When is a Machine Learning Model ready for Product

Author(s): Rijul Singh Malik

Originally published on Towards AI.

What to look for in your machine learning model when building a product

When you build a machine learning model, you need to make sure that the model is fit for its purpose. You don’t want to build a model that is not fit for your purpose. This is not just important for the model but also for the time and money that is being spent to create the model. How do you know when a machine learning model is ready for the product? This blog will discuss when a machine learning model is ready for production.

Why knowing when your machine learning model is ready for the product is important

Almost every business desires to have an AI and Machine Learning is a driven system. This can be achieved in many ways, the most popular being through a series of AI-powered chatbots and other conversational interfaces. The question that most businesses ask is when can we actually implement it. When is the Machine Learning model ready for production? The answer to this question is not so simple, as it depends on several factors including the Machine Learning model, business, target audience, and the type of platform it is being deployed on. The most important factor is the accuracy of the model. You can have a highly accurate model that predicts very well, but if it returns the wrong results then it is of no use. So the accuracy of the model is the most important factor in determining its readiness for production.

If you’re working with machine learning or artificial intelligence, you’ve no doubt done a fair amount of experimentation. You’ve tried different models, evaluated different algorithms, and probably have even gone back to the drawing board with your data set and the way you’re building your model. With the increase in machine learning and artificial intelligence, there’s a lot of hype surrounding the possibilities of these technologies. But what about the possibilities of mistakes? I’ve seen a lot of organizations (myself included) put machine learning models into production that were not ready for prime time. It’s not because we’re impatient. It’s usually because it’s hard to know when a model is ready. As developers, we often know when a piece of software is ready to go because we’ve written it, and it’s just a matter of doing some QA and putting it out there. But with machine learning, it’s not that easy. We have to know when the model is ready, but we don’t really have a lot of data to judge it on. And that’s why we have to have some kind of process or methodology.

What are the best ways to evaluate a machine learning model?

A lot of people don’t realize that when you’re working with a machine learning model, there are a lot of ways to figure out if the model is working out. The most important thing is to test out the model before you put it into production. You can test out your model in several ways. One way is to compare the results of your model to a baseline algorithm, or the results of a different algorithm. This is known as your ground truth. This is a very common method, and there are several ways to do this. For example, you can use k-fold cross-validation to do leave-one-out testing. It’s important to note that you should try to use several different ways of testing your model because you can never tell how it will perform in the future. Other ways of testing your model are to make sure that the model is accurate in terms of precision, recall, and F1 scoring. There are a lot of ways to test your model, so it’s important to do some research on different methods.

Evaluation of machine learning models is the process of determining whether a model performs according to expectations. If a model is expected to be able to predict the outcome of a certain task, an evaluation is carried out to determine how well it can do that job. There are many ways to evaluate a machine learning model. Broadly, you can use a hold-out set, cross-validation set, or k-fold cross-validation.

A lot of people have already discussed how to evaluate a machine learning model and some of them are quite good. The problem is that most of them are tailored for data scientists or people who are interested in model evaluation, to begin with. I wanted to write this blog post to give a broader perspective to product managers and other stakeholders interested in using machine learning models in production. I will focus on the model evaluation as an end-to-end process that includes: — Model Development — Model Testing — Model Improvement I will try to provide a checklist of things you should look for when you are evaluating the readiness of your machine learning model for the product. The following is a list of questions you should ask yourself (or your team) while evaluating the model.

What performance criteria should you use when looking at your model?

Machine Learning models (ML) are always a matter of tradeoff. You can ask for a model that is able to predict the behavior of your users with high accuracy, but if you sacrifice the time of building the model, the project is going to be a failure. On the other hand, if you want to build a model that is able to predict the behavior of your users with high accuracy, but you sacrifice optimization to get the model ready for production, you’re also going to fail. Performance criteria are a must when defining the goals of a project. To define the right performance criteria, you need to know what your goals are. There are four main goals that should be defined before you start building a model: prediction accuracy, learning time, production time, and generalization. Prediction accuracy is the most important goal. If you’re trying to build a model that will be used to predict the feelings of your users, then high prediction accuracy is going to be your number one goal. However, if you’re trying to build a model that will be used to optimize your website, then high prediction accuracy is not going to be your main goal.

So, it’s the responsibility of the product owner to decide when a model is ready for production. This decision is usually an educated guess based on the performance of the model, the accuracy, and the confidence level of the model. If the model is returning reasonably good predictions, is providing high accuracy, and you have a high degree of confidence in the model, it’s probably ready to go. If you don’t have these things, it’s probably not ready.

How to use benchmarks in your model evaluation

Machine learning is such a broad industry, it is not surprising that many people are confused about what machine learning is and how it can be applied. I’ve been working in this field for several years and I still feel that I don’t know enough about it. How can we be expected to know everything about machine learning when there are so many different types of ML models and when some of them are so complex? It is really easy to find a lot of resources about machine learning on the Internet, but finding the right information for your application can take some time. Machine learning models are really powerful and have great potential, but you have to understand how to evaluate and measure them. In this article, I will walk you through different ML models, from basics to complex, and I will explain how to evaluate a model.

The process of evaluating a machine learning model can seem tedious and complex, but it doesn’t need to be. As a matter of fact, there is a simple way to evaluate your model, and it is much more straightforward than you might think. In addition, you get to learn a lot along the way, which can be just as valuable as a good model. When evaluating a model, we use a set of metrics that help us to determine if the model is ready for production. It is important to set up a process or a pipeline that you can easily go through to evaluate your model. As a matter of fact, the process itself can be split into three steps: Data-preparation Evaluation of the model Tuning of the model These steps are not necessarily in sequence and can be done iteratively. But for now, let’s take a look at each one of them in turn.

Conclusion

Before you send your model off to a not-so-friendly environment, make sure that your model is ready for the real world.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI