How to Manage Machine Learning Initiatives
Machine learning is a growing technology popping up in more companies than ever. Regardless of company size, industry, or product offering, artificial intelligence has the potential to offer business-changing insights. From consumer observations to new product prototyping, machine learning is ready to impact your company.
But knowing machine learning is important doesn’t help you build a machine learning initiative, or better understand and work with your team of experts. At Algorithmia, we can help.
How machine learning affects business
Whether you are a project manager or an executive officer, understanding machine learning will only get more important as time goes on. Just as more data is available today than ever before, even more data will be available tomorrow and the next day. As data availability grows, the only way to truly use it for understanding and insight will be with machine learning algorithms that learn to use such large amounts of information without human error or fatigue.
The benefits of machine learning are already being realized in many industries: athletic organizations can look at millions of data points about player stats, health metrics, or injury history to build strategy around what they can do to have the most success. Weather services analyze previous weather data to learn what patterns to watch for to identify everything from major natural disasters to the everyday forecast. Supply chains everywhere can utilize historical data on supply, demand, and cost to make the best possible business decisions moving forward, affecting what they stock and how much they charge based on what they believe people will buy.
Why managers need to understand machine learning
Machine learning is a technical practice that requires the right skill set and expertise to truly succeed. But managing a business or team also requires the right skill set and expertise to succeed. So while you may not be able to learn the intricacies of your organization’s projects overnight, you can learn how to support them with your management.
Taking the time to learn the basics can go a long way with your data scientists and engineers. Showing the initiative to truly understand what they do, why it matters, and how you can help shows them that you value both their work and them as individuals. As your employees feel valued, the relationship between you as management and them as fulfillment will strengthen, allowing for more open communication and collaboration.
With better communication channels and respect in place, you can more clearly communicate what is needed from certain projects. With your basic knowledge, you’ll also understand what is possible and how to get the results you need. You can also ensure the feedback you receive and the analytics you gain are pointing in the right direction.
Afterall, as management you have leadership skills that can be used to support your employees, and you have a better idea of the big picture. You know how the ML efforts fit into bigger business initiatives, so understanding the ML efforts can help you better bridge the gap between strategy and success. The more you understand what your scientists are doing, the more you can guide and direct initiatives to stay on track for c-suite goals. They know the technical side, the programming and systems, but you know how it relates to the overall business and what is needed for success.
With all this in mind, let’s start with the basics of machine learning so you can better understand what you are leading.
What is machine learning?
For you, machine learning can take information, find patterns, and return actionable insights. Machine learning is the process of creating algorithms that take structured data and analyze the inputs and their outputs to create rules, or ‘models’, that can make decisions or predict future outputs of new input data. Machine learning is a shift from concrete cause-and-effect analysis to predicting future effects based on past causes and effects.
Algorithms are trained based on the purpose of the model, and validated through input data with already existing outputs. Think about training and validation like a set of flash cards: when reading the front of the flashcard (input), you predict the back of the flashcard (output). Getting the output correct validates that you’ve learned how to find the output for the input, getting the output incorrect means you’ll need to revisit how you select outputs. However, machine learning is more complex than flashcards, which have a set, pre-decided output for each input. Once trained and validated, an ML model can take new data inputs and predict, based on what it learned previously, what the outputs will be.
A common example is identifying images of shapes: when a machine gets a large amount of pictures of circles and squares, it can train to identify the differences, like spotting corners, or straight vs. curved lines. Once the machine correctly identifies the circles and squares, it can be validated with labeled data to see if it determined the shapes correctly. If the model did predict correctly, moving forward it can be given images of circles or squares that haven’t been labeled, and it will remember what it learned from the previous labeled data (looking for curved lines, corners, etc.) and regularly predict which shape it was given.
Unlike traditional software, machine learning can look for rules and variables on it’s own to better understand what attributes of an input help classify the output. Without ML, a developer needs to code the exact rules and variables to associate with certain outputs, and a machine follows the rules exactly, unsure of what to do with anything not specifically coded. And while circles and squares may not have many attributes to code, more complex issues can have too many variables to count, let alone provide to a computer.
But how do circles and squares apply to your business? Shape-identifying may not, but more complex models that identify customer happiness from semantic analysis of reviews or potential interactions of a prototyped medication just may.
Is machine learning different from artificial intelligence?
Artificial intelligence as a whole looks to mimic human intelligence by reproducing systems that think or act similar to the brain. Machine learning is one part of artificial intelligence that is founded on the idea that machines can learn from data and make decisions without being explicitly programmed with rules to follow.
Types of machine learning algorithms
Just like machine learning is a small part of artificial intelligence, algorithm types are a small part of machine learning. We’ll go over a few of the main types of algorithms you’ll use to get you started.
Supervised learning happens when an algorithm is provided with clear, labeled inputs and outputs, and the model what it is meant to learn. It is much like a teacher helping to train a student. The student is given problems to solve, but the teacher provides an answer key to help the student learn how to solve the problems correctly. The intention is for the student to learn in a supervised setting, and then be ready to solve similar problems, without the answer key.
Supervised algorithms are one of the most common machine learning algorithm types, and the best place for newcomers to start.
While labeling data can help an algorithm learn quickly and validate its learning, companies can’t always manage to label the vast amount of information they are bringing in. This is where unsupervised machine learning comes in.
Unsupervised learning algorithms take the inputs they are given and sort them into groups or collections containing similar attributes, as deemed by the algorithm. These sections of the overall data can then be put through a supervised learning algorithm to achieve faster, more accurate results.
Different from supervised or unsupervised, reinforcement learning is a constant process. This machine learning algorithm makes decisions based on data inputs and then compares its predicted outputs with the actual outputs in order to learn. As the machine predicts correctly, it is validated in it’s method, but as the machine predicts incorrectly, it course corrects to be better ready for new inputs and predictions.
Types of machine learning problems
Beyond types of algorithms, machine learning can also depend on the type of problem being solved. We’ll touch on the basic types of problems machine learning is advantageous for.
Think about classification like a yes or no question, there is a finite amount of ‘classes’ the inputs can be sorted into. Classification problems have specific outcomes the algorithm is looking for and matching. Sorting images of circles and squares would be a classification problem.
Regression problems result in a range of outputs, like predicting the approximate weather for a given day of the week. Regression problems probably remind you of algebra as they can be plotted on a graph and look for how the changes in variable ‘x’ affect variable ‘y’ on a consistent basis. Predicting the weather throughout the day would be a regression problem.
Clustering problems sound exactly like what they are. In clustering, machine learning takes a large amount of data and sorts them into smaller, more matched groups. If we provided inputs of every color of the rainbow, clustering would separate them into groups of red, orange, yellow, green, and blue.
Similar to clustering, dimensionality reduction is meant to help take a large amount of data and sort it into smaller, easier to use groups. However, in dimensionality reduction the number of input variables or factors is reduced to help the machine better identify differences, without getting caught up on insignificant attributes.
If you were to sort images of clothing tops from bottoms, you would not need to also sort short-sleeve vs. long-sleeved shirts, as they both fall under tops. You would also not need to sort shorts vs. pants vs. skirts, as they all fall under bottoms. Ignoring the factors that separate a long-sleeve from a short-sleeve top will help the model more quickly identify what is generally a top and what is generally a bottom, creating a dimensionality reduction problem.
How has machine learning evolved
With advances in digital technologies, data has become more available than ever before. The amount of data that can be collected, scraped, or bought grows exponentially each day. But to use that data to its fullest potential can take years of manual parsing, labeling, and reviewing. This is where machine learning comes in. By creating algorithms that can be trained to find and identify specific attributes, ML models can do the manual parsing, labeling, and reviewing in a fraction of the time, and provide you only the most valuable insights.
Not only has advanced technology created more data access, continual hardware and software improvements have made computational processing more efficient and affordable for all users. What once took a full room to run can now fit in your pocket, and machine learning has only benefitted.
Voice assistants, chatbots, auto-translation services, and, of course, the self-driving car are all examples of ways machine learning has entered our everyday lives, and this list will only grow.
How Algorithmia makes managing easier
Algorithmia provides one unified platform to securely deploy, operate, and scale your machine learning portfolio. With a single control center for all things ML, your data scientists and operations engineers can more easily work together to accomplish your businesses goals. Connect data and track model versioning in the same place that you manage costs, oversee infrastructure usage, and control data, model, and infrastructure access.
Give your ML team the tools they need for success. Get a demo today to learn how Algorithmia can streamline the management of your machine learning initiatives.