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Training
Course Information
Course Name
MLO: Machine Learning Operations
Duration
3 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
The “MLOps Basics” course provides a comprehensive introduction to the principles and practices of Machine Learning Operations (MLOps). Participants will explore the entire ML lifecycle, from data preparation to model deployment, emphasizing collaboration between data scientists and operations teams. This course is designed to equip learners with the foundational skills necessary to implement MLOps in real-world scenarios
Overview
Overview
The “MLOps Basics” course provides a comprehensive introduction to the principles and practices of Machine Learning Operations (MLOps). Participants will explore the entire ML lifecycle, from data preparation to model deployment, emphasizing collaboration between data scientists and operations teams. This course is designed to equip learners with the foundational skills necessary to implement MLOps in real-world scenarios
Audience Profile
Audience Profile
This course is suitable for:
- Data Scientists: Professionals seeking to enhance their understanding of operationalizing ML models.
- Machine Learning Engineers: Individuals looking to improve their skills in deploying and maintaining ML models.
- DevOps Engineers: Those interested in integrating machine learning into existing DevOps practices.
- Students and New Graduates: Individuals entering the field of machine learning who want a solid grounding in MLOps concepts.
Prerequisities
Prerequisites
Participants should have:
- A basic understanding of machine learning concepts and algorithms.
- Familiarity with programming languages commonly used in ML, such as Python.
- Knowledge of version control systems, particularly Git, will be beneficial but is not mandatory.
At Course Completion
At Course Completion
By the end of this course, participants will be able to:
- Understand the stages of the ML lifecycle using the CRISP-DM framework.
- Implement effective feature engineering techniques to enhance model performance.
- Utilize AutoML frameworks for automating various aspects of ML development.
- Deploy ML models using web containers and manage virtual environments for seamless development.
- Employ Git for version control in ML projects and track experiments using DVC (Data Version Control).
- Execute end-to-end workflows with MLFlow, ensuring proper model tracking and versioning.
- Conduct testing on ML models to ensure reliability and performance before deployment.
- Analyze real-world MLOps use cases to understand best practices in production environments.
This structured approach ensures that learners not only grasp theoretical concepts but also gain practical experience applicable to their professional roles in machine learning and data science
Course Outline
Course Outline
Module 1: Introduction to MLOPs
- Definition and Importance: Understanding MLOps and its role in bridging data science and operations.
- MLOps vs. DevOps: Key differences and similarities between MLOps and traditional DevOps practices.
- MLOps Maturity Levels: Overview of different maturity levels in MLOps implementation.
Module 2: ML Life Cycle Stages with CRISP-DM
- Overview of CRISP-DM: Introduction to the Cross-Industry Standard Process for Data Mining.
- Phases of the ML Life Cycle:
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
Module 3: Data Engineering & Management
- Data Collection Techniques: Methods for sourcing data from various platforms.
- Data Cleaning and Preprocessing: Techniques for preparing data for analysis.
- Feature Engineering: Strategies for selecting and transforming features to improve model performance.
Module 4: Unsupervised Learning Techniques
- Clustering Methods: Overview of clustering algorithms like K-means, hierarchical clustering, and DBSCAN.
- Dimensionality Reduction: Techniques such as PCA (Principal Component Analysis) and t-SNE.
Module 5: Supervised Learning Techniques
- Supervised Learning Fundamentals: Overview of supervised learning concepts.
- Regression Analysis: Techniques for predicting continuous outcomes.
- Classification Methods: Overview of classification algorithms, including decision trees, SVMs, and neural networks.
