What does the MLOps course cover?
MLOps is covered here in 7 modules: Introduction to MLOps: MLOps vs. DevOps, Benefits of MLOps, Data Preparation: Data quality and validation, Data storage and management, Model Training: Model optimization and regularization and 4 more. The outline lists 29 specific topics, opening with What is MLOps? and closing with final project presentation and review.
How do you approach MLOps step by step?
The work is sequenced in 7 stages. It starts with Introduction to MLOps: MLOps vs. DevOps, Benefits of MLOps, moves through Data Preparation: Data quality and validation, Data storage and management and Model Training: Model optimization and regularization, and ends at Self-Assessment and Project: Project evaluation and feedback.
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?, Benefits of MLOps, MLOps vs. DevOps and 1 more. It sets the vocabulary the remaining 6 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 MLOps Toolkit, Machine Learning Operations (MLOps) Toolkit, MLOps Mastery, Automated Machine Learning (AutoML) Models.
More answers: what you get with every course, refund policy, all help answers.
Mastering MLOps: A Hands-on Guide to Machine Learning Operations and Self-Assessment
Course Overview
This comprehensive course is designed to equip you with the knowledge and skills needed to master Machine Learning Operations (MLOps) and implement it in your organization. With a focus on hands-on learning and self-assessment, you'll gain a deep understanding of the concepts, tools, and techniques required to streamline your machine learning workflows.Course Objectives
- Understand the fundamentals of MLOps and its importance in machine learning
- Learn how to design and implement efficient MLOps pipelines
- Gain hands-on experience with popular MLOps tools and technologies
- Develop skills in data preparation, model training, and model deployment
- Learn how to monitor and maintain machine learning models in production
- Understand how to apply MLOps in real-world scenarios
Course Outline
Module 1. Introduction to MLOps: MLOps vs. DevOps, Benefits of MLOps
- What is MLOps?
- Benefits of MLOps
- MLOps vs. DevOps
- Key components of MLOps
Module 2. Data Preparation: Data quality and validation, Data storage and management
- Data ingestion and processing
- Data quality and validation
- Data transformation and feature engineering
- Data storage and management
Module 3. Model Training: Model optimization and regularization
- Model selection and hyperparameter tuning
- Model training and evaluation
- Model optimization and regularization
- Model interpretability and explainability
Module 4. Model Deployment: Model serving and deployment, Model monitoring and logging
- Model serving and deployment
- Model monitoring and logging
- Model updating and maintenance
- Model scaling and performance optimization
Module 5. MLOps Tools and Technologies: PyTorch and PyTorch Lightning, Kubernetes and containerization
- TensorFlow and TensorFlow Extended (TFX)
- PyTorch and PyTorch Lightning
- Scikit-learn and Scikit-learn Pipelines
- Kubernetes and containerization
- Apache Airflow and workflow management
Module 6. MLOps in Practice: MLOps in healthcare and finance, Case studies and real-world examples
- Case studies and real-world examples
- MLOps in computer vision and natural language processing
- MLOps in recommendation systems and time series forecasting
- MLOps in healthcare and finance
Module 7. Self-Assessment and Project: Project evaluation and feedback
- Self-assessment and project planning
- Project implementation and execution
- Project evaluation and feedback
- Final project presentation and review
Course Features
- Interactive and engaging: Hands-on exercises, quizzes, and projects to keep you engaged and motivated
- Comprehensive and up-to-date: Covers the latest MLOps tools and technologies
- Personalized and flexible: Self-paced learning with lifetime access to course materials
- Practical and real-world applications: Case studies and examples from various industries
- High-quality content and expert instructors: Taught by experienced professionals in the field
- Certification and recognition: Receive a certificate upon completion, issued by The Art of Service
- Community-driven and supportive: Join a community of learners and professionals in the field
- Actionable insights and hands-on projects: Apply your knowledge and skills to real-world projects
- Bite-sized lessons and progress tracking: Track your progress and stay motivated with bite-sized lessons
- Gamification and friendly competition: Engage in friendly competition and gamification to stay motivated
- Mobile-accessible and user-friendly: Access course materials on-the-go with our mobile-friendly platform