Could Azure Machine Learning Really Disrupt The Data Science Space By Making Predictive Analytics Ubiquitous?
Yes, Azure Machine Learning will surely be going to accelerate this trend but they are not the only player pushing this forward. IBM Watson Analytics was also announced with the same goals. All the major players are moving to make Big Data more accessible. The Hadoop project is absolutely much on demand and useand the hadoop ecosystem is growing every day.
- Azure ML is an incredible service for data scientists who want to approach the web the right way. Creating complex data science workflows can be implemented in many ways, but data scientists are not the web developers and will always have the problem especially with exposing their result to the real world. Azure ML gives them the possibility to freely create Web Services to access their models, in the cloud, in a scalable way.
- It also allows Normal developers to meet the world of data science without a deep mathematical background, which will actually make the whole process even easier for experienced programmers.
- Machine Learning is still a new idea for the mainstream businesses. The large and sophisticated companies that really need Machine Learning have already hired their own team and built their own tools. The smaller companies that don’t have too much need just aren’t going to invest in this yet. So the market for this service is always kind of squeezed from above and below.
- Azure ML will let you build machine learning workflows in the cloud using drag and drop interface from your browser. These workflows could include modules for ingesting the data from a variety of sources, generating features, trainings, scoring and evaluating ML models. Azure ML also lets you run any R-scripts in addition to a lot of built in state-of-the-art modeling modules.
- If you are an enterprise with a servers in the cloud serving your business and you need to monitor those servers. You will find that there is a service for anomaly detection in Azure marketplace that you can call to track any anomalies the telemetry data from the servers. This scenario can be extended to tracking anomalies in real time sensor data that is flowing into Azure.
The world is moving to where every decision is data driven, and Azure Machine Learning can power a lot of those decisions that would lead to Azure development. Machine Learning is still evolving in the market and sudden competitions are surely going to make it better.
Originally published at laitkorblog.wordpress.com on March 21, 2016.