What does the MLOps course cover?
MLOps is covered here in 10 modules: Introduction to MLOps: Understanding the MLOps lifecycle, Data Preparation and Management: Data versioning and tracking changes, Model Development and Training: Hyperparameter tuning and optimization and 7 more. The outline lists 30 specific topics, opening with defining MLOps and its importance in machine learning and closing with applying MLOps principles to your own projects and organization.
How do you approach MLOps step by step?
The work is sequenced in 10 stages. It starts with Introduction to MLOps: Understanding the MLOps lifecycle, moves through Data Preparation and Management: Data versioning and tracking changes and Model Development and Training: Hyperparameter tuning and optimization, and ends at Case Studies and Real-World Applications: Real-world applications and case studies of MLOps.
What is in Module 1 of the MLOps course?
Module 1 is Introduction to MLOps: Understanding the MLOps lifecycle. It works through defining MLOps and its importance in machine learning, Understanding the MLOps lifecycle and key concepts and terminology in MLOps. It sets the vocabulary the remaining 9 modules build on.
How is the MLOps course delivered?
The MLOps course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the MLOps course cost?
The MLOps course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Machine Learning Operations (MLOps) Toolkit, MLOps Mastery, AI-Driven MLOps for Machine Learning Engineers, MLOps Frameworks for Machine Learning Practitioners.
More answers: what you get with every course, refund policy, all help answers.
Mastering MLOps: A Step-by-Step Guide to Implementing Machine Learning Operations
Course Overview
This comprehensive course is designed to help you master the art of Machine Learning Operations (MLOps). With a focus on practical, real-world applications, you'll learn how to implement MLOps in your organization and take your machine learning skills to the next level.Course Features
- Interactive and engaging learning experience
- Comprehensive curriculum covering all aspects of MLOps
- Personalized learning experience with expert instructors
- Up-to-date and high-quality content
- Practical, real-world applications and hands-on projects
- Flexible learning with lifetime access to course materials
- User-friendly and mobile-accessible platform
- Community-driven with discussion forums and live webinars
- Actionable insights and feedback from instructors
- Gamification and progress tracking to keep you motivated
Course Outline
Module 1. Introduction to MLOps: Understanding the MLOps lifecycle
- Defining MLOps and its importance in machine learning
- Understanding the MLOps lifecycle
- Key concepts and terminology in MLOps
Module 2. Data Preparation and Management: Data versioning and tracking changes
- Data quality and preprocessing techniques
- Data storage and management solutions
- Data versioning and tracking changes
Module 3. Model Development and Training: Hyperparameter tuning and optimization
- Machine learning model development best practices
- Model training and evaluation techniques
- Hyperparameter tuning and optimization
Module 4. Model Deployment and Serving: Model monitoring and logging
- Model deployment strategies and techniques
- Model serving and inference solutions
- Model monitoring and logging
Module 5. Model Monitoring and Maintenance: Model maintenance and updates
- Model performance monitoring and metrics
- Model maintenance and updates
- Model explainability and interpretability
Module 6. MLOps Tools and Technologies: Overview of popular, Using Docker and Kubernetes for MLOps
- Overview of popular MLOps tools and technologies
- Using Docker and Kubernetes for MLOps
- Using Apache Airflow and Zapier for workflow management
Module 7. Collaboration and Communication: strategies for MLOps teams
- Collaboration and communication strategies for MLOps teams
- Using version control systems like Git
- Creating and sharing documentation and reports
Module 8. Security and Compliance: considerations in MLOps, Data encryption and access control
- Security and compliance considerations in MLOps
- Data encryption and access control
- Regulatory compliance and governance
Module 9. Scalability and Performance: considerations in MLOps
- Scalability and performance considerations in MLOps
- Using distributed computing and parallel processing
- Optimizing model performance and resource utilization
Module 10. Case Studies and Real-World Applications: Real-world applications and case studies of MLOps
- Real-world applications and case studies of MLOps
- Success stories and lessons learned from industry leaders
- Applying MLOps principles to your own projects and organization
Certification
Upon completion of the course, participants will receive a certificate issued by The Art of Service.What to Expect
- Interactive and engaging learning experience with expert instructors
- Comprehensive curriculum covering all aspects of MLOps
- Personalized learning experience with flexible pacing and lifetime access to course materials
- Practical, real-world applications and hands-on projects to reinforce learning
- Actionable insights and feedback from instructors to help you improve
- Gamification and progress tracking to keep you motivated and engaged