Your Speakers
Nur Fathiha Tahiat Seeum
Nur Fathiha Tahiat is a graduate student in the Department of Computer Science and Engineering at BRAC University in Bangladesh. She currently works as a Senior Manager in the R&D department of a well-known non-profit organization Bangladesh Extension Education Services (BEES) and has experience as a Student Tutor (ST) and Research Assistant (RA) in several organizations. She is currently a Gold- Microsoft learn student Ambassador having experience working with Azure Machine learning studio, LUIS, and Azure Cognitive services. With a passion for exploring data, doing analysis and drawing conclusions, she considers herself a Data Science and Machine Learning enthusiast. She is particularly interested in the intersection of AI and Public Health Research. In her free time, she enjoys writing, singing, and spending time with her family.

Sahan Dissanayaka
Sahan Dissanayaka is a final-year undergraduate in Computer Science at the University of Colombo School of Computing. He serves as a researcher at the COTS Lab at UCSC and collaborates with the AITeam Sri Lanka as a Researcher. Also, he is a dedicated learner, volunteer, and developer who has a strong interest in Application Development, Machine Learning, Data Science, and Computer Vision with a great work ethic and a willingness to learn, and continuous enthusiasm about the constantly evolving field of technology.

Event Description
Machine learning deployment using Azure can be valuable for organizations that are looking to scale their machine-learning models in a cost-effective and efficient way. Azure provides a range of services and tools to deploy, manage and monitor machine learning models in production environments. MLOps, on the other hand, is a set of practices and techniques that aim to streamline the process of developing, deploying, and managing machine learning models at a scale. It involves the integration of data science, software engineering, and operations to create a continuous delivery pipeline for machine learning models. By combining the power of Azure's machine learning deployment capabilities with MLOps practices, organizations can create a robust and scalable machine learning infrastructure that can deliver value to the business consistently and reliably.

Content Level: intermediate

Who is it aimed at?

1. Students, Developers, Data Scientists, and DevOps Engineers who are interested in learning how to deploy and manage machine learning models in production environments using Azure and MLOps practices
2. Individuals who have some knowledge of machine learning and Azure Machine Learning Designer but may not be familiar with deploying models in production or MLOps practices.
3. Individuals who are interested in learning how to leverage Azure's machine learning capabilities and MLOps frameworks to build scalable, robust, and reliable machine learning solutions.

Additional Resources
https://aka.ms/CreateNoCodePredicitiveModelsAzureReg1
https://aka.ms/ScaleAIOrganisationsReg1
https://aka.ms/BuildAISolutionswithAzureReg1