Real-world success with continuous modernization
Take a look at three companies who invested in continuous, holistic modernization with remarkable success.
Take a look at three companies who invested in continuous, holistic modernization with remarkable success.
This blog post will show you how to approach and think about pricing throughout your cloud adoption journey.
Sharing insights on technology transformation along with important updates and resources about the data, AI, and digital application solutions that make Microsoft Azure the platform for the era of AI.
The joint solution of GitHub and Azure, combined with the power of Visual Studio, can create order for previously staggered workflows and delayed deployments.
By connecting customers with our 90,000+ partner ecosystem, we want to enable organizations in every industry to leverage the Microsoft Cloud as the best foundational investment for bringing their biggest opportunities to life.
Welcome to Microsoft Build 2023—the event where we celebrate the developer community. This year, we’ll dive deep into the latest technologies across application development and AI that are enabling the next wave of innovation.
During the last two years navigating changing economic climates and a global pandemic that shifted the way we work, we’ve learned that teams can continue to collaborate together productively and effectively in remote and hybrid settings. A recent Microsoft study shows that hybrid work works.
We’re excited to share that Microsoft has been recognized as a Leader in the IDC MarketScape Worldwide Machine Learning Operations (MLOps) Platforms 2022 Vendor Assessment.
The growing adoption of data-driven and machine learning-based solutions is driving the need for businesses to handle growing workloads, exposing them to extra levels of complexities and vulnerabilities.
The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your MLOps model.
Robustness is the ability of a closed-loop system to tolerate perturbations or anomalies while system parameters are varied over a wide range.
Testing is an important exercise in the life cycle of developing a machine learning system to assure high-quality operations.