Course Included :

  • 1

    MLOps and Data Engineering

    • Building Multi Cloud Success for Cross Database Usage

    • Slides - Machine Learning and AI in 2021: Recent Trends, Technologies, and Challenges

    • Machine Learning and AI in 2021: Recent Trends, Technologies, and Challenges

    • Slides - MLOps in DL Model Development

    • MLOps in DL Model Development

    • Re-imagining Privacy in the Age of Technical and Social Disruption


Professor, Researcher at University of Wisconsin-Madison | Author of 'Python Machine Learning'

Sebastian Raschka, PhD

Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics. Sebastian Raschka is also the author of the bestselling book “Python Machine Learning", which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.

Chief Technical Officer |

Anna Petrovicheva

Anna is CTO of OpenCV.AI - a for-profit arm of, the most popular Computer Vision library in the world. Anna is an expert in Deep Learning for Computer Vision with 10-year experience in the industry. Previously Anna created open-source optimized Machine Learning libraries, and worked on state-of-the-art Deep Learning algorithms for autonomous driving, retail, medicine and AR, most of them specifically optimized for fast inference on small edge devices.

Chief Cloud Strategy Officer, Adjunct Instructor | Deloitte Consulting, Louisiana State University

David Linthicum

David Linthicum was named one of the top 9 Cloud Pioneers in Information Week 7 years ago, but started his cloud journey back in 1999 when he envisioned leveraging IT services over the open internet. Dave was named the #1 cloud influencer via a major report by Apollo Research, and is typically listed as a top 10 cloud influencer, podcaster, and blogger. David is a cloud computing thought leader, executive, consultant, author, and speaker. David has been a CTO five times for both public and private companies, and a CEO two times in his 35 year career. He is credited with creating 4 billion dollars in shareholder return in those roles. Beyond cloud computing Dave has created, or assisted in creating foundational technical concepts, including Enterprise Application Integration (EAI), Service Oriented Architecture (SOA), and advanced distributed computing architectures. All still in use today.

Head of the Computing Science Faculty, Program Director | Griffith College Dublin

Dr. Waseem Akhtar

Dr. Waseem Akhtar is Head of Computing Science faculty at Griffith College Dublin. He holds a PhD in Computer Science from University College Dublin (UCD) and an MBA in Higher Education Management from University College London (UCL). Waseem's current research interests focus on multidisciplinary topics such Big Data Analytics based Business, Process and Management Intelligence Systems, Evidence and Analytics Based Management, Robotic Process Automation and other applications of Artificial Intelligence, Machine Learning and Big Data Analytics. His research interests also include software development for large & complex systems and studying the fundamental nature of information. Waseem is also interested in science communication, history and dissemination of knowledge and studying the impact of emerging technologies on the future of humanity.

MLOPs and Data Engineering

As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices increases. The challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI are a few of the issues organizations wrestle with.