Demystifying Machine Learning: 3 Tips for Getting Started with ML
Machine learning is a little bit like yoga: we all know what it is, we all know it’s good for us, but few of us understand how to actually do it. There’s a mystery surrounding machine learning that (even if our colleagues won’t admit) leaves many people in the built environment overwhelmed. Still, machine learning is more than just an industry buzzword at this point; it’s a necessity. It helps organizations make more accurate estimates, produce more valuable products, use capital and resources efficiently, identify and prioritize safety concerns, perform schedule and portfolio optimization and so much more. Now more than ever, companies are looking at ways machine learning can make them more efficient. International Data Corporation predicts spending on Artificial Intelligence systems like machine learning will reach $97.9 billion in 2023, more than two and one-half times what was spent in 2019.
It’s clear that machine learning can bring incredible cost- and time-saving opportunities to your business, but it’s difficult to know where to start. The concept can seem large and abstract, especially if you’ve never implemented machine learning solutions before. It’s easy to think to yourself, “this isn’t in my existing set of skills,” and hope someone else in your organization leads the charge. Understanding and implementing machine learning is quickly becoming an invaluable skill in our industry because organizations have to move quickly and change often. It is one of the quickest ways to put distance between you and your competitors.
Trust Your Instincts
At Enstoa, we work with many organizations dipping their toes into the machine learning pond for the first time. In our experience, if you have a hunch that machine learning could optimize areas of your business, it probably can.
One of our clients came to us with a few machine learning hunches that translated into impactful machine learning solutions. For example, their team spends hundreds of hours a year manually entering data received in vendor quotations. They had a feeling that PDFs could be read and populated in the cost system automatically. We helped with the ML. Their hunch was spot-on, and the solution development is underway.
Top 3 tips to get started
If you’re still unsure about how to get started with machine learning, here are three good first steps:
- Get a foundational understanding of what machine learning is and what it can achieve.
Many of us haven’t wrapped our heads around the intricacies of machine learning just yet, but the good news is that there are TONS of resources available online. If you have time to do the research and read all the guides, check out these useful “beginner guides” to machine learning by Bernard Marr and Chris Nicholson to get started. If you prefer to save time and take an interactive course to experience machine learning as you learn about it, check out our Colonnade course, AI: Machine Learning Essentials, to get you up-to-speed on the technique shaping the industry.
- Think backwards to get ahead.
As we mentioned above, if there are situations where you think machine learning can help, you’re probably right. Examine challenges your organization currently faces and the business processes that underpin these areas. Then, think about the end-state you desire and all the different types of data you have available and brainstorm ways to use automation to get you there.
- Make data your best friend.
After people, data is your organization’s most significant asset. Data helps your senior leadership team make critical, informed business decisions. When you’re looking for opportunities to get started with machine learning, it’s a good idea to focus on your data first and make sure you’re optimizing both the input and the output. There may be a few situations where you get a “hunch” that machine learning could speed things up and make them more accurate.
A bonus tip!
Machine learning is sweeping the built environment for a good reason: it brings proven, data-driven results. The last and perhaps most important tip we’ll share with you is simple. If you haven’t already started thinking about how machine learning can help your organization, don’t waste any more time - start now.
Want to get a deeper understanding of Machine Learning quickly before you start implementing the industry’s leading technology at your organization? Sign up for our Machine Learning Essentials course. Learn more here.
Machine learning is a little bit like yoga: we all know what it is, we all know it’s good for us, but few of us understand how to actually do it. There’s a mystery surrounding machine learning that (even if our colleagues won’t admit) leaves many people in the built environment overwhelmed. Still, machine learning is more than just an industry buzzword at this point; it’s a necessity. It helps organizations make more accurate estimates, produce more valuable products, use capital and resources efficiently, identify and prioritize safety concerns, perform schedule and portfolio optimization and so much more. Now more than ever, companies are looking at ways machine learning can make them more efficient. International Data Corporation predicts spending on Artificial Intelligence systems like machine learning will reach $97.9 billion in 2023, more than two and one-half times what was spent in 2019.
It’s clear that machine learning can bring incredible cost- and time-saving opportunities to your business, but it’s difficult to know where to start. The concept can seem large and abstract, especially if you’ve never implemented machine learning solutions before. It’s easy to think to yourself, “this isn’t in my existing set of skills,” and hope someone else in your organization leads the charge. Understanding and implementing machine learning is quickly becoming an invaluable skill in our industry because organizations have to move quickly and change often. It is one of the quickest ways to put distance between you and your competitors.
Trust Your Instincts
At Enstoa, we work with many organizations dipping their toes into the machine learning pond for the first time. In our experience, if you have a hunch that machine learning could optimize areas of your business, it probably can.
One of our clients came to us with a few machine learning hunches that translated into impactful machine learning solutions. For example, their team spends hundreds of hours a year manually entering data received in vendor quotations. They had a feeling that PDFs could be read and populated in the cost system automatically. We helped with the ML. Their hunch was spot-on, and the solution development is underway.
Top 3 tips to get started
If you’re still unsure about how to get started with machine learning, here are three good first steps:
- Get a foundational understanding of what machine learning is and what it can achieve.
Many of us haven’t wrapped our heads around the intricacies of machine learning just yet, but the good news is that there are TONS of resources available online. If you have time to do the research and read all the guides, check out these useful “beginner guides” to machine learning by Bernard Marr and Chris Nicholson to get started. If you prefer to save time and take an interactive course to experience machine learning as you learn about it, check out our Colonnade course, AI: Machine Learning Essentials, to get you up-to-speed on the technique shaping the industry.
- Think backwards to get ahead.
As we mentioned above, if there are situations where you think machine learning can help, you’re probably right. Examine challenges your organization currently faces and the business processes that underpin these areas. Then, think about the end-state you desire and all the different types of data you have available and brainstorm ways to use automation to get you there.
- Make data your best friend.
After people, data is your organization’s most significant asset. Data helps your senior leadership team make critical, informed business decisions. When you’re looking for opportunities to get started with machine learning, it’s a good idea to focus on your data first and make sure you’re optimizing both the input and the output. There may be a few situations where you get a “hunch” that machine learning could speed things up and make them more accurate.
A bonus tip!
Machine learning is sweeping the built environment for a good reason: it brings proven, data-driven results. The last and perhaps most important tip we’ll share with you is simple. If you haven’t already started thinking about how machine learning can help your organization, don’t waste any more time - start now.
Want to get a deeper understanding of Machine Learning quickly before you start implementing the industry’s leading technology at your organization? Sign up for our Machine Learning Essentials course. Learn more here.
