MLOps 101: Introduction to Machine Learning Operations
Completed by Regan Schutte on May 4, 2025
Verified learner ·
Badge ID: jQhbuVMERHSIW7lTBLR0kw
About this Badge
This path covers the foundations of MLOps, including its role in the ML lifecycle and how it differs from DevOps. Learn key MLOps standards, explore machine learning pipelines from data collection to deployment, and understand the importance of version control. Ideal for those looking to operationalize ML workflows effectively. Disclaimer: The tools and workflows in this content may vary from Target’s setup and usage. For implementation details and guidance, visit go/MLOps for the latest info.
Learning objectives
- Define MLOps and its importance in the machine learning lifecycle. Identify key differences between traditional DevOps and MLOps. Understand Target MLOps Standards/Best Practices #working-group-mlops
- Describe the components of a typical machine learning pipeline. Explain the stages of data collection, preprocessing, model training, and deployment.
- Understand the importance of version control in MLOps. Learn how to use tools like Git for versioning data and models.
- Introduce the concepts of monitoring and logging in machine learning models. Learn to set up basic logging for model performance and data drift.
Skills learned
- Applied Machine Learning
- Exploratory Data Analysis
- Ethical Standards And Conduct
- Statistical Hypothesis Testing
- Natural Language Processing
- System Design and Implementation
- Software Development Life Cycle
- Google Cloud Platform
- Verification And Validation
- Linear Algebra
- Dimensionality Reduction
- Issue Tracking
- Generative AI
- Applied Mathematics
- Continuous Deployment
- Statistical Methods
- Application Deployment
- Continuous Delivery
- Technology Solutions
- Continuous Integration
- Test Case
- Performance Metric
- Predictive Modeling
- Version Control
- Predictive Analytics
- Unit Testing
- Supervised Learning
- Performance Tuning
- Software Development Tools
- Continuous Monitoring
- Risk Mitigation
- Stakeholder Communications
- Technical Documentation
- Python Programming
- Deep Learning
- Unsupervised Learning
- Regression Analysis
- System Monitoring
- Technology Strategies
- Feature Engineering
- Data Collection
- Data Validation
- Machine Learning
- Software Versioning
- Kubernetes
- Data Cleansing
- Data Manipulation
- Algorithms
- Jenkins
- Jupyter
- OpenShift
- Containerization
- Debugging
- GitHub
- NumPy
- Data Processing
- Data Science
- Operational Analysis
- Business Intelligence
- Performance Testing
- Software Documentation
- Systems Integration
- Data Maintenance
- Data Pipelines
- Data Transformation
- DevOps
- Production Planning
- Systems Architecture
- Business Operations
- Business Strategy
- Data Modeling
- Business Analytics
- Automation
- Data Quality
- Technical Management
- Data Analysis
- Cloud Management
- Data Management
- Git (Version Control System)
- Artificial Intelligence and Machine Learning (AI/ML)
- Classification And Regression Tree (CART)
- Infrastructure as Code (IaC)
- Data-Driven Decision-Making
- CI/CD
- Docker (Software)
- Machine Learning Methods
- MLOps (Machine Learning Operations)
Learning content completed
Some of the content and skills presented have been contributed by the content provider.