Daily we are inundated with huge amounts of data yet don’t have the time or necessary manpower to generate insights that can make a real impact. Machine learning algorithms are the key for data scientists bridging this gap. The entire machine learning lifecycle consists of a number of phases but it’s the initial steps that are the most crucial. Take note.
For starters, a solid understanding of the data is necessary. Ask yourself, do I have a grip on the data? Is the data too directional? What transformers do I need to apply? Is it labeled or unlabeled? Understanding from the beginning how the data will transform once it’s operational will help move the process forward. Without this level of understanding, the odds of building an algorithm that reaches the desired conclusion will not be in your favor.
Step 1: Clean
This is the most critical step and where 90 percent of the time should be spent. While cleaning does entail making sure that multiple variables are stored in one column or that multiple types of observational units are stored in the same table, it’s also about getting your data ready for training. This is your foundation and where the solid understanding of data that occurred beforehand will come in handy and help to build an accurate model that makes the right inferences within a real-world environment. It’s this step that will ensure your data transforms and learns over time.
Step 2: Build
Once the data has been cleansed, next up is creating the appropriate models so that the machine learning algorithm is able to make intelligent inferences from the data and take into account the new data that comes in. Building a model is easy and there are tools and software available to automate the process. However, never lose sight of what you are building. At all times, a data scientist needs to be able to explain a model. Take for instance you work in a regulated industry like financial services or insurance. If your bank declines a loan application, the individual or organization who applied has the authority to come back and ask what the reasoning is. The answer, “the model made me do it,” will not suffice. You need to be able to interpret the model and explain the decision process the data followed to anyone that asks.
Step 3: Operationalize and Manage
Besides cleaning data, the second most important phase of the machine learning process is operationalizing the model. How do you make sure that the model will fit into an existing 30-40 year infrastructure? This process is incredibly difficult as bringing two disparate technologies built worlds apart is no easy feat. Software exists that can help make this process easier but having a professional to do this step for you is recommended.
I recently worked with the CIO of a large financial institution to put their machine learning algorithm in place. Prior to working with IBM, the data took three days to build and 11 months to operationalize. By implementing more structured tools such as Machine Learning on z Systems, the organization was able to operationalize its model from months to days.
Finally, it’s all about keeping your eyes on the prize. Once models are created, the right algorithm is in place, continuously managing the process will ensure success. Without this upkeep, the algorithm won’t be able to evolve, learn from the varying data sets and will be susceptible to vulnerabilities or flaws that left undetected can cause the model to run very slowly, use too much memory and impact the user experience.
IBM will be exploring this topic further in depth on June 22 at IBM’s The Fast Track Your Data in Munich. The one-day event will feature new solutions, services, and client stories that advance our capabilities in data science and data governance. Be sure to register to view the livestream or to attend in person here.
Feature image via Pixabay.
