One of my favorite films that depicted the power of Machine Learning (ML) for predictive analysis is Moneyball (2011). In the film, Brad Pitt who plays Billy Beane, the Manager of the Oakland A’s, hires a Yale Economics graduate who uses computer-generated analysis of historical data and predictive modeling to create a champion baseball team.
Managers that want to create a champion business are quickly realizing that to gain a competitive advantage, infusing AI into their processes is a must. Machine Learning isn’t a buzzword any longer.
Your data sources are spread far and wide, much of it in silos, some in data lakes and some residing on legacy systems, thus presenting many hurdles to achieve the benefits of ML. Data, regardless from where it originates needs to be unified, so when you have your ML or deep learning model built, it can be operationalized, and return the real time insights you need to stay competitive.
The far-reaching benefits of AI are empowering businesses across industries to increase accuracy and make smarter impactful data-driven decisions as they leverage instantaneous deep insights.
These decisions are wide-ranging and can impact and redefine the business in highly diverse areas such as reducing churn, optimizing online campaigns, personalizing offers, risk analysis, fraud detection and a whole lot more.
The Benefits of Unifying AI and ML with Transactional Processing
Operationalizing your ML Models answers the glaring question on how you can capitalize on all the data at your disposal in applying the ML models you created. The ability to unify AI and ML transactional processing not only simplifies and shortens ETL processes. Moreover, it also eliminates unnecessary data duplication and prevents data ingestion bottlenecks from your various data sources.
As most companies can attest, the difficulties they experience in tapping into the potential insights that ML can deliver are quite daunting. Specific steps must be followed, but most companies do not have the necessary infrastructure or resources to reach these levels of insight.
Let’s take a look at a case example of how operationalizing ML can benefit Financial Services.
Predicting Stock Prices with InsightEdge
You can implement a ML based stock prediction application by:
- Leveraging sentiment analysis on unstructured data from multiple news sources and social media
- Correlating the news articles with the correct stock ticker
- Using the historical data of the stock to enrich the predictive accuracy
The prediction will provide you with insight on how the stock will behave in within the next minutes.
The following technical challenges must be addressed in order to implement the application:
- The need to ingest in real-time unstructured data streaming from over 30,000 of sources
- Ability to process 100s of thousands of trades in sub-second latency
- Support reinforced learning for ML model
- Execute predictions at sub-second latency
The following diagram shows the conceptual data flow using InsightEdge platform to meet the solution requirements:
Figure 1 : Conceptual data flow using InsightEdge platform
In depth data visualizations with Tableau empower Traders and Business Analytics Managers to explore data interactively and quickly understand the predictions for smarter actions with a real-time view.
Figure 2 : Data visualizations with Tableau
- Increase in trading margins
- Reduce trade order response time
- Handle peak events such as Brexit, elections etc.
Below are the steps that demonstrate how to build and train your ML model:
Load the dataset
Prepare the dataset for training and validation