The end-to-end Machine Learning and Artificial Intelligence lifecycle
Have More Questions?Streamline your Machine Learning models from building, training, and deployment. With this Python-based service, we can help you bring Machine Learning models to the market with the tools and frameworks of your choice. Our services give you benefits such as increased productivity, secured processes, and a platform that grows with you.
Lead with intelligence to drive radical business outcomes
Azure Machine Learning is at the cutting edge of computer science research. Azure allows machines to recognize patterns over large datasets providing benefits to industries of all sizes. Our Azure consultants focus on using these benefits to create accurate, useful, and maintainable models that drive consistent business results.
Dynamic Consultants Group has worked in projects ranging from categorization and image recognition to predicting customer needs based on historical data. The experts on our consulting and development team have abundant experience in this rapidly growing field. No matter if you’re starting a project from scratch or supplementing existing projects with senior expertise, we can help!
Use powerful AI models to deliver transforming experiences. You can find these models in widely used products like Office 365, Bing, and Xbox. Get started with Azure Cognitive Services and Azure Bot Service simultaneously .
Build and deploy machine learning models from anywhere. Scale on-demand with direct access to the cloud and tools you need. Your team gains immediate access to automated machine learning capabilities supporting no-code or open-source code models.
DevOps for machine learning, or MLOps, streamline the end-to-end lifecycle, from data preparation to deployment and monitoring. Increase efficience and simplify your workflows by using machine learning pipelines. Take advantage of continuous integration and continuous delivery (CI/CD) for ease of support and maintenance while improving model quality over time. Manage your model artifacts from a central portal, and monitor the performance of deployed models.
By improving forecast using Azure Machine Learning automated ML, we can reduce waste and ensure pizzas are ready for our customers. This will reduce the guesswork for our operators and allow them to spend more time focusing on other aspects of store operations. Rather than guessing how many pizzas to have ready, store operators are focusing on making sure every customer experience is an excellent one.
Anita Klopfenstein
CEO, Little Caesars Pizza
The service is generally available in several countries/regions, with more on the way. With 54 announced regions, more than any other cloud provider, Azure makes it easy to choose the datacenter that’s right for you. Microsoft provides more information on Azure global infrastructure, here.
SLA, or service-level agreement, is part of your Microsoft volume licensing agreement. This applies to Microsoft Online Services, but does not apply to separately branded services. The SLA for Azure Machine Learning service is 99.9 percent.
The Azure Machine Learning service workspace is the top-level resource for the service. It provides a centralized place to work with all the artifacts you create. The major components of the service is broken up into five parts – train, package, validate, deploy, and monitor.