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5 on 5: Demystifying Machine Learning in the Enterprise

29 Jul

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Machine learning is become one of the most important trends in the next generation enterprise data solutions. The evolution of machine learning platforms as well as complementary technology movements such as big data has lowered the entry point for organizations embracing machine learning models to drive more effective business intelligence.

Despite the remarkable technological advances in the last few years, enterprise machine learning remains surrounded by strong myths. We regularly encounter those myths during our work with large enterprises around the world implementing data science and machine learning solutions. This brief article is our attempt to demystify some of the most common misconceptions about enterprise machine learning and also takes a look at the technology stacks that are helping to democratize machine learning in the enterprise.

5 Myths of Enterprise Machine Learning

Implementing Machine Learning is Expensive

If you were implementing a machine learning solution a few years ago, you were stuck with commercial packages ranging on the high six figures to low seven figures that also require a lot of professional services to be implemented. Consequently, there is a myth that machine learning implementations need to be unreasonably expensive. The last few years have seen an emergence of a new group of platforms that have helped to commoditized the price of machine learning platforms while also lowering the entry point for developers and architects looking to implement these types of solutions. Today, it is possible to get up and running with a machine learning solution in a few weeks without spending anything on software licenses.

Is Impossible to Build In-House Expertise in Machine Learning

A side effect from the previous myth. Machine learning has been traditionally seen as a professional services intensive endeavor. While it is true that an organization could benefit from starting their machine learning journey accompanied by the right experts, it is also true that today machine learning platforms provide a low entry point for developers and architects looking to work on the next generation data analytics solutions. In that sense, it is factually possible for an enterprise to start building machine learning knowledge in house while leveraging an expert firm to help them take the initial steps in that journey.

We Need Data Scientists

Machine learning is typically seen as a disciplined practiced by introverted data scientists or statisticians who wear thick glasses and are the only people capable to reasoning through machine learning data and algorithm. This myth couldn’t be further from the truth. Most modern machine learning platforms includes dozens of well understood algorithms that can be enabled with minimum level of effort.

Machine Learning is About Predictions

People mistakenly associate machine learning with data predictions. While predictive analytics is certainly a popular disciplined in the machine learning space is far from covering the entire value of machine learning solutions. Classification, clustering, regression algorithms are incredibly useful to help enterprises extract value from data assets and they are typically simpler to implement than predictive models.

We Need a Big Data Infrastructure to Implement Machine Learning

The recent evolution of machine learning platforms was, arguably, catalyzed by the explosion in big data technologies. Consequently, many organizations feel they are not ready to take advantage of machine learning until they can implement a proper big data infrastructure. While leveraging big data infrastructure brings certain advantages, modern machine learning platforms work effectively against traditional enterprise relational data stores and data warehouses.

5 Technologies that Simplify Enterprise Machine Learning

Azure Machine Learning (http://azure.microsoft.com/en-us/services/machine-learning/)

Azure native cloud-based predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Azure ML provides a visual environment to create ML models as well as an API model to access the models programmatically. Azure ML also allows a developer to use languages like R or python in their ML models.

AWS Machine Learning (http://aws.amazon.com/machine-learning/ )

Similar to Azure ML, AWS ML Service provides a series of tools and algorithms that allow developers to start building and using machine learning solutions without a heavy investment on infrastructure.

Spark MLib(https://spark.apache.org/docs/1.2.1/mllib-guide.html)

The incredibly popular Spark platforms includes a very simple model to execute machine learning algorithms using MPP scale. Interestingly enough, Spark and AWS is now fully supported in Azure and AWS which makes it an interesting complement to the native machine learning engines included in those platforms.

Scikit Learn(http://scikit-learn.org/)

One of the most powerful ML frameworks in the world. Scikit learn provides a series of python based libraries that include over 50 ML algorithms and has a very vibrant community behind it.

Mahout (http://mahout.apache.org/)

Even though Mahout has seen its popularity eclipsed by the raise of new machine learning platforms, it remains incredibly relevant when comes to evaluating machine learning solutions in the enterprise. Mahout provides a large gallery of machine learning algorithms optimized to work in Hadoop infrastructures.

Other Platforms

The aforementioned technologies form a core group of platforms that are actively driving machine learning adoption in the enterprise. Like any other fast growing technology space, we are seeing an increasing number of platforms that are bringing new and innovative capabilities to enterprise machine learning solutions. Consequently, the previous list is likely to increase in the next few months but it is a good place to start today.

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Posted by on July 29, 2015 in Uncategorized

 

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