Machine learning is becoming a fundamental need for every business these days and plays a major role in differentiating your startup from others. Unfortunately, most startups underestimate how difficult and expensive implementing machine learning can be. Here are the guidelines I follow for successfully integrating machine learning for startups while they are still in their infancy.
So What is Machine Learning?
Machine learning refers to one aspect of this goal, in particular, algorithms and processes that learn in the sense of being able to generalize past data and experiences in order to predict future outcomes. Fundamentally, machine learning is a set of mathematical techniques, implemented on computer systems, which enables a process of exploring information, discovering patterns and drawing inferences from data.
Your spam filter is a machine learning program that, from examples of spam emails and regular emails, can learn how to report spam. The samples that a system uses to learn from are known as a training set. Each training sample is known as a training instance. In this case, the task T is to mark spam for new emails, experiment E corresponds to the training data and the performance measure P must be defined. This particular performance measure is called precision and is often used in classification tasks.
Machine Learning for Startups
The main problem with machine learning for startups is that as a startup you do not have data to make decisions, and it can seem almost impossible to acquire all the data you will need to make use of it as machine learning algorithms need data. You can try partnering with an organization that has all the data you could possibly need, but few startups are successful.
Another commonly suggested strategy is to use data extraction and labelling services, but this is usually quite expensive. As a startup, you usually don’t have a lot of money and don’t want to waste your entire budget creating a dataset. It is possible to save the expense and do the extraction and labelling yourself as long as you have nothing else to do for the next few months.
I offered a few options and opposed all of them. So what should a startup founder do? I’ve found that the best way to solve the problem is to plan for a gradual shift to machine learning while designing your product in a way that gets your users to label your data for you.
Make a Strategy:
For the implementation of Machine Learning in startups, the tools you use are important, especially for the tools that govern your business data. How do you choose the right data tool? Whatever you choose, it should be inexpensive or free to start with, should have a low learning curve, and should offer flexibility so that your business can grow without unnecessary friction. Essentially, you’ll want to avoid corporate tools.
Focus on Data:
Building data pipelines into your system early on will make it easier to add machine learning as your startup matures. However, you will not have any data on the first day. So, when designing your product experience, you should consider ways to get your users to label the data for you. The goal is to acquire precisely labelled data as close as possible to free.
Expansion of Data:
When you have a lot of data but it doesn’t seem like quite enough for a machine learning model, maybe you can use the data expansion strategy to increase the size of your dataset without simply duplicate records. By doing this, you can reduce the potential for high bias models caused by data sets without sufficient variance.
Depending on the data you have available, you can apply transformations to the data to generate more data points for training from your existing data set. Techniques such as rotating images or adjusting the audio height can help broaden your data set and produce better results.
Do Not Reinvent:
Companies like Google, Amazon, and Microsoft all have APIs that allow you to make predictions using their Machine Learning models. For more specialized applications that their services don’t cover, you’ll have to roll up your sleeves and use frameworks like TensorFlow and Keras to build your own models.
I’m not suggesting doing this at an early stage unless it’s absolutely crucial to your value proposition. Creating quality models from scratch can take months, and despite your best efforts, they can still end in failure.
It’s not easy to run a startup, let alone a machine learning company. As you get creative in the way you acquire and label data, you’ll find that Machine Learning doesn’t have to be an overwhelming hurdle. Hope you liked this article on How to Use Machine Learning for Startups. Please feel free to ask your valuable questions in the comments section below.