What does the MLOps course cover?
MLOps is covered here in 8 modules: Introduction to MLOps, Machine Learning Fundamentals, Data Preparation and Management and 5 more. The outline lists 32 specific topics, opening with Defining MLOps : Understanding the concept and importance of MLOps and closing with Future Directions : Future directions and trends in MLOps.
How do you approach MLOps step by step?
The work is sequenced in 8 stages. It starts with Introduction to MLOps, moves through Machine Learning Fundamentals and Data Preparation and Management, and ends at Real-World Applications and Case Studies. Each stage carries its own topic list, so the sequence is followed rather than summarised.
What is in Module 1 of the MLOps course?
Module 1 is Introduction to MLOps. It works through Defining MLOps : Understanding the concept and importance of MLOps, Machine Learning Lifecycle : Overview of the machine learning lifecycle and its phases, mLOps Challenges : Identifying challenges in deploying and managing machine learning models and 1 more. It sets the vocabulary the remaining 7 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: Business and Information Systems Engineering, IT-OT Convergence, Closing the Gap Between Attention and Action, Machine MLOps Toolkit.
More answers: what you get with every course, refund policy, all help answers.
Mastering MLOps: Bridging the Gap between Machine Learning and Operations
Course Overview
This comprehensive course is designed to bridge the gap between machine learning and operations, providing participants with the knowledge and skills needed to deploy and manage machine learning models in a production environment. Upon completion of the course, participants will receive a certificate issued by The Art of Service.Course Features
- Interactive and engaging learning experience
- Comprehensive and up-to-date content
- Personalized learning approach
- Practical and real-world applications
- High-quality content and expert instructors
- Certificate issued upon completion
- Flexible learning schedule and user-friendly interface
- Mobile-accessible and community-driven
- Actionable insights and hands-on projects
- Bite-sized lessons and lifetime access
- Gamification and progress tracking
Course Outline
Module 1: Introduction to MLOps
- Defining MLOps: Understanding the concept and importance of MLOps
- Machine Learning Lifecycle: Overview of the machine learning lifecycle and its phases
- MLOps Challenges: Identifying challenges in deploying and managing machine learning models
- MLOps Benefits: Understanding the benefits of implementing MLOps
Module 2: Machine Learning Fundamentals
- Supervised Learning: Understanding supervised learning concepts and algorithms
- Unsupervised Learning: Understanding unsupervised learning concepts and algorithms
- Deep Learning: Introduction to deep learning concepts and algorithms
- Model Evaluation: Understanding metrics for evaluating machine learning models
Module 3: Data Preparation and Management
- Data Preprocessing: Techniques for preprocessing and cleaning data
- Data Transformation: Methods for transforming and feature engineering data
- Data Storage: Understanding data storage options and solutions
- Data Governance: Importance of data governance and quality control
Module 4: Model Development and Deployment
- Model Development: Best practices for developing machine learning models
- Model Deployment: Techniques for deploying machine learning models
- Model Serving: Understanding model serving options and solutions
- Model Monitoring: Importance of monitoring and logging model performance
Module 5. MLOps Tools and Technologies: Automation : Importance of automation in MLOps
- Containerization: Understanding containerization using Docker
- Orchestration: Introduction to orchestration using Kubernetes
- Model Management: Understanding model management tools and platforms
- Automation: Importance of automation in MLOps
Module 6. Collaboration and Communication: Documentation : Importance of documentation in MLOps
- Stakeholder Management: Understanding stakeholder roles and responsibilities
- Communication Strategies: Effective communication strategies for MLOps teams
- Collaboration Tools: Introduction to collaboration tools and platforms
- Documentation: Importance of documentation in MLOps
Module 7. MLOps Best Practices: Version Control : Understanding version control using Git
- Version Control: Understanding version control using Git
- Testing and Validation: Importance of testing and validation in MLOps
- Continuous Integration: Understanding continuous integration and delivery
- Security and Compliance: Importance of security and compliance in MLOps
Module 8: Real-World Applications and Case Studies
- Industry Examples: Real-world examples of MLOps in different industries
- Case Studies: In-depth case studies of successful MLOps implementations
- Lessons Learned: Key takeaways and lessons learned from MLOps implementations
- Future Directions: Future directions and trends in MLOps