Cesar de la Torre
Principal Program Manager at the Azure team.
Latest posts
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 application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. In terms of ML.NET this is awesome for many scenarios like data exploration, data cleaning, plotting data charts, documenting model experiments, l...
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' and ML.NET Image Classification, Object Detection and Text Analysis are probably the most common tasks in Deep Learning which is a subset of Machine Learning. However, for this blog post I am only focusing on Image Classification/Recognition and the multiple approaches you can take ...
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 TensorFlow model to train on top of it which was originally trained with similar image types to your new images. Here's some specific examples to understand that statement: In any case, it can get tricky and you need to understand some complexity. For some custom scenarios...
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 it additionally embraces the 'Machine Learning Model Lifecycle'. Extending Azure DevOps CI/CD pipelines with the ML Model lifecycle When deploying ML models to production, you need to automate the process to track, version, audit, certify and re-use every asset in your ML...
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 interesting, though. This is the first main milestone of a great journey in the open that started on May 2018 when we released ML.NET 0.1 as open source. Since then we've been releasing monthly, 12 preview releases plus this final 1.0 release, as shown in the roadmap below: ...
How to optimize and run ML.NET models on scalable ASP.NET Core WebAPIs or web apps
Context ------ UPDATE on May 13th 2019: The recommended way to deploy/run an ML.NET model into ASP.NET Core web apps or WebAPI services is by using the 'Microsoft.Extensions.ML' Integration package. Read about it in this tutorial: - Deploy an ML.NET model in an ASP.NET Core Web API The tutorial above uses optimized code based on an .NET Core Integration Package comparable to integration packages targeting Entity Framework, SignalR, etc. so it's transparent and a lot easier for you than the explained blog post below. This article below was written before that mentioned integration package was creat...
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 Docker containers. Why should you use API Gateways instead of direct communication? In a microservices architecture, the client apps usually need to consume functionality from more than one microservice. If that consumption is performed directly, the client will need to handle ...
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 producción, de forma independiente. En paralelo, las empresas actualmente están descubriendo como con contenedores Docker pueden reducir costes, resolver problemáticas de despliegues y en definitiva mejorar DevOps y operaciones de despliegues a producción. Docker se esta convirtiendo...
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 you need or you can even make sure that it runs a single instance of that microservice process/container. From a generic point of view, in .NET Core we called these type of tasks as Hosted Services, because they are services/logic that you host within your host/application/...