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Mastering Machine Learning Operations (MLOps) and AI Security Bootcamp
Description
Machine Learning Operations (MLOps) and AI Security Bootcamp Introduction
Welcome to our MLOps and AI Security Bootcamp: Mastering Machine Learning Operations , a comprehensive three-day event designed to immerse technical professionals in the crucial areas of Machine Learning Operations (MLOps) and AI Security. This bootcamp bridges the essential gap between data science and operational teams, emphasizing continuous collaboration and integration within MLOps to efficiently produce and deploy AI models.
Our curriculum will deepen your understanding of the MLOps lifecycle with extensive tools and techniques, complemented by hands-on lab sessions. These labs are designed to enhance your skills in automating machine learning workflows and managing MLOps environments effectively.
In the segment on AI Security, you’ll explore how to safeguard AI systems—critical in today’s AI-dependent infrastructures. Guided by experts, you’ll navigate the landscape of AI threats and learn to implement robust security measures to protect your systems.
Diving deeper, the course covers advanced topics such as maintaining AI privacy, addressing ethical considerations, countering adversarial attacks, and devising defense strategies. By the end of this bootcamp, you will be equipped to streamline machine learning projects and enhance security measures within your organization, making you a proficient practitioner in both MLOps and AI Security.
Machine Learning Operations (MLOps) and AI Security Bootcamp Objectives
- Understand the MLOps lifecycle and distinguish it from DevOps and DataOps.
- Develop practical skills in MLOps tools and techniques, such as MLflow and Kubeflow.
- Master automating machine learning workflows for streamlined project efficiency.
- Familiarize with the AI Security landscape, identifying threats and implementing best practices.
- Dive into advanced AI Security concepts, including differential privacy and defending against adversarial attacks.
- Learn to balance technical implementation with ethical AI Security considerations.
Prerequisites
- Familiarity with basic machine learning concepts and algorithms.
- Experience with data preprocessing and programming, preferably in Python.
- Basic knowledge of cloud platforms like AWS, Azure, or GCP.
- Understanding of the software development process or lifecycle.
Audience
- Ideal for technical professionals eager to deepen their knowledge in MLOps and AI Security.
- Suitable for Data Scientists, Machine Learning Engineers, IT Security Professionals, DataOps Engineers, and technical leads overseeing AI projects.
Machine Learning Operations (MLOps) and AI Security Bootcamp Outline
Day 1
Introduction to MLOps
- Introduction to MLOps: Understanding its need and lifecycle.
- MLOps Tools and Techniques: Exploring MLflow, Kubeflow, and pipeline components.
- Automating Machine Learning Workflows: Role of automation in MLOps.
Day 2
Advanced MLOps and Beginning AI Security
- Model Monitoring and Management: Handling model decay and performance.
- Introduction to AI Security: Overview of the AI threat landscape and best practices.
Day 3
Advanced AI Security
- AI Privacy and Ethical Considerations: Risks in AI applications and understanding differential privacy.
- AI Adversarial Attacks and Defenses: Techniques against adversarial attacks.
$2495.00
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3 Days Course |