David Akuma
Software Engineer
Manchester, United Kingdom
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David is a seasoned Software Engineer specializing in Cloud Engineering/Architecture and MLOps. He is also an AWS Community Builder and AWS-certified Solutions Architect. He enjoys sharing his development journey across different platforms and mentoring junior engineers. He is experienced in helping businesses build resilient and highly available systems in the cloud.
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Topics
Automate the Management of Temporary Elevated Access with IAM Identity Center
Automation of work flows and processes is encouraged across cloud estates and achieving full automation can be a daunting task - Temporary Access Management shouldn’t be on the list of the headaches organisations using AWS should be facing. Temporary Elevated Access Management (TEAM) is an open source solution provided to deal with this. We will delve into the intricacies of contemporary cloud access management. Uncover the nuances of the Temporary Elevated Access Management (TEAM) solution, intricately woven into the fabric of AWS IAM Identity Center. Explore the implementation of just-in-time access, striking a harmonious chord between automation and human-driven operations. Navigate through TEAM's streamlined workflow, approval processes, and a robust authorization model, ensuring a secure AWS environment with tailored human interactions. Elevate your comprehension of advanced cloud access management – an indispensable discussion for today's dynamic technological landscape.
Security Considerations for MLOps Infrastructure on AWS
The rapid adoption of MLOps has unlocked new levels of innovation, allowing organizations to build, deploy, and maintain machine learning models efficiently. However, these advantages come with security challenges that are often underestimated, leading to risks such as data breaches, model theft, and unauthorized access. Securing MLOps infrastructure on AWS requires a holistic approach, extending beyond traditional cloud security practices to address the unique needs of machine learning workflows.
In this session, we’ll explore often overlooked but critical security considerations for MLOps environments. We’ll discuss strategies for protecting sensitive training data, securing model artifacts in storage, and implementing fine-grained access control across AWS services like SageMaker, S3, Lambda, and Redshift. Emphasis will be placed on securing data pipelines, handling PII securely and employing robust encryption methods for data at rest and in transit.
We’ll also tackle overlooked areas such as securing third-party integrations, preventing data poisoning attacks, and monitoring for malicious model behavior using AWS-native tools like GuardDuty, CloudWatch and Detective. Real-world case studies and practical examples will illustrate best practices and pitfalls to avoid.
Attendees will gain actionable insights on how to safeguard their MLOps workflows, ensuring not only model performance but also data integrity and trustworthiness. Whether you're a security professional, machine learning engineer, or cloud architect, this presentation will equip you with the knowledge to build resilient and secure MLOps solutions on AWS.
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