Nothing is stopping you from taking your APIs to the next level!
Many enterprises adopt API strategies to build business models that can innovate and keep up with market demands.
Benefits of APIs include external integrations, exposing services internally and externally and making data sharing easy and secure.
If you use API management solutions such as Azure API Management, Apigee, WSO2 API Manager or Mulesoft, you might enjoy the benefits of orchestrating APIs through a single layer. But, there might be difficulties with CI/CD and DevOps patterns with multiple Microservice teams consuming a shared API Management layer. It is crucial to make the API publishing process painless so that dev teams can use it efficiently.
Kodez has perfected API Management publishing solutions for customers like Spotless and JBHiFi. We can integrate our automation solutions into the current DevOps process. If you are an Azure customer using services such as Azure functions and AKS, along with Azure API Management, we can help leverage more security benefits.
With Kodez on board, you can look forward to faster deployments, enhanced security and improved developer productivity. This is how we bring you closer to delivering customer value fast.
If you are yet to adopt API oriented information sharing patterns, now is the time! Combine the preferred API Management tool(s) with the Kodez solution, and can leverage the full potential of APIs and make the technology stack more agile. Nothing is stopping you from taking your APIs to the next level!
Treating your infrastructure as code is becoming more and more necessary these days. Writing these instructions becoming challenging too. In Azure we use ARM templates to define the resources and associate them with a deployment pipeline. But ARM templates are quite complicated and they are not everybody’s cup of tea.
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.
