Machine Learning 101

June 14, 2017

Over the last several months, we have published numerous blogs and articles around the topic of Machine Learning. These articles have talked about the business value it can bring to your organization and how it can impact your daily business life. Now let's take a look at the basics of this hot industry topic....

 

What is machine learning?
Machine learning describes algorithms that can learn from data without having to be explicitly programmed. Improved processing power, better algorithms and the availability of Big Data are the foundation and the reason why machine learning will take enterprise software to a new level now.

 

What’s the difference between machine learning and other terms like Big Data, artificial intelligence, and predictive analytics?
Machine learning describes algorithms that can learn from experience without having to be explicitly programmed. Instead of having a programmer write instructions on how to solve a problem, the computer learns from experience, usually from data. Learning in this context means optimizing the parameters for a machine learning model to solve the task. Machine learning is part of the broader field of artificial intelligence (AI).


Deep learning describes a revival of neural networks. Neural networks take inspiration from the human brain: they consist of small neuron-like computing units resembling the synapses of the brain. These networks can learn complex, non-linear problems from the input data. Deep learning networks
derive their name from their “deep architectures” with several hidden layers. Deep learning networks have led to breakthroughs in several machine learning tasks and are currently the best bet in getting us closer to some of the goals of AI, for example making computers see and understanding language.


Big Data is an umbrella term for technology that can process data with high volume, velocity, and variety, beyond what traditional databases can offer. The availability of Big Data is one of the driving forces behind the progress in machine learning in recent years. But not every aspect of Big Data is
about machine learning. Analytics is concerned with the analysis and interpretation of patterns in data and is a term mostly used in industry. Predictive analytics describe the widely-used analytics methods, where tools or users explicitly train exploratory models on given and well prepared data and features, in order to apply such models on new data to predict the respective pattern classification or values. Moreover, forecasting extends the concept by predicting a time series of values about the future. Many predictive
analytics methods use machine learning to make their predictions.

 

How does machine learning relate to IoT?
The Internet of things (IoT) is the inter-networking of physical devices (“things”) to collect and exchange data. Thus, IoT generates massive volumes of data. This represents a great opportunity for machine learning to turn this data into value-creating assets. For example, in predictive maintenance
machine learning is used to predict machine failure before it happens.

 

Machine Learning and SAP HANA

SAP HANA offers an end-to-end solution for developing and deploying high-value predictive analytics and machine learning programs. The machine learning capabilities in SAP HANA enable data scientists and application developers to build, train, and manage machine learning models, where data is persisted.
 

  • Data Input - Gain support for a variety of data types with the ability to analyze high speed event streams from sensors, and other sources – combine your static and streaming data to build machine learning algorithms that predict in real-time with the ability to scale for large data sets.

  • Machine Learning - Combined with the ability to analyze any type of data and seamlessly integrate with Hadoop, R, and SAS – leverage 90+ native machine learning algorithms, and extension capabilities via open source R integration, or custom develop algorithms in C++ using a SDK.

  • Data Output - Integrate models into apps written on SAP HANA or deployed into SAP HANA Smart Data Streaming for real-time predictions on streaming data. SAP BusinessObjects Predictive Analytics ensures models perform as expected, and can be re-trained as needed.

 

Embrace new technology today
Approyo can help your company deal with the changing world. Our SAP S/4HANA and Big Data solutions address these important questions and more. Let us show you today! Set up a free consultation.

 

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