TTAI2810: Mastering Machine Learning Operations (MLOps) and AI Security Boot Camp
About this Course
Artificial intelligence and machine learning operations (MLOps) are reshaping how organizations create, deploy, and protect their AI systems, and learning these skills puts you at the forefront of this transformation. This expert-led, three-day boot camp is designed to help you build the confidence and know-how to work with machine learning pipelines and secure AI systems in real-world settings. Whether you are a data scientist, machine learning engineer, DevOps specialist, cybersecurity professional, or a technical lead or manager overseeing AI projects, this course will help you strengthen your technical skills while gaining the bigger-picture perspective that organizations need today. With about 50 percent hands-on labs, you will not just hear about the techniques, you will practice them yourself.
Throughout the camp, you will learn how to design ML pipelines that are efficient, reliable, and ready for production, while also building a strong foundation in AI security. You will gain experience automating machine learning workflows to save time and reduce errors, setting up model monitoring to keep performance on track, and applying continuous integration and delivery to keep systems running smoothly. More importantly, you will understand why these practices matter — how they help teams work better together, avoid common pitfalls, and reduce the risks that can come from neglected models or poor version control. You will also build valuable skills in secure coding, learning how to protect systems against vulnerabilities, avoid adversarial attacks, and ensure that your AI integrations work safely inside web applications.
On the security side, you will explore how to think like a defender, identifying threats before they cause harm and creating action plans for incident response. You will also learn how to strengthen privacy, ethical practices, and governance around AI, helping you contribute not just as a technical expert but as a thoughtful leader on your team. Whether you are hands-on with the code or guiding AI projects from a leadership role, you will leave this course ready to help your organization improve the way it builds, deploys, and secures AI — adding value that goes far beyond the technology itself.
Audience Profile
This course is designed for intermediate-level learners who want to deepen their technical skills in MLOps and AI security. It is a great fit for data scientists, machine learning engineers, DevOps professionals, IT security specialists, and technical leads or managers who support AI projects. To get the most from this course, you should have some hands-on experience with machine learning and basic familiarity with cloud services or programming.
Skills-Based Pre-Reqs:
1.A solid understanding of machine learning fundamentals such as supervised and unsupervised learning and basic model building.
2.Basic Python programming experience and comfort working with data analysis tasks.
3.Familiarity with general cybersecurity principles and cloud or DevOps workflows.
At Course Completion
The goal of this course is to help you build the confidence and skills to manage machine learning pipelines and secure AI systems in ways that truly make an impact. By the end of the course, you will be able to:
- Design and automate machine learning workflows that save time, reduce errors, and keep projects on track.
- Monitor and manage models effectively so you can quickly spot and fix performance issues before they affect results.
- Apply continuous integration and delivery practices to ensure smooth and consistent updates to your AI systems.
- Strengthen your secure coding skills to help protect systems from common vulnerabilities and risks.
- Identify and defend against AI threats like adversarial attacks, data poisoning, and prompt injection to keep systems safe.
- Develop practical strategies for managing AI risks, improving privacy practices, and guiding your team toward more secure and ethical AI use.
If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.
Outline
Please note that this list of topics is based on our standard course offering, evolved from current industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation. The course tools, topics, use cases and hands-on labs can also be easily adjusted to suit your specific needs, goals or requirements. Please inquire for details and options.
Introduction to MLOps
Learn why MLOps matters and how it connects data science and operations to bring machine learning models smoothly into real-world use.
MLOps: The key to integrating data science with operations for AI model efficiency
Understanding the need for MLOps
Differences between MLOps, DevOps, and DataOps
MLOps lifecycle overview
MLOps Tools and Techniques
Get familiar with the key tools and practices that help you build, manage, and improve machine learning pipelines with confidence.
Review essential tools and practices for building effective and sustainable ML pipelines
Overview of MLOps tools (MLflow, Kubeflow, etc.)
MLOps pipeline components
MLOps best practices
Walking through a simple machine learning pipeline
Automating Machine Learning Workflows
Discover how to save time and improve results by automating important steps in your machine learning workflows.
Explore the importance of automating ML workflows for improved efficiency and model deployment
The role of automation in MLOps
Continuous Integration and Continuous Deployment (CI/CD) in machine learning
Model Monitoring and Management
Find out how to keep an eye on your models, catch performance issues early, and keep them running at their best.
Understanding model decay and why it matters
Continuous performance monitoring and anomaly detection
Data drift and concept drift analysis
Implementing feedback loops for continuous improvement
Learn how to track, organize, and safely update your models so you always know what’s running in production.
MLflow in Practice
See how to use MLflow to log, compare, and fine-tune your experiments, making it easier to learn what works and what doesn’t.
Setting up MLflow tracking servers and UI
Organizing experiments, runs, and hyperparameter tuning
Capturing parameters, metrics, and artifacts
Visualizing and comparing experiment results
Orchestration and Pipelines
Explore how to design smart, automated pipelines that help your machine learning projects run smoothly and reliably.
Understanding the role of orchestration in MLOps
Setting up workflows and pipelines using tools like Prefect
Adding retries, notifications, and caching
Enabling collaboration across teams and workflows
Foundations of AI and Secure Coding
Understand the basics of AI and why secure coding is critical when building or working with AI-powered applications.
Exploring the intersection of AI and software security
Key concepts: machine learning, deep learning, LLMs, generative AI
Real-world examples of AI in web applications
Why AI awareness matters for secure coding and design
Secure Coding Principles in the Age of AI
Learn how to write and check code that keeps AI systems safe from common and emerging risks.
Identifying AI-specific coding vulnerabilities
Applying OWASP principles to AI systems
Securing data pipelines, model inputs, and outputs
Implementing output constraints and validation
How AI Attacks Your Code, Systems, and Teams
See how attackers use AI to find weak spots—and how you can recognize and reduce these risks.
Understanding AI-powered attack techniques
Prompt injection, data poisoning, and model evasion
Risks from AI-generated code and API abuse
Human-in-the-loop risks and social engineering amplification
Defending Against AI-Powered Attacks
Build practical strategies for protecting your systems and data from AI-driven threats.
Building AI-aware threat models
Mapping risks to practical defenses and controls
Monitoring and logging for AI systems
Tools and strategies for securing the software supply chain
Secure AI Integration in Web Applications
Learn how to safely connect AI models to your web applications without adding new security holes.
Managing risks of integrating AI in web apps
Securing API interactions and data protection
Validating and sanitizing inputs and outputs
Avoiding common integration pitfalls
Natural Language Processing (NLP) and AI Security Risks
Understand the unique risks that come with NLP tools and how to use them safely in your work.
Understanding NLP and its widespread use
Mitigating prompt injection, data leakage, and context hijacking
Using NLP tools responsibly in security workflows
Implementing safeguards for NLP models in production
AI Risk Management and Security Leadership
Discover how to guide teams and organizations toward smart, responsible, and secure AI use.
Applying governance frameworks to manage AI risk
Incorporating AI into the secure development lifecycle (SDLC)
Establishing guardrails and policy approaches
Evaluating AI tools and leading responsible adoption
Bonus: Staying Safe with AI Tools at Work
Pick up practical tips to help yourself and your team use popular AI tools safely and responsibly on the job.
Recognizing risks of workplace AI tools (e.g., ChatGPT, Copilot)
Best practices for safe AI use on the job
Protecting data, intellectual property, and organizational reputation
Tips for guiding teams on responsible AI adoption
Addendum: Resources, Insights & Tip Guides
Prerequisites
To get the most out of this course, you should have experience with:
Machine Learning Fundamentals. Understanding of supervised and unsupervised learning, model training, and evaluation techniques.
Python Programming for Data Science. Ability to write and modify Python scripts, work with libraries like Pandas and NumPy, and preprocess data for machine learning.
Basic Cloud and DevOps Concepts. Familiarity with cloud platforms (AWS, Azure, or GCP), version control (Git), and workflow automation principles.
Take Before: In order to gain the most from this course, you should have incoming skills equivalent to those in the course listed below, or should have attended this as a prerequisite:
TTML5502
Exploring AI & Machine Learning for the Enterprise Overview (Light Hands-on)
TTPS4873
Fast Track to Python for Data Science and/or Machine Learning