What does the MLOps course cover?
MLOps is covered here in 8 modules: Introduction to MLOps: MLOps vs DevOps, Benefits of MLOps, MLOps Principles and Best Practices: Automated testing and validation, MLOps Tools and Technologies: Overview of MLOps tools, TensorFlow Extended (TFX) and 5 more. The outline lists 36 specific topics, opening with What is MLOps? and closing with group discussions and presentations.
How do you approach MLOps step by step?
The work is sequenced in 8 stages. It starts with Introduction to MLOps: MLOps vs DevOps, Benefits of MLOps, moves through MLOps Principles and Best Practices: Automated testing and validation and MLOps Tools and Technologies: Overview of MLOps tools, TensorFlow Extended (TFX), and ends at Hands-on Projects and Case Studies.
What is in Module 1 of the MLOps course?
Module 1 is Introduction to MLOps: MLOps vs DevOps, Benefits of MLOps. It works through What is MLOps?, Importance of MLOps in Machine Learning, MLOps vs DevOps and 2 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: MLOps Model Deployment Automation for Operational, MLOps Continuous Model Deployment for Operational, Stop Re-Engineering AI Workflows, MLOps Implementation.
More answers: what you get with every course, refund policy, all help answers.
MLOps Complete Guide to Implementation and Deployment
Course Overview
This comprehensive course provides a detailed guide to implementing and deploying Machine Learning Operations (MLOps) in real-world applications. Participants will gain hands-on experience and in-depth knowledge of MLOps principles, tools, and best practices.Course Objectives
- Understand the fundamentals of MLOps and its importance in Machine Learning
- Learn how to design and implement MLOps pipelines for efficient model development and deployment
- Gain practical experience with MLOps tools and technologies
- Understand how to monitor and maintain deployed models
- Learn how to ensure model reliability, scalability, and security
Course Outline
Module 1. Introduction to MLOps: MLOps vs DevOps, Benefits of MLOps
- What is MLOps?
- Importance of MLOps in Machine Learning
- MLOps vs DevOps
- Benefits of MLOps
- Challenges in implementing MLOps
Module 2. MLOps Principles and Best Practices: Automated testing and validation
- MLOps principles
- Version control for data and models
- Collaboration and communication in MLOps
- Automated testing and validation
- Continuous Integration and Continuous Deployment (CI/CD)
Module 3. MLOps Tools and Technologies: Overview of MLOps tools, TensorFlow Extended (TFX)
- Overview of MLOps tools
- TensorFlow Extended (TFX)
- MLFlow
- Kubeflow
- Other MLOps tools and technologies
Module 4. Designing and Implementing MLOps Pipelines: Model deployment and serving
- Designing MLOps pipelines
- Data preparation and preprocessing
- Model development and training
- Model evaluation and validation
- Model deployment and serving
Module 5. Model Monitoring and Maintenance: Types of model monitoring, Model performance monitoring
- Importance of model monitoring
- Types of model monitoring
- Model performance monitoring
- Model explainability and interpretability
- Model updating and retraining
Module 6: Ensuring Model Reliability, Scalability, and Security
- Model reliability
- Model scalability
- Model security
- Best practices for ensuring model reliability, scalability, and security
Module 7. Advanced MLOps Topics: AutoML and MLOps, MLOps for edge devices
- AutoML and MLOps
- MLOps for edge devices
- MLOps for real-time applications
- MLOps for large-scale applications
Module 8: Hands-on Projects and Case Studies
- Hands-on projects
- Case studies
- Group discussions and presentations
Course Features
- Interactive and engaging: Video lectures, hands-on projects, and group discussions
- Comprehensive and up-to-date: Covers the latest MLOps tools and best practices
- Personalized learning: Flexible pacing and lifetime access to course materials
- Practical and real-world: Hands-on projects and case studies
- High-quality content: Expert instructors and high-quality video lectures
- Certification: Receive a certificate upon completion issued by The Art of Service
- Flexible learning: Learn at your own pace and on your own schedule
- User-friendly: Easy to navigate and access course materials
- Mobile-accessible: Access course materials on-the-go
- Community-driven: Join a community of learners and experts
- Actionable insights: Gain practical knowledge and skills
- Hands-on projects: Apply MLOps principles and tools to real-world projects
- Bite-sized lessons: Learn in manageable chunks
- Lifetime access: Access course materials for a lifetime
- Gamification: Engage with interactive elements and track progress
- Progress tracking: Monitor your progress and stay motivated