Merci d'avoir envoyé votre demande ! Un membre de notre équipe vous contactera sous peu.
Merci d'avoir envoyé votre réservation ! Un membre de notre équipe vous contactera sous peu.
Plan du cours
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
Pré requis
- Knowledge of machine learning algorithms and libraries
- Strong command of 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
21 Heures
Nos clients témoignent (2)
l'écosystème ML comprend non seulement MLFlow mais aussi Optuna, hyperops, docker et docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Formation - MLflow
Traduction automatique
J'ai apprécié de participer à la formation Kubeflow, qui s'est déroulée en ligne. Cette formation m'a permis de consolider mes connaissances sur les services AWS, K8s et tous les outils DevOps associés à Kubeflow, qui sont les bases nécessaires pour aborder correctement le sujet. Je tiens à remercier Malawski Marcin pour sa patience et son professionnalisme dans la formation et ses conseils sur les meilleures pratiques. Malawskiaborde le sujet sous différents angles, avec divers outils de déploiement Ansible, EKS kubectl, Terraform. Maintenant, je suis définitivement convaincu que je m'oriente vers le bon domaine d'application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Formation - Kubeflow
Traduction automatique
