Gain practical skills in MLOps and AI Security, including pipeline setup, workflow automation, and threat identification

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.

Working in a hands-on workshop style environment guided by our AI security expert, you’ll explore a wide range of topics and hands-on labs designed to provide a robust understanding of both MLOps and AI Security. Starting from an introduction to MLOps, you'll uncover the importance of this discipline, its distinction from DevOps and DataOps, and its lifecycle. You'll explore MLOps tools and techniques, including MLflow and Kubeflow, along with pipeline components and best practices. You will be able to set up an MLOps environment, automate ML workflows, monitor and manage models, and implement vital security measures in real-world situations. Lastly, you'll dive into the world of AI Security, exploring the AI threat landscape and best practices while applying basic security measures in a lab environment. The boot camp wraps up with advanced topics in AI Security, covering AI privacy, ethical considerations, adversarial attacks, and defenses.

Upon completion, you will have gained practical, hands-on skills in operationalizing and securing machine learning workflows, implementing best practices in model management, and understanding ethical considerations in AI Security. Our boot camp ensures that you will have the necessary knowledge to navigate MLOps and AI Security effectively, making your machine learning projects more efficient and secure.

Who Should Attend?

This intermediate to advanced 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.

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We can host this training at your preferred location.

Prerequisites

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.

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What You Will Learn

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.

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Outline

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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

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Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Machine Learning Operations (MLOps) and AI Security Boot Camp Training Course in Finland

Finland is a country located in northern Europe. Helsinki is the capital and largest city of the country. The majority of the people are Finns but there is also a small Lapp population in Lapland, where the country is famous for the Northern Lights. Finland's national languages are Finnish and Swedish.

Known for its vast forests, lakes, and natural beauty, Finland is one of the world's largest producers of forest products, such as paper, pulp, and lumber. One of the world's largest sea fortresses Suomenlinna, Rovaniemi with the "White Nights", dogsled safaris and of course the Northern Lights are what makes Finland so popular for tourists. Finland is one of the best places in the world to see the Northern Lights and attracts millions of tourists during its seasons.

Finland is home to a thriving technology industry and is widely recognized as one of the world's leading technology hubs. Companies such as Nokia and Rovio (creator of the popular game Angry Birds) are based in Finland. Some of the key factors that have contributed to Finland's success in technology include; strong investment in research and development, a highly educated workforce and fundings.

Finland has a strong educational system, and is widely regarded as one of the world's most literate countries. In fact, Finland's literacy rate is one of the highest in the world, and its students consistently perform well in international tests of math and reading ability.

Also, as a pioneer in environmental sustainability, Finland is known for its efforts to reduce its carbon footprint and promote clean energy. This Nordic country is also famous for its unique and distinctive cultural heritage, including its traditional folk music and its elaborate traditional costumes.

Helsinki, Finland's capital city, is the country's business center. Helsinki is Finland's largest city, and it is home to many of the country's major corporations and organizations, including many of the country's leading technology firms. The city is also a commercial, trade, and financial center, as well as one of the busiest ports in the Nordic region.

Take advantage of our diverse IT course offerings, spanning programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our knowledgeable instructors will provide you with practical training and industry insights, delivered directly to your chosen venue in Finland.

Machine Learning Operations (MLOps) and AI Security Boot Camp Training in Finland

Who Should Attend?

Prerequisites

What You Will Learn

Outline

Avaible Training Dates