Mihai Tataran is the General Manager of Avaelgo, he is also a Microsoft MVP on Microsoft Azure and Microsoft Regional Director.
During the last years Mihai has been working on building Avaelgo's Managed Services business, now being an award-winning Microsoft Gold Partner on Cloud Platform. His main focus is designing products and services on top of the Cloud, helping the Sales processes and also working as a consultant on enterprise projects.
Mihai is regular speaker at international conferences: DevReach in Bulgaria, Codecamp Macedonia, Microsoft TechEd, Microsoft Summit; he is also involved in organizing ITCamp Romania.
In this presentation you will see how to design and implement the Strategy for migrating to the Microsoft Cloud (Azure, Microsoft 365), using the Cloud Adoption Framework developed by Microsoft and used by our company in multiple projects.You will also understand how Governance of IT resources in the Cloud is different than on premises. We will discuss aspects like: resources security, cost monitoring and control, performance optimization and scalability improvements, policies and compliance - all with examples on Microsoft Azure.
In todays world, more and more workloads are moving to the Public Cloud, including old-ish applications, designed and coded many years ago with different architecture styles than today.
In this session I will focus on how to migrate an application to Azure, by rearchitecting it so it really benefits from the public cloud functionality. We will go through some concepts of what "Cloud Native" means, but will focus on the Microsoft-designed "Well Architected Framework" to show you how to design the application and the actual migration process so your app in Azure is: performant and scalable, you have minimum time to market (e.g. fast updates), highly available and with a disaster recovery procedure in place, etc.
Predicting Balance Sheet items and Cash Flow is a major challenge for today's CFOs around the world, and it became exceptionally important during a cataclysmic event like the Coronavirus pandemic. They have been struggling with such topics for years, and most of the implementations we've seen are something very naïve like extrapolating on past data.
In this session I will show how to approach such a timeseries prediction project (setup, tools, methodology), what are the common obstacles you need to surpass (data integrity and format, interpreting data, choosing what data to consider and how), how to run models training at scale using AutoML and when and what to customize for better prediction.
This presentation is based on the work we've done for a few large enterprise customers (Oil&Gas and other industries), helping them predict Balance Sheet items and Cash Flow.