Course Outline
Introduction
Installation and Configuration of Machine Learning for .NET Development Platform (ML.NET)
- Setting up ML.NET tools and libraries for government use
- Operating systems and hardware components supported by ML.NET for government operations
Overview of ML.NET Features and Architecture
- The ML.NET Application Programming Interface (API) for government applications
- Machine learning algorithms and tasks supported by ML.NET for government use
- Probabilistic programming with Infer.NET in a government context
- Deciding on the appropriate ML.NET dependencies for government projects
Overview of ML.NET Model Builder
- Integrating the Model Builder into Visual Studio for government development
- Utilizing automated machine learning (AutoML) with Model Builder for government applications
Overview of ML.NET Command-Line Interface (CLI)
- Automated machine learning model generation using ML.NET CLI for government projects
- Machine learning tasks supported by ML.NET CLI for government use
Acquiring and Loading Data from Resources for Machine Learning in Government
- Utilizing the ML.NET API for data processing in government applications
- Creating and defining data models for government datasets
- Annotating ML.NET data models for government use
- Cases for loading data into the ML.NET framework for government operations
Preparing and Adding Data Into the ML.NET Framework for Government Use
- Filtering data models using ML.NET filter operations for government projects
- Working with ML.NET DataOperationsCatalog and IDataView in government applications
- Normalization approaches for ML.NET data pre-processing in government contexts
- Data conversion in ML.NET for government datasets
- Working with categorical data for ML.NET model generation in government use
Implementing ML.NET Machine Learning Algorithms and Tasks for Government Applications
- Binary and multi-class classifications using ML.NET for government projects
- Regression analysis with ML.NET for government datasets
- Grouping data instances with clustering in ML.NET for government use
- Anomaly detection machine learning tasks in a government context
- Ranking, recommendation, and forecasting using ML.NET for government applications
- Choosing the appropriate ML.NET algorithm for government datasets and functions
- Data transformation techniques in ML.NET for government use
- Algorithms for improving the accuracy of ML.NET models in government projects
Training Machine Learning Models in ML.NET for Government Use
- Building an ML.NET model for government applications
- Methods for training a machine learning model using ML.NET in government contexts
- Splitting data sets for ML.NET training and testing in government projects
- Working with different data attributes and cases in ML.NET for government use
- Caching data sets for ML.NET model training in government applications
Evaluating Machine Learning Models in ML.NET for Government Use
- Extracting parameters for model retraining or inspection in government contexts
- Collecting and recording ML.NET model metrics for government applications
- Analyzing the performance of a machine learning model in a government context
Inspecting Intermediate Data During ML.NET Model Training Steps for Government Use
Utilizing Permutation Feature Importance (PFI) for Model Predictions Interpretation in Government Applications
Saving and Loading Trained ML.NET Models for Government Use
- ITTransformer and DataViewScheme in ML.NET for government projects
- Loading locally and remotely stored data for government applications
- Working with machine learning model pipelines in ML.NET for government use
Utilizing a Trained ML.NET Model for Data Analyses and Predictions in Government Applications
- Setting up the data pipeline for model predictions in government projects
- Single and multiple predictions using ML.NET in government contexts
Optimizing and Re-training an ML.NET Machine Learning Model for Government Use
- Re-trainable ML.NET algorithms for government applications
- Loading, extracting, and re-training a model in government contexts
- Comparing re-trained model parameters with previous ML.NET models in government projects
Integrating ML.NET Models with the Cloud for Government Use
- Deploying an ML.NET model with Azure functions and web API for government applications
Troubleshooting for Government Applications
Summary and Conclusion for Government Use
Requirements
- Proficiency in machine learning algorithms and libraries
- Strong command of the C# programming language
- Experience with .NET development platforms
- Basic understanding of data science tools for government use
- Experience with fundamental 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.
