If you have an interest in machine learning, you are not alone. Buzz surrounding this new technology has been growing steadily throughout the years, and many companies are acutely aware of how potentially impactful machine learning (ML) technologies can be for their businesses.
Despite the high level of interest in ML, most of the business world is still in the early phases of investigation and adoption. The learning curve can be steep, and here are many hurdles for companies to overcome if they want to successfully implement machine learning in their organizations.
Here are a few suggestions that can help your organization move through the adoption process and start driving value from machine learning:
1. Introduce New Roles
With this new technology should come new roles. There is significant effort required to learn and adopt ML technology, and it will require full time resources. These new roles will be occupied with people who specialize in building and deploying various machine learning models.
Today, upwards of 40% of companies that use machine learning have at least one dedicated machine learning engineer. In addition to this technical role, an individual is needed to drive the ML adoption process inside the company by communicating value, and identifying specific applications that would benefit by adopting ML.
2. Implement New Metrics
Your organization must implement specific machine learning metrics to track progress and measure success.
Some teams will hire dedicated engineers to establish and track ML metrics. This can be particularly effective when measuring the process of building your ML infrastructure. Business oriented metrics can be adopted and measured by an existing in-house team once the ML process is up and running. As with any new technology, creating an accurate ROI model of your machine learning adoption is a process to develop over time.
3. Approach Development Differently
When it comes to machine learning, the old developmental processes aren’t always the best. Instead, today’s ML experts recommend taking a broader view of tools and processes to address the unique issues surrounding ML. This can mean evaluating new devops techniques, adopting cloud and container integration tools for deployment, and incorporating software stacks built specifically for ML. There are many new tools designed to streamline the process of model deployment and operation, and these should be evaluated in the context of your company’s current infrastructure and development models.
4. Align the Tech With User Goals
Driving the adoption of ML depends on how well it can be aligned with every user’s needs. This not only means that a company must pull the correct data to address user needs, but also that the data must be presented in the most intuitive way possible. Remember that each user’s end goals and needs vary, and implementing the right solution requires being information-driven.
In many cases, the software for information collection and presentation will be the major portion of the ML deployment effort. Defining the ML data presentation models as early as possible in a project will have a strong influence on your project success.
The Future of Machine Learning
As organizations begin to implement machine learning, they can encounter a number of challenges. Hopefully the suggestions in this guide can help companies anticipate and plan for a few of these challenges.