Azure Machine Learning Consulting Services

Lead with intelligence to drive radical business outcomes

Azure Machine learning is a Python-based machine learning service and it can be accessed from any Python development environment. It simplifies and accelerates the building, training, and deployment of your machine learning models, seamlessly deploying to the cloud and the edge with one click. With automated machine learning capabilities, data scientists can build models faster. DevOps for machine learning also enables data scientists and developers to enhance productivity with experiment tracking, model management, and monitoring, reduce costs with autoscaling compute and integrated CI/CD, and machine learning pipelines. Models can be deployed and managed in the cloud, on-premises and the edge.

What are the benefits of Azure machine learning?

Increase your rate of experimentation build and train models faster with automated machine learning, autoscaling cloud compute, and built-in DevOp

Use Azure Machine Learning service from any Python environment and with your favorite frameworks and tools.

Deploy models into production faster in the cloud, on-premises, or at the edge.

Seamlessly integrate with the rest of the Azure portfolio. Improve enterprise readiness with Azure security, compliance features, and virtual network support.

Machine Learning and AI Portfolio

What are the capabilities of Azure machine learning?

Azure Machine Learning service integrates with any Python environment, including Visual Studio Code, Jupyter notebooks, and PyCharm

Azure Databricks is an Apache Spark-based big-data service with Azure Machine Learning integration. The two work together well, providing interactive notebooks that enable collaboration between data scientists and data engineers.

Azure Databricks simplifies model development:

Easily collaborate in interactive workspaces
Automate job execution and model version control

Databricks also helps you scale compute resources to meet your advanced analytics needs:

Easily scale up on VMs or scale out on clusters
Autoscale on serverless infrastructure, paying only for what you use in the cloud
Leverage commodity hardware to reduce TCO

Azure Machine Learning Services enables you to quickly determine the right model for your data:

Determine which algorithm is best for your data
Tune hyperparameters to optimize your ML models
Rapidly prototype in agile environments

An R package that provides a light-weight frontend to use Apache Spark from R

Provides a distributed DataFrame implementation that supports operations like selection, filtering, and aggregation—similar to R data frames, dplyr
Supports distributed machine learning using Spark MLlib
R programs can connect to a Spark cluster from RStudio, R shell, Rscript, or other R IDEs

Microsoft is the only company that offers the ability to deploy and manage models, whether in the cloud, on-premises, or even the Edge. This is extremely valuable in disconnected scenarios, where predictions have to be made on the Edge, without connectivity to the cloud. With IoT deployments becoming more widespread, Technology Services and Microsoft are well-positioned to help you innovate with AI wherever you need.

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Accuweather predicts weather impact using cloud-based machine learning.

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