The Pathway to Machine Learning in Federal

Many federal agencies are now on the path to understanding how machine learning can apply to their specific needs in predictive analysis for cyber threat detection, automating data breach detection tasks and identifying trends in social media indicative of potential terrorist threats. Every agency can benefit from applying some element of machine learning to advance their mission.

Adopting machine learning can also mean adopting new perspectives on data security, data engineering and data-based decision-making. For agencies exploring the transition from understanding to implementation, even knowing where to get started can be a challenge. The following steps help chart a successful path when embarking on a machine learning journey.

Start with Security

Like any systems integration project, it is critical to include security considerations into the initial requirements and design process. Questions specific to data are important. For example:

• Who can see the data?
• How do I change data access rights?
• Can I integrate with Active Directory/Lightweight Directory Access Protocol solutions?
• Can I anonymize data at the row or cell level?
• Can I share my data, algorithms, and project results with other data scientists and then change access controls when needed?

Answers to questions around data management, data security, governance, and lineage are fundamental before starting any machine learning project.

Focus on the Mission

Understand exactly what problem you are trying to solve. Choose a technology to fit the problem, not the other way around. Frame the question to maximize value from the technology.

Need Data, Will Travel

One of the biggest challenges to any machine learning project is getting access to data sets. This often requires getting access from multiple data owners, as well as different data types. Data owners must be willing to share data and participate in machine learning projects.

In its simplest form, machine learning is based on algorithms to identify trends based on historical data and then make predictions. Better data, better algorithms, better insights.

I finally have the data, now what?

After obtaining the data set, it is time to fully understand the data. Engage domain experts, data scientists, and developers. Understand your data so that you can engineer it to maximize its usefulness. This might require adding new data elements, merging multiple data sources, conducting data analysis, and beginning "feature" engineering. In machine learning, a feature is an individual attribute or "explanatory variable." It takes time and domain expertise to identify specific, independent features in your data. Knowledge of the data is key to selecting appropriate features to make algorithms successful. After features are selected, start training and refining the model.

Manage the Data

Gone are the days when everyone moves data to a single data warehouse or data lake or Hadoop ecosystem. Having a control layer in place makes it easier to pull data from multiple sources and make changes, especially as it relates to data access and data sharing.

• Leverage the legacy data stores that you have, and then manage both data and interactions (connectors) to accelerate access to the data.
• Eliminate manual checkpoints to optimize the feedback loop between the model outputs and the overall enterprise.
• Ensure policies are in place and enforced for compliance and security.

Communicate Insights

Remember the use case. Avoid the “science project syndrome" and stay focused on answering the initial question and identifying actual insights that can be gained from the model. Find ways to communicate these insights. Many tools provide visualization methods to make this easier.

Make Models Production Ready and Sustainable

Operationalize by moving from proof of concept to production quickly. Once environments and control layers are in place, continue to add use cases and more data sets.

Enabling an organization to make the most out of machine learning and data science requires a long-term commitment to build talent and grow skills over time. Entering the machine learning space might require a shift in skill set from operational analytics to predictive analytics.

Recognize that a culture shift might be required as leadership needs to recognize the importance of making decisions based on data insights as opposed to gut emotions. Encourage data sharing and welcome collaboration.

Maintain your models. Data changes over time. Trends change over time. Building accurate, predictive models is an ongoing effort. Develop a plan to track your model's performance and a cycle to update it.

Scott Smith is the managing director of federal for Sila.