How to Set Your Company Up for Success with Machine Learning
With the growth of machine learning applications, businesses are starting to wonder how they can leverage these algorithms for themselves. Indeed, machine learning has become a bit of a buzzword, the hot new technology trend. Everyone wants a piece.
Unfortunately, wanting to use machine learning and actually deploying an effective solution are very far apart. In fact, VentureBeat found that 87% of data science-based projects never make it into production. Without the right groundwork, companies that try to use machine learning too quickly risk costly errors and projects bound for nowhere.
So, how do you avoid failure and set your data projects up for success? We’ve put together our top insights in this post to get you on the right path.
Laying the Groundwork for Machine Learning
Most companies using machine learning in production today have massive amounts of data, large development teams, and experts on staff to make sure things go right. In addition, they’re willing to spend large budgets on these projects and accept some failures on the way to success.
For most companies, however, that’s not the case. For these smaller or less experienced companies, moving incrementally toward a data-centric approach is the key.
Here are the top steps along the way to effectively using data and, eventually, machine learning.
Clear, Achievable Results
The first challenge is to define the expected result from your project. At first, this should be a simple minimum viable product with a narrow scope. Don’t set out to do machine learning right away. Instead, think of questions that would be relatively easy to answer by analyzing the data you already have.
This point is key because many companies set out with grand ambitions that aren’t realistic. Often, the problem doesn’t fit existing machine learning models or the company doesn’t have enough data for effective training. As the team scrambles to put together a work product that might fit some of the executives’ expectations, morale declines, and budgets skyrocket.
Instead, start small. Find a question that’s easy to answer. Allow your team to explore the data you currently have and provide insights as they go.
Another challenge is that companies often underestimate the resources needed to get machine learning into production.
While it’s fairly easy to get a rudimentary open source machine learning model working, it’s very difficult and time-consuming to fine-tune that model to higher accuracy. Additionally, moving that model to production comes with many challenges for deployment, building data pipelines, and ongoing improvements.
Additionally, these projects can take a while to bear fruit. Be prepared to spend money on developer payroll and computing resources for many months before the project produces its first useful outputs.
Hire the Right People
When it comes to building data pipelines, choosing models, training them on large datasets, and putting it all together in production, experience matters.
Due to surging demand, expect to pay top dollar to get experienced staff on your team that can begin to build out your systems. Crucially, your first hire probably shouldn’t be a data scientist who can choose the machine learning models and calculate higher-level statistics. Instead, focus your first hires on building infrastructure and organizing data. Known as data engineers, these hires typically come from a software engineering background and can help you build a clean, organized data store.
At Intertech we’re biased, but another great option to bring experience onto your team quickly is to hire a data engineering and machine learning consultant to quickly get you up to speed.
Machine Learning First Steps
If you front-load the work, you’ll set your machine learning project up for success. By considering the groundwork, infrastructure, and requirements of your data projects, you’re already ahead of the competition who dive right in without careful planning. Now, you can take advantage of the early wins open to companies that have their data organized and readily available.
Founded in 1991, Intertech delivers software development consulting and IT training to Fortune 500, Government and Leading Technology institutions. Learn more about us. Whether you are a developer interested in working for a company that invests in its employees or a company looking to partner with a team of technology leaders who provide solutions, mentor staff and add true business value, we’d like to meet you.