In 2014, Microsoft launched Azure Machine Learning Studio for highly skilled data scientists and developers to be able to create ML models. At the time, it was a big breakthrough but Microsoft still looked to improve services. They decided to work with clients to understand their needs and demands. Today, using an easy drag and drop cloud-based service, data scientists and developers of all skill levels can build on any framework, test outcomes, and deploy Azure ML models in a matter of hours.
What is Machine Learning?
Machine learning is a branch of artificial intelligence that focuses on teaching computer systems how to retain information, learn from data, and make decisions. When machine learning algorithms are built into applications, they are able to analyze data then determine the right answer or provide future predictions without being explicitly programmed. ML applications can also improve answers or predictions using new data. Examples of Azure machine learning can be seen in personalized ads, spam email filters, Netflix recommendations and fraud protection.
Inside the Microsoft Azure Machine Learning Brain
While Machine learning has improved the operations of hundreds of industries, building ML into software and applications can be laborious and time-consuming. In order for the machine learning algorithms to produce the desired outcome, developers must input enormous amount of data. Microsoft Azure Machine Learning accelerates that time significantly by having an easy drag and drop set up for predictive analytics and Azure ML models.
Microsoft Azure Machine Learning capabilities:
- Discover the right algorithm and hyperparameters faster
- Build on any framework (Pytorch, TensorFlow, or Scikit-learn)
- Autoscale capabilities to reduce costs
- Increase productivity among team members and manage testing with Azure DevOps
- Easily deploy Azure Machine Learning to cloud
- Integrate any Python environment with Azure Machine Learning including Visual Studio Code, Jupyter notebooks, and Pycharm.
Rolling out Azure ML Services – Platform As a Service (PaaS)
In 2016, Microsoft announced Azure ML Services as a Platform As a Service (PaaS), a cloud computing model. This service joined Microsoft’s AI and ML portfolio including Cognitive Services and Azure Machine Learning Studio making it convenient for users to be able to experiment, test, and manage ML Models. On top of it, the service is based on the Jupyter Notebook app for developing Azure Python SDK and has built in Application Program Interfaces (APIs). Now, users are able to develop data prep, feature engineering, training, testing, hyperparameter tuning, and Azure ML model management all in one place.
Why Hire Pointivity For Machine Learning Services
Microsoft Azure Machine Learning can take your business to the next level and allow your operations to outperform the competition. At Pointivity, our development and IT teams train and support you while building, testing, and managing Azure ML models. We have specialists that customize Azure ML models to meet your desired needs and demands.
Need help creating or implementing Microsoft Azure Machine Learning? We can also help with azure migration, blockchain deployment and intelligent load balancing. Connect with us today to get started.
