogs and white papers on HPC and AI/ML. On the academic front, he completed his MBA from United Institute, Brussels, and has multiple certifications in AI and ML.
Anna Astori holds a master’s degree in computational linguistics and artificial intelligence from Brandeis University. Over the years, Anna has worked on multiple large-scale machine learning and data science applications for companies such as Amazon and Decathlon. Anna is an AWS Certified Developer and Solutions Architect. She speaks at conferences and podcasts, reviews talk proposals for tech conferences, and writes about Python and machine learning for curated publications on Medium. She is currently a co-director of the Women Who Code Boston network.
Kevin Sayers is a research scientist based in Colorado with expertise in high-performance computing (HPC). He holds a master’s degree in bioinformatics. His work has primarily focused on scientific workflows and he is an open source contributor to a number of workflow tools. He has previously worked with university HPC centers and a national lab supporting the HPC user community. He currently works in cloud HPC bringing customer HPC and machine learning workloads to the cloud.