Course Outline
Introduction
Installing and Configuring Machine Learning for the .NET Development Platform (ML.NET)
- Setting up ML.NET tools and libraries
- Operating systems and hardware components supported by ML.NET
Overview of ML.NET Features and Architecture
- The ML.NET Application Programming Interface (ML.NET API)
- ML.NET machine learning algorithms and tasks
- Probabilistic programming with Infer.NET
- Deciding on the appropriate ML.NET dependencies
Overview of ML.NET Model Builder
- Integrating Model Builder with Visual Studio
- Utilising automated machine learning (AutoML) with Model Builder
Overview of ML.NET Command-Line Interface (CLI)
- Automated machine learning model generation
- Machine learning tasks supported by ML.NET CLI
Acquiring and Loading Data from Resources for Machine Learning
- Utilising the ML.NET API for data processing
- Creating and defining the classes of data models
- Annotating ML.NET data models
- Scenarios for loading data into the ML.NET framework
Preparing and Adding Data Into the ML.NET Framework
- Filtering data models using ML.NET filter operations
- Working with ML.NET DataOperationsCatalog and IDataView
- Normalisation approaches for ML.NET data pre-processing
- Data conversion in ML.NET
- Working with categorical data for ML.NET model generation
Implementing ML.NET Machine Learning Algorithms and Tasks
- Binary and multi-class ML.NET classifications
- Regression in ML.NET
- Grouping data instances with Clustering in ML.NET
- Anomaly Detection machine learning task
- Ranking, Recommendation, and Forecasting in ML.NET
- Choosing the appropriate ML.NET algorithm for a dataset and functions
- Data transformation in ML.NET
- Algorithms for improved accuracy of ML.NET models
Training Machine Learning Models in ML.NET
- Building an ML.NET model
- ML.NET methods for training a machine learning model
- Splitting datasets for ML.NET training and testing
- Working with different data attributes and cases in ML.NET
- Caching datasets for ML.NET model training
Evaluating Machine Learning Models in ML.NET
- Extracting parameters for model retraining or inspection
- Collecting and recording ML.NET model metrics
- Analysing the performance of a machine learning model
Inspecting Intermediate Data During ML.NET Model Training Steps
Utilising Permutation Feature Importance (PFI) for Model Predictions Interpretation
Saving and Loading Trained ML.NET Models
- ITTransformer and DataViewScheme in ML.NET
- Loading locally and remotely stored data
- Working with machine learning model pipelines in ML.NET
Utilising a Trained ML.NET Model for Data Analyses and Predictions
- Setting up the data pipeline for model predictions
- Single and multiple predictions in ML.NET
Optimising and Re-training an ML.NET Machine Learning Model
- Re-trainable ML.NET algorithms
- Loading, extracting and re-training a model
- Comparing re-trained model parameters with the previous ML.NET model
Integrating ML.NET Models with The Cloud
- Deploying an ML.NET model with Azure Functions and web API
Troubleshooting
Summary and Conclusion
Requirements
- Knowledge of machine learning algorithms and libraries
- Strong command of the C# programming language
- Experience with .NET development platforms
- Basic understanding of data science tools
- Experience with basic machine learning applications
Audience
- Data Scientists
- Machine Learning Developers
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
