Machine learning is an extremely useful tool. You have public, but mostly they built behind the scenes. Machine learning is used to solve hard problems for different companies. How can I use ML to build smart and profitable tool from our data? — If you are asking yourself, this everyday-you must read this blog. We’ve helped many businesses answer this question.
I will give you an outline of our ML process:
1. Set up the screening meeting with the decision-makers in the company and introduce everyone to machine learning.
It is crucial to explain what is machine learning and what can you do and what you can’t do. It is crucial to remember that you don’t have a magic wand to make money from anything. You should first focus on the most significant problem companies has and resolve that. If you do that you can after test other crazy stuff like scanning surface with drones and getting data insights from it. Any use case you find needs to be rooted in company goals and data. You can’t expect management nor developers to give you all the answers. You need a cross-disciplinary team and meeting. Have a meeting with a vision team (CEO, VP Product), and someone who knows data (CTO, Head of Data Engineering). You have to schedule at least half a day for this. You need a cross-disciplinary meeting. Machine learning is a tool. The way how it goes is that the more you understand it, the better you can put it to use. If people think it’s a magic wand like in Harry Potter, then they won’t be able to help you with your search.
You have to make it practical, leave out the math, and ask yourself these three questions about machine learning:
What is machine learning?
When can you use it?
What are the common misconceptions?
When everyone has an understanding of what machine learning is, it’s time for you to learn from them. Every company is unique. The overlap in what two different companies need is small. Don’t try to fit your company into a box.
2. Get inputs from all departments and forget about assumption.
You should look at the facts. Leave assumptions later when you have enough data and testing and predictions.
3. Make a list of machine learning processes that they can use in their company
4. First mover advantage
Check If MVP ( minimal valuable product) can be made in less than 4 months. It is crucial to deliver a solution fast so they can incorporate in the business.
5. You can’t make it all in once Putting ideas early in the process isn’t the right decision. Refocus the discussion: “This is more of a nice-to-have, so let’s leave it for now.” Ask critical questions: “If we did manage to automate and improve the accuracy or speed of this process by 20%, what would that mean in revenue per year?” To compare the cases you have left, make an Excel spreadsheet with the following columns: Data availability — How easy is it to access the correct data for this tool? If you don’t have the data yet, assume how much time you need to get it. Potential growth — If things go very well, how significant is the potential impact on a business priority? Risk — Do you have unknown factors that could dismiss the project? What does your experience tell you? Time to implement — Prioritise quick wins. With one substantial success behind you, you can move on to the more complex projects. You should use 80/20 rule to prioritize. As I mentioned above resolve first the most significant pain, they have in the company. The more machine learning projects you’ve already delivered, the better you can ask the right questions. You can ask yourself: What are the joint fails in projects like this one? Which datasets are essential to have, and which ones are not so important? What is a reasonable level of improvement? If you haven’t delivered a similar use case, talk to a team that has.
6. Research, analyze and again research :
When you’ve identified your top 3 cases, Google it: Who has implemented similar systems before? What approaches did they try? What were the findings and the final results? However, in machine learning, you can put together some of that information from published academic research. You shouldn’t copy approach. Get inspired and steal the best ideas. Use them to guide your further investigations and make it different. Making different it helps resolve real business problems and get your hands dirty on a machine learning project.
7. Make a report and visualize your insights (use Tableau for example)
You should explain the key point in 15 minutes. I know it is hard to summarize 8-hour meeting in 15 minutes, but that is actually why Data Scientist is paid that so much.
Great ML tools:
Goals
What goals are driving your company now? History What projects did you implement in the past? What were the results, the challenges?
Data
What data do you have? Where is it made, and where is it saved? How much consistent history is there in each database?
Infrastructure
What is the preferred infrastructure? Are there relevant restrictions on which provider to use (on-premises, AWS, or Google Cloud)?
Data Science Strategy
What is your data science vision? Do you want to build up your expert team, or do you want to find an experienced team to build you a solution? Combination of both? After you know what’s driving your business now, you can get more precise.
It’s time to find all the potential use cases.
Outline the list of processes for machine learning How can machine learning be used to automate decision making in your company? Machine learning is a tool to automate pattern discovery. It’s about improving an existing process by making it a bit better.
Great examples of machine learning are usually:
Data based: The process is already entirely based on data.
Large scale: happens over and over again
Automated: The process already uses technology to some degree.
Good ML examples:
Product recommendations
Credit scoring
Personalized marketing
Fraud detection
Image recognition
So think where data is being used to automate decision making and is their room for improvements. The best place to use machine learning is to support a process that’s currently done by people. Law firms are a good example. If it’s highly repetitive, tedious, and therefore slow, it might be perfect. Can you make it faster? Is it training a machine-learning algorithm to take on some decisions? Finally, make a decision! Update your use case ranking with the new information you picked up. Based on your findings, research, and experience, make a rough project plan. Together with your prioritization goals and the project plans, present your findings to your team. If you’ve done this, always keep the big picture in mind. You should now get your hands dirty. Have fun!
You need help making an ML plan? More than 10 companies have developed their ML plan with us in the last 12 months. I am super excited to get in touch with you and find out what you want to achieve and how ML can help your business to grow and gain more profit.
In this short article, you will learn everything a CEO needs to know about machine learning. How Machine Learning works. After that what you can do with it. In conclusion how to get started.
It is all about connecting A to B
What a CEO needs to know about machine learning? How Machine Learning works. After that what you can do with it. In conclusion how to get started. Therefore, information on ML is often confusing and sometimes downright misleading (I’m looking at you, Microsoft). But ML for business is simple:
“99% of the economic value created by AI today is through one type of AI, which is learning A to B or input to output mappings.” Andrew Ng
Almost every business application of ML today is about learning to produce certain outputs from certain inputs:
How does ML learn to predict the correct output?
You collect examples of input –> output pairs (the more the better).
The ML algorithm learns the connection between input and output (that’s the magic).
You apply the trained algorithm (the “model”) to new input data to predict the correct output.
In other words, almost everyone who uses ML to make $$ does it exactly like this.
Use ML when you have a lot of data
ML needs data
Therefore, ML is powerful because it turns data into insights. But it is less efficient at learning than people are (yes, way less efficient), so it needs a lot of data in order to learn. If you have lots of data, you should think about it!
Data is a competitive advantage, not Algorithms.
However, that’s why Google and Facebook have no problem open sourcing their algorithms. But they definitely don’t open source their data. If you have a lot of data no one else has, then that’s the perfect opportunity to build a unique ML system.
The 3 simplest ways to find your ML use cases
For instance, you have a lot of data. Now, what do you do? Here are the 3 best ways I know to discover ML use cases:
1. Improve automated decision-making
If you are wondering what the CEO needs to know about machine learning. Where do you have software that automates rule-based decisions?
For example:
Call Routing
Credit Scoring
Image Classification
Marketing Segmentation
Product Classification
Document screening
There is a good chance ML can improve the accuracy of these decisions because ML models can capture more of the underlying complexity that connects A to B. By comparison when you write rules into software manually (the traditional way), you can only encode rudimentary dependencies.
2. Things people can do in < 1- second
Another great heuristic I first heard from Andrew Ng is:
“Pretty much anything that a normal person can do in <1 sec, we can now automate with AI.” Twitter
So what are some things humans can decide in < 1 sec?
Who’s in that picture?
Do I have a good feeling about this potential customer?
Does this look like a problematic CT Scan?
Many jobs are a sequence of < 1-sec decisions. Like driving:
Is that person going to cross the street?
Am I too close to the sidewalk?
Should I slow down?
… and many, many more.
Anything you can do in less than 1 second, ML can most likely do too (or it will be able to soon).
3. Get inspired by Kaggle competitions
Large corporations like Zillow, Avito, Home Depot, Santander, Allstate, and Expedia are running data science competitions on Kaggle. These are challenges they want outside data scientists to solve. So these competitions give you an idea of what types of AI solutions they are working on. It’s really a great resource.
Building a good data science team is super hard (and expensive!)
Many companies struggle (and ultimately fail) to build an efficient data science team. Why is it so hard?
Misinformation about who to hire
Tough competition for talent
Few managers who can lead data science teams effectively
In addition…
You don’t know yet whether you need a data science team
You might have a lot of data and a lot of ideas, but that doesn’t mean you need your own data science team. Only after you’ve built your first AI systems will you really know how much manpower you’ll need in the long run?
Above all build something that works — fast
Your first goal should be to pick the lowest hanging AI fruit and finish it quickly. This gets you miles ahead:
You’ll achieve the tangible success that gets investors, the board, and your team excited;
Get to know the taste of ML and get real-life experience of what works and what doesn’t;
Have better ideas about where to use AI next.
With that first finished system under your belt, you are in a much better position to hire and train a data science team. Or maybe you find that you don’t want (or need) to hire a whole team because that low-hanging fruit already accounted for 80% of what you can feasibly do with AI.
ML teams should work across the whole company
However, If you do build an AI team, you should build it for the whole company, not just one department. A horizontal AI team.
In other words, don’t think in departments
AI experience is very transferable: To a data scientist, your CRM data looks almost the same as your inventory data. And to an algorithm, they look even more similar. It makes a lot of sense to build one AI task force for the whole firm.
Side note: For that to work, you should also have your data in one place!
More life-saving tips
In other words, don’t listen to people selling a “better algorithm”
Either they have no experience, or they’re trying to sell you open source for a markup. In AI especially, everyone is cooking with water, meaning they’re using publically available algorithms.
Above all focus on business experience & engineering quality
Also, Work with someone who takes the time to really understand the business problem you’re trying to solve and has high standards when it comes to engineering quality. However, If you want lots of results with fewer headaches, then the plumbing is more important than the cute algorithm that flows through it.
Magic Triangle meetings 😋
In other words, the best ideas develop when you put three types of people in a room:
A person who’s in touch with the current business priorities (you?),
A person knows the data you have (your database engineer), and
Someone who has lots of practical experience building ML systems.
For instance, together, these three people can make realistic plans, really fast.
In conclusion:
AI is about connecting A to B.
Look into processes that involve a lot of data and < 1-sec decisions.
Get the first win before you plan big.
In conclusion, you learned everything a CEO Needs To Know About Machine Learning. In other words, If you need help along the way, drop me a line. I’m always happy to hear about exciting challenges: manja.bogicevic at kageera.ai
