Mastering Machine Learning Operations (MLOps) and AI Security Boot Camp (TTAI2810)

Dive into the rapidly evolving world of Machine Learning Operations (MLOps) and AI Security with our intensive 3-day boot camp. MLOps bridges the gap between data science and operation teams, delivering continuous collaboration and integration to drive the efficient production of AI models. Similarly, AI Security focuses on protecting AI systems from potential vulnerabilities, a critical skillset given the increasing reliance on AI in modern infrastructures. By mastering these skills, you’ll be able to streamline machine learning projects and bolster security within your organization.

  • Price: $2,795.00
  • Duration: 3 days
  • Delivery Methods: Virtual
Date Time Price Option
03/24/2025 09:00 AM - 05:00 PM CT $2,795.00
05/19/2025 09:00 AM - 05:00 PM CT $2,795.00
07/14/2025 09:00 AM - 05:00 PM CT $2,795.00
09/08/2025 09:00 AM - 05:00 PM CT $2,795.00
10/27/2025 09:00 AM - 05:00 PM CT $2,795.00
12/08/2025 09:00 AM - 05:00 PM CT $2,795.00
For questions call: (469) 721-6100
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Why choose TOPTALENT?

  • Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
  • Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
  • Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
  • Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.

Course Topics / Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

Day 1: Introduction to Machine Learning Operations (MLOps)

  1. Introduction to MLOps
  • Understanding the need for MLOps
  • Differences between MLOps, DevOps, and DataOps
  • MLOps lifecycle overview
  1. MLOps Tools and Techniques
  • Overview of MLOps tools (MLflow, Kubeflow, etc.)
  • MLOps pipeline components
  • MLOps best practices
  • Hands-on Lab: Setting Up an MLOps Environment using MLflow
  • Walking through a simple machine learning pipeline
  1. Automating Machine Learning Workflows
  • The role of automation in MLOps
  • Continuous Integration and Continuous Deployment (CI/CD) in machine learning
  • Hands-on Lab: Automating ML workflows

Day 2: Advanced MLOps and Beginning AI Security

  1. Model Monitoring and Management
  • Understanding model decay
  • Monitoring model performance in production
  • Model versioning and rollback
  • Hands-on Lab: Model Management
  • Implementing model monitoring with MLflow
  • Experimenting with model versioning and rollback
  1. Introduction to AI Security
  • Understanding the need for AI Security
  • Overview of AI threat landscape
  • AI Security best practices
  • Hands-on Lab: Implementing basic security measures in a machine learning environment

Day 3: Advanced AI Security

  1. AI Privacy and Ethical Considerations (2 hours)
  • Privacy risks in AI/ML applications
  • Understanding differential privacy
  • Ethical considerations in AI Security
  • Hands-on Lab: Implementing differential privacy in a machine learning model
  1. AI Adversarial Attacks and Defenses
  • Understanding adversarial attacks
  • Techniques to defend against adversarial attacks
  • Hands-on Lab: Defending Against Adversarial Attacks
  • Implementing defense measures against sample adversarial attacks

Course Wrap-Up and Q&A

Learning Objectives

Throughout the course you’ll learn how to:

  • Gain a solid understanding of the Machine Learning Operations (MLOps) lifecycle, including its purpose, key elements, and how it differs from related fields like DevOps and DataOps.
  • Develop practical skills in using key MLOps tools and techniques, such as setting up an MLOps environment using MLflow and Kubeflow, and working through a basic machine learning pipeline.
  • Master the art of automating machine learning workflows to streamline and improve the efficiency of your machine learning projects.
  • Familiarize yourself with the AI Security landscape, including threat identification and application of best practices for securing machine learning environments.
  • Dive deep into advanced AI Security concepts, including understanding and implementing differential privacy in machine learning models and defending against adversarial attacks.
  • Learn to balance technical implementation with ethical considerations, developing a well-rounded approach to AI Security that respects privacy concerns and adheres to ethical guidelines.

The intermediate and beyond level course is a great fit for technical professionals eager to deepen their knowledge in machine learning and AI security. Roles such as Data Scientists, Machine Learning Engineers, IT Security Professionals, and DataOps Engineers would find significant value in this intensive, hands-on learning experience. This course is also suitable for technical leads and managers who oversee machine learning projects and need to understand both the operational and security aspects of AI systems.

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

  • Familiarity with basic machine learning concepts such as supervised and unsupervised learning, regression, classification, and neural networks will be beneficial.
  • Experience with data preprocessing, feature engineering, and understanding of algorithms and data structures would be advantageous.
  • Ideally, attendees should have practical experience with a programming language, preferably Python, given its prominence in machine learning and AI development. Those without programming background can follow along with the labs.
  • Basic knowledge of cloud platforms like AWS, GCP, or Azure will be useful, especially regarding how they support machine learning operations and AI security.
  • A general understanding of the software development process or lifecycle (SDLC), including stages like design, development, testing, and deployment, will be helpful as MLOps is a similar, but more specific, lifecycle.

Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.

Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.

You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.

If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.

You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.