Proper governance is a must to optimize cloud deployments.
Like any application infrastructure, cloud services need careful monitoring.
One of the most pressing reasons to do so is cost. If a cloud deployment is causing severe strain on the budget, it defeats the purpose of moving to the cloud. Proper governance is a must to optimize cloud deployments. The teams who work on these setups need to have clear ownership and ensure that the services are not mismanaged and exist for a purpose.
Kodez took on a project with AGL Energy to bring down their cloud costs. The main success factors here were the custom-developed automation capabilities and our experience in Microsoft Azure. We conducted a thorough analysis and found several areas of cost savings in just 3 Azure subscriptions. Within six months, we managed to save AGL approximately $2 million annually in Azure costs. We carried out a similar strategy with MYOB. By optimising their Azure subscriptions, we helped them save $700,000 annually!.
Migrating to the cloud is half the battle won. Keeping teams productive and costs under control complete the story. Don’t let mismanaged resources take away from the hard work you put into getting everything on the cloud. Let us take a deep dive into the architecture and help you save on cost without compromising on output!
Azure MachineLearning Service provides four main compute options each with a specific purpose attached to it. In this post we will go through each of those and see where we can occupy them in your ML experiments.
In the era of Industry 4.0 where data and predictive analytics players the major role, developing machine learning pipelines have become an essential in intelligent application development.
Since instant messaging applications being popular among the users, embedding conversational agents or the Chatbots with such services are essential for better user experience.
