Cesar de la Torre

Principal Program Manager at the .NET product Group (Microsoft Corp in Redmond, Seattle). Focus on Machine Learning .NET (ML.NET), .NET Core, Microservices based architecture, Docker Containers, Azure services.

Using ML.NET in Jupyter notebooks
Using ML.NET in Jupyter notebooks
I do believe this is great news for the ML.NET community and .NET in general. You can now run .NET code (C# / F#) in Jupyter notebooks and therefore run ML.NET code in it as well! - Under the covers, this is enabled by 'dotnet-try' and its related .NET kernel for Jupyter (as early previews). The Jupyter Notebook is an open-source web ...
Training Image Classification/Recognition models based on Deep Learning & Transfer Learning with ML.NET
Training Image Classification/Recognition models based on Deep Learning & Transfer Learning with ML.NET
                Blog Post updated targeting ML.NET 1.4 GA (Nov. 2019) Note that this blog post was updated on Nov. 6th 2019 so it covers the updates provided in ML.NET 1.4 GA, such as Image classifier training and inference using GPU and a simplified API. Context and background for 'Image Classification', 'training vs. scoring' ...
Run with ML.NET C# code a TensorFlow model exported from Azure Cognitive Services Custom Vision
Run with ML.NET C# code a TensorFlow model exported from Azure Cognitive Services Custom Vision
With ML.NET and related NuGet packages for TensorFlow you can currently do the following: However, in the scenario where you want to train with your own images, the Transfer Learning approach can be a bit complex because even without taking into account the code implementation for transfer learning you'll need to find a base ...
ML.NET Model Lifecycle with Azure DevOps CI/CD pipelines
ML.NET Model Lifecycle with Azure DevOps CI/CD pipelines
As a developer or software architect, you are focused on the application lifecycle – building, maintaining, and continuously updating the end-user business application, as illustrated in the simplified image below: When you infuse AI (such as an ML.NET model) into your application, then your application lifecycle needs to be extended so...
What is ML.NET 1.0 – Machine Learning for .NET
What is ML.NET 1.0 – Machine Learning for .NET
Today, coinciding with //BUILD 2019/ conference, we’re thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I'm providing quite a few additional technical details along with my personal vision that you might find ...
Designing and implementing API Gateways with Ocelot in .NET Core containers and microservices architectures
Designing and implementing API Gateways with Ocelot in .NET Core containers and microservices architectures
We're currently evolving the .NET microservices guidance and eShopOnContainers reference application. One of the most important topics is about the API Gateway pattern, why it is interesting for many microservice-based applications but also, how you can implement it in a .NET Core based microservice application with a deployment based on ...
Microsoft eBook gratuito en Español: “Microservicios .NET – Arquitectura para aplicaciones .NET contenerizadas” – Docker, .NET Core, Kubernetes, Service Fabric, Azure.
Microsoft eBook gratuito en Español: “Microservicios .NET – Arquitectura para aplicaciones .NET contenerizadas” – Docker, .NET Core, Kubernetes, Service Fabric, Azure.
Las arquitecturas basadas en Microservicios están emergiendo actualmente como opciones apropiadas para aplicaciones distribuidas de misión crítica. En una arquitectura basada en microservicios, la aplicación se construye basada en una colección de servicios que deben ser desarrollados, probados, versionados y desplegados en ...
Implementing background tasks in .NET Core 2.x webapps or microservices with IHostedService and the BackgroundService class
Implementing background tasks in .NET Core 2.x webapps or microservices with IHostedService and the BackgroundService class
Background tasks and scheduled jobs are something you might need to implement, eventually, in a microservice based application or in any kind of application. The difference when using a microservices architecture is that you can implement a single microservice process/container for hosting these background tasks so you can scale it down/up as ...