Semantic Kernel deep dive en

Let's discover Semantic Kernel! Let's go deep into this open-source SDK that makes it easy to combine AI services such as OpenAI, Azure OpenAI and Hugging Face with conventional programming languages such as C# and Python.
Semantic Kernel is an open-source developer SDK that is very lightweight and allows developers to orchestrate AI plugins.
With Semantic Kernel, developers can leverage the same AI orchestration models that power Microsoft 365 Copilot and Bing in their own applications, leveraging existing skills and development investments.

Porta il tuo virtual agent a un altro livello con l'AI en

A volte il miglior modo di interagire con i nostri dati non è una app. Le chatbot offrono un'esperienza utente più naturale e non è mai stato così semplice crearne una da zero con la Generative AI!

In questa sessione vedremo come sfruttare le novità introdotte in Power Virtual Agent per creare chatbot dinamicamente sfruttando tutte le potenzialità di Copilot. Durante le demo vedremo come creare nuovi argomenti usando il linguaggio naturale, come abilitare risposte generative a partire da siti pubblici o dai contenuti presenti nella nostra intranet e molto altro.

ML.NET 3.0 en

ML.NET è un framework di machine learning open source e multipiattaforma per sviluppatori .NET che consente l'integrazione di modelli di machine learning personalizzati nelle app .NET. Ora è alla versione 2.0, vediamo quali sono i punti salienti di questa versione e approfondiamo il codice .NET 7!

Power virtual agent vs Bot Framework en it

A session where we will compare Power Virtual Agent and Microsoft Bor Framework. We will create a virtual agent and discover all the peculiarities of this useful tool for our company.

Power virtual agent vs Bot Framework en it

Una sessione dove metteremo a confronto Power Virtual Agent e Microsoft Bor Framework. Creeremo un agente virtuale e scopriremo tutte le peculiarità di questo utile strumento per la nostra azienda.

Object Detection with ML.NET en

Session where we will talk about ml and object detection. We will use ml.net to detect objects within our images

Understanding MLOps en it

A session in where we will explore through the similarities and differences between DevOps and MLOps. How we can apply DevOps philosophy to a machine learning system?

Machine Learning in ML.NET en it

Session in which I talk about Machine learning, data analysis and ML.NET, the new open source framework that allows you to create customized machine learning solutions that can be integrated into your .NET applications.

Machine Learning in ML.NET en it

Sessione in cui parlero di Machine learning, di analisi dei dati e di ML.NET, il nuovo framework open source che permette di creare soluzioni di machine learning personalizzate e integrabili nelle proprie applicazioni .NET.

ML.NET Model Lifecycle with Azure DevOps en it

Are you a developer? Are you a Architect? You must have the focus about on the application lifecycle! Building, maintaining, and continuously updating the end-user business application. No news here, but if we add also ML, for example a ML.NET model what we must to do? We need a update and extend a new type of lifecycle: Machine Learning Model Lifecycle.

ML.NET Model Lifecycle with Azure DevOps en it

Are you a developer? Are you a Architect? You must have the focus about on the application lifecycle! Building, maintaining, and continuously updating the end-user business application. No news here, but if we add also ML, for example a ML.NET model what we must to do? We need a update and extend a new type of lifecycle: Machine Learning Model Lifecycle.

Understanding MLOps en it

A session in where we will explore through the similarities and differences between DevOps and MLOps. How we can apply DevOps philosophy to a machine learning system?

A session in where we will explore through the similarities and differences between DevOps and MLOps. How we can apply DevOps philosophy to a machine learning system?