Embracing Machine Learning: How to Get 2 Steps Ahead of Everyone Else
Embracing Machine Learning: How to Get 2 Steps Ahead of Everyone Else
But no matter where you are in the process of adopting Machine Learning, your company needs a Machine Learning platform. Here's why.
Join the DZone community and get the full member experience.Join For Free
Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Read how Alegion's Chief Data Scientist discusses the source of most headlines about AI failures here.
I am certain you have heard of Artificial Intelligence — but you might be wondering what it can actually do for your company. Is it just all hype? Well, a lot of it is hype. I’m looking at you, killer robots. As Andrew Ng said, “Fearing a rise of killer robots is like worrying about overpopulation on Mars.” But even when the discussion about AI is not dominated by unwarranted fears, there is still a misinformation epidemic that engenders unrealistic expectations. Consider that the state-of-the-art in AI is nowhere near as advanced as countless movies love to portray. Even something like the home assistant in Why Him? is easily still a decade away.
But it’s not all hype, either. It is not a coincidence that the five biggest companies in the world are all in technology and are all heavily investing in AI.
However, if you look closely, what these companies are mostly investing in is not killer robots but rather one aspect of Artificial Intelligence called Machine Learning. Essentially, Machine Learning allows you to program computers to do complex tasks using data instead of hard-coded rules.
And while Machine Learning isn’t really new, it’s only in recent years that computation has become cheap enough and data readily available enough, combined with easy-to-use tools like BigML, that have finally made Machine Learning practical.
So while the stories that grab the headlines are things like computers learning to drive or mastering Go well enough to beat a top human player, the applications of Machine Learning are much broader than this. For example, it is now possible to turn your company’s data into insights like:
- Predict if a customer will like a specific product (recommender).
- Tell you if a customer might cancel your service before they do (churn).
- Find fraudulent charges in a high volume transactional system (fraud).
- Predict the advertising method to which a specific customer will respond positively (marketing mix).
- Help you find the optimal price for an asset (sales).
- And more!
It’s unlikely you are looking at that list and thinking, I don’t need any of that. But even if I’m wrong, take note: Your company needs Machine Learning ASAP!
If you are not using it now, your competition most likely is, and that’s going to give them a quantifiable business edge that will be expensive to ignore. In some fields like finance, Machine Learning is even emerging as a requirement for certification.
Maybe you already knew that. You are reading a Machine Learning blog post, after all! Even better, maybe you are currently designing your company’s Machine Learning initiative. If so, congratulations!
But no matter where you are in the process of adopting Machine Learning, there is one critical thing you need to know if you want to avoid a mess of false starts and get ahead of everyone else.
The very idea of a platform, if implemented correctly, is that it is designed so that all aspects of the workflow are accounted for and work well together.
We worked with a mobile developer that wanted to build a recommender for their mobile application. From the time they emailed us with a few questions until they had a working system with BigML was three days.
Yes, three days and it was in production!
Compare this to other companies I’ve spoken with who decided instead to roll their own solution with open-source tools — the timeline tends to be more like one year. And even then, once they finally have it working, they are not done because there is no easy way to put the models they have hand crafted into production.
To be clear, open source is not the problem itself. There are lots of great open-source tools; in fact, BigML relies on open source as well for certain aspects. The problem is that the open-source tools are typically focused on one thing, so you end up with a big puzzle of incompatible pieces that have to be glued together with custom code that likely won’t stand the test of time.
And you get to write the glue.
Everything is already put together, saving you all that time and any future headaches due to accumulating technical debt.
You are probably familiar with Pareto’s Principle. It comes up a lot, and I assure you it applies to your employees, as well. You know that Machine Learning solution that your team assures you will be no problem to put together even though they’ve already been working on it for six months with very little to show?
Well, Pareto’s Principle warns us that 80% of that system has been produced by just 20% of your team. So, in a team of five people, there is almost assuredly one critical employee. When that one person leaves, no one left on the team will know how to finish or maintain it.
You can probably already picture who that one person is.
And guess what? That one person is in huge demand. Are you certain that you can keep them long enough to finish the project?
For that matter, are you sure what they are building has been tested?
When you adopt a Machine Learning platform like BigML, you are joining a community of more than 45,000 users in over 120 countries who has built hundreds of millions of models. You can rest easily knowing that our platform has been thoroughly tested and has survived the ravages of real-world data in all its varieties.
We’ve even started educating the world through the first Machine Learning engineer certification program, free Summer Schools, and our educational program, which brings free ML and other perks to universities around the world.
This means that every day it gets easier to find someone who knows how to use BigML.
Ease of Use
Speaking of a roll-your-own solution, even if it works, how many of your employees are going to understand how to use it?
If you are thinking no problem; we have a team that will build all the models, then you are missing a critical aspect of the future of Machine Learning.
Machine Learning needs to be for everyone.
Machine Learning is the modern spreadsheet for massive data, and everyone needs to be able to use it. You wouldn’t hire a team of Excel experts and expect all of your company spreadsheets to be managed by only them, right?
And BigML is no longer the only company that foresees this:
- "Opening AI to the masses, in other words, presents an opportunity for humans and machines to thrive."
- "In late 2014, we set out to redefine Machine Learning platforms at Facebook from the ground up and to put state-of-the-art algorithms in AI and ML at the fingertips of every Facebook engineer."
That should be the vision of your company, as well — except, you don’t need to spend millions of dollars and years of research to build your own easy-to-use ML tools.
BigML was founded in 2011, and from the beginning, we believed that Machine Learning needs to be simple enough for everyone to use.
It is a core principle of everything we do.
Speaking of everyone using Machine Learning, adopting a Machine Learning platform has another significant advantage: it makes it easy to collaborate.
Resources, like models, can be shared with a secret link making it possible to send someone a URL that when clicked on them lets them interact with the model you built and then just as easily use it to make predictions.
Commonly used resources, like a dataset, can also be shared in a gallery making it possible for a small team to curate data and then share it for everyone to use. In a private deployment, which allows your company to use BigML in a private cloud or even on-premises, these resources can be shared privately with everyone within your organization.
Despite how easy BigML makes Machine Learning, there are often other steps that need to be performed, like transforming your data, filtering, augmenting with new features, etc. It is extremely rare that a real-world problem will be solvable without implementing a workflow composed of such steps.
The good news is that these workflows are often reusable, running the same series of steps over and over with new data. The bad news is that if you are rolling out your own solution, then you are rebuilding these workflows every time.
On the other hand, BigML has created tools like Flatline a data transformation language, and WhizzML a workflow automation language, that make it possible to separate the workflow logic from the data.
The beauty of this is that these workflows can then be easily shared and reused, extending the functionality of the platform.
WhizzML is a really big deal!
In the early phases of a Machine Learning initiative, it’s easy to get bogged down in the unknowns that cloud your path:
- What problems do you want to solve?
- What data do you have?
- What data do you need?
- Will it even work?
- How will you measure success?
With all of these questions, it’s super easy to overlook something even more important: Once you build a solution, how will you automate all the steps?
The importance of automation can not be understated.
Your data is not static; it will change, and you need to build a system that can adapt along with it.
I remember talking to a telecommunications company that already had a process in place for building models to solve a particular marketing question. The problem was that most of the steps required manual processing. We asked how long it took to refresh the models, and the answer was six months!
By the time they built the models, they were no longer relevant!
We explained as gently as possible that by automating the entire workflow with BigML, they could refresh the models every day if they wanted.
This is possible because, as a platform, we’ve already built an API into BigML. In fact, our API is in some ways our core product. Even our beautiful UI uses our API, the same one we expose to customers.
This means that every single thing you can do in BigML, you can do programmatically. And we even provide bindings in many languages to make it as easy as possible to get started programming.
Hopefully, after reading the previous section, you can see the benefits of bringing a Machine Learning platform, like BigML, to your company. However, now I have to warn you about something.
We find that this platform message resonates with people who are innovators, the do-ers in a company that wants actionable results, and more often than not, the people who are specifically tasked with evaluating new technologies for their company.
This is because these are the very people who can see past the hype, past the excitement of the latest greatest tool, and understand the bigger picture. And they understand the benefits that adopting a platform like BigML can bring to their company.
However, not everyone understands this yet, it’s still the early days of Machine Learning. It reminds me of the early days of e-commerce sites when everyone who wanted a shopping cart would hack together some CGI and HTML into a custom system. And the people that could code those monstrosities were in high demand and paid handsomely for their effort. Sound familiar?
But who does that now? Well, no one.
That’s because the entire process has been commoditized. And this is a good thing because those early days saw a lot of repetitive work and wasted time. The same thing is happening with Machine Learning right now.
And if someone is in the trenches, wrangling custom solutions and you come along and say, “stop that; let’s use a Machine Learning platform instead,” you are threatening their existence.
Even worse, playing with the latest and greatest tools is more fun than solving business problems, like actually answering the question how can we improve our conversion by 10%?
But it should be clear which choice is more important to the success of your company.
This resistance will change eventually, but by then everyone will be using Machine Learning and you will be counted among the laggards.
Published at DZone with permission of Poul Petersen , DZone MVB. See the original article here.
Opinions expressed by DZone contributors are their own.