Building enterprise-class machine learning apps using Microsoft Azure

In our earlier post, we introduced the concept of Machine learning (ML) and also some types as well as applications in real world. In the second part of this series, lets peek into how to build Machine learning apps using Microsoft Azure.

What is Azure Machine Learning Studio?

Microsoft breaks down the use of Machine Learning (ML) in simple terms. As they put it, “ML examines large amounts of data looking for patterns, and then generates code that lets you recognize those patterns in new data. Your applications can use this generated code to make better predictions. In other words, Machine Learning can help you create smarter applications.”

Naturally, ML will seem daunting at first, and you may possibly feel like it’s a technology that has no use for your organization but there are a number of applications that make ML easy to use.

The Machine Learning Studio, powered by Microsoft Azure, is a powerful simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. At its core, it’s a cloud service that helps people and organizations execute the machine learning process.

The Microsoft ML solution integrates neatly with open-source technology, and really delivers on the inherent value that is created from all the data that our modern sophisticated tools can generate. It stands to reason that the more data you have available, the more accurate your relevant results are going to be.

Azure enabled Machine Learning and Analytics

Azure Data Platform also known as Cortana Intelligence provides everything you need to transform your organization’s data into intelligent action. Below, we take a look at some key advanced analytics components that are a part of this suite and can help you build enterprise-grade machine learning applications-

1. Azure Machine Learning Studio: Azure ML Studio is a fully managed cloud service that allows easy to build, deploy and shares predictive analytics solutions. It enables to deploy your model into production as a web service that can be called from any device, from anywhere and that can use any data sources.
2. Data Lake Analytics: Azure Data Lake Analytics is a new distributed service in the Azure Data Lake. Built for cloud scale and performance, Data Lake Analytics makes the complex task of managing distributed infrastructure and complex code easy. It dynamically provisions resources and lets you do analytics on exabytes of data.
3. HDInsights: Azure HDInsights can handle any amount of data, scaling from terabytes to petabytes on demand. It is a 100% Apache Hadoop distribution and because of that, HDInsights can process unstructured or semi-structured data from various sources. This helps business to get and analyze new types of data and discover some actionable business insights for competitive advantages.
4. Stream Analytics: Azure stream analytics helps businesses to develop and deploy cost effective solutions with faster approach to acquire new business possibilities from streaming data in real-time. Stream Analytics can query data as it’s collected using an SQL-like language or feed it into machine learning models for analysis.

Choose the Right Partners for Implementing Machine Learning for Your Organization

Working with third-party providers such as CloudMoyo gives organizations the ability to access the incredible power of Machine Learning, without needing to spend vast amounts of money and resources in setting them up.  Choosing the right partners to setup your infrastructure might be the most important decision that you ever make with regard to ML. The CTO of Sift Science, Fred Sadaghiani is quoted in Forbes magazine as saying that “a good machine learning person is a curious person, is somebody who can be creative, is somebody who can take an extremely abstract unclear problem and bring to light clarity around the possibilities.”

Machine Learning can help companies to:

• Analyze historical or current data
• Identify patterns and trends
• Forecast future events
• Embed Predictive Analytics into applications
• Recommend decisions

Leveraging the power of data driven insights should be the goal of all analytics. It needs to produce results. When you add to the insights the predictive ability of the software itself to recommend decisions, then you begin to see the immense potential of machine learning over a period of time.

Conclusion

Machine Learning is a new and complex field. Successes will be hard won, and frustration is likely to be the order of the day. Companies need to look for partners who are determined and who have a relentless drive to seek out new answers and try new methodologies. Passion for this growing field is also a necessity, as well as passion for the industries in which the machine learning solutions are being applied.

Every passing day sees new stories coming to light about the applications around machine learning. Using ML to save on water bill, to boost the rewards for frequent flyer programs, to transform radiology, the list goes on and on. Over the next decade, organizations that have put systems in place and asked the tough questions about what ML can do for them now, stand to be the greatest beneficiaries of this brave new frontier of computer science.

CloudMoyo is a Microsoft Gold Partner that has invested heavily in developing a strong machine learning competency leveraging the Microsoft Azure Data Platform. Using Data Science, Natural Language processing (NLP) Internet scale data management, API and data cleansing/parsing/analysis, we can help your business to identify patterns or trends by analyzing current or historical data with the purpose to forecast future events. While integrating Machine Learning / Artificial Intelligence into business, we will embed predictive analytics into your application that will help in taking future decisions. Contact us today to set up a free consultation and start to reap the advantages from the data that you create.