Nadia Reyhani
DevOps engineer at Mechanical Rock
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Nadia is extremely passionate about AI and Machine Learning, with a goal of bringing DevOps best practices into the ML Solutions. Currently a Full-stack developer at Mechanical. Nadia is also an AWS Community Builder, certified AWS Solutions Architect, certified AWS Machine Learning and a huge advocate for diversity in tech.
Harnessing Generative AI with Sustainable MLOps
In this presentation, we will explore the key MLOps principles and best practices to ensure efficient development, deployment and operation of generative AI applications. By integrating these best practices, organisations can unlock new avenues to leverage the full potential of generative AI. We will use real world use cases to show case how to implement these best practices.
Win the security battle in your Machine Learning workflow
Prioritising security in Machine Learning is crucial. The implementation of automated and efficient security practices can empower Data Scientists and bolster your team's capabilities. However, acquiring expertise in multiple domains and applying these measures to Machine Learning workloads can be challenging for Data Scientists.
Security has long been a fundamental aspect of DevOps, how can it be applied to Machine Learning?
In this presentation, I will examine the security risks within a Data Scientist environment utilising Amazon SageMaker. Furthermore, I will offer a demonstration of how to leverage the "Amazon Service Catalog" to equip data scientists with ready-to-use templates that incorporate essential security features.
By doing so, we can proactively safeguard our solutions and stay ahead of potential vulnerabilities.
Data Warehouse: Data Scientists' new lab!
Incorporating Machine Learning into your traditional data warehouse can be a complex process that entails managing multiple distributed components written in programming languages that may be unfamiliar to you, potentially slowing down your delivery speed.
Imagine if your data warehouse could serve as an "All-in-One Tool" for your Data Scientists, enabling them to securely and cost-effectively utilise Machine Learning models without the need for extensive Extract, Transform, Load (ETL) processes. What if all they needed was SQL?
In this presentation, I will demonstrate how Amazon Redshift ML can serve as the ultimate solution, acting as the "One Tool to Rule Them All," allowing you to achieve this seamlessly in just a few minutes.
Unveiling the Power of MLOps: Avoiding the Pitfalls of MLOops!
Machine Learning, an established field for many years, has witnessed significant investments from organisations. Many of these companies embarked on building machine learning solutions with the belief that artificial intelligence would solve all their problems. However, a lack of understanding regarding the lifecycle of a data science project was evident, even among those proficient in software development.
In this captivating presentation, we will delve into the realm of MLOps.
We will explore a range of popular solutions available on AWS that can enhance and streamline your ML workflows, helping you navigate the decision-making process effectively.
Furthermore, you will have the valuable opportunity to witness a live demonstration showcasing the seamless implementation of a collaborative MLOps solution. By leveraging the power of AWS Step Functions, this solution will unite your Data Science and DevOps teams, allowing for efficient collaboration and enhanced productivity.
By the end of this presentation, you will gain valuable insights and practical knowledge to navigate the MLOps landscape, avoiding common pitfalls and maximising the potential of your machine learning initiatives.
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