Course Outline
Introduction.
Installing and Configuring Machine Learning for .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 the Model Builder to Visual Studio.
- Utilizing 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.
- Utilizing the ML.NET API for data processing.
- Creating and defining the classes of data models.
- Annotating ML.NET data models.
- Cases for loading data into the ML.NET framework.
Preparing and Adding Data Into the ML.NET Framework.
- Filtering data models for with ML.NET filter operations.
- Working with ML.NET DataOperationsCatalog and IDataView.
- Normalization 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 data set 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 data sets for ML.NET training and testing.
- Working with different data attributes and cases in ML.NET.
- Caching data sets for ML.NET model training.
Evaluating Machine Learning Models in ML.NET.
- Extracting parameters for model retraining or inspecting.
- Collecting and recording ML.NET model metrics.
- Analyzing the performance of a machine learning model.
Inspecting Intermediate Data During ML.NET Model Training Steps.
Utilizing 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.
Utilizing 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.
Optimizing 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 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.
- Proficient command of the C# programming language.
- Experience with .NET development platforms.
- Fundamental 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.
