AutoML, or automated machine learning, can help you leverage AI within your organization. It can automate everything from gathering data to deploying machine learning models, so you can easily make strategic decisions.
This comprehensive guide will review the top three ways AutoML can add value to your organization:
1. Boost Efficiency
One of the most compelling use cases for implementing AutoML tools is that they can significantly boost your efficiency. Time is money, and the more effectively you can use your machine learning resources, the better.
Similarly, ML is only beneficial when you can apply it to real-world problems. By leveraging AutoML, you can streamline the process needed to use machine learning models to solve your business problems. Think model development, testing, and deployment – if you can automate this process, your organization will be much more productive.
Finding data scientists can be tough enough, and no one wants to work in a role that involves cleaning and preparing data 80% of the time!
AutoML provides your team with a user-friendly interface that even someone without a machine learning background can use. In other words, it simplifies the entire machine learning pipeline so that your staff can benefit from the insights machine learning provides – without spending months working through complex processes.
Choosing the Right Algorithm
Selecting the right algorithm for your machine learning model is key to your success. The answer will vary depending on the AI’s goal and the type of data you have to work with, though.
So, how do you choose the best algorithm? Normally, this is a manual process that involves evaluating various models, tuning hyperparameters, and testing.
For instance, if you need to solve a yes/no classification problem, you can choose from a wide variety of algorithms. You can consider decision trees, logistic regression, gradient boosted trees, and more. Choosing the algorithm that would provide the most accurate predictions would require significant evaluation and adjustment.
With AutoML, you can optimize your algorithm automatically! It will transfer your data to each training algorithm and find the best architecture without any manual work required. That means instead of taking hours, you can complete algorithm selection in just a few minutes.
Another aspect of machine learning development that AutoML simplifies is feature selection. The features you choose to include in your data have a significant effect on the model’s results and predictions.
If you don’t perform the feature selection properly, your scoring will be affected, and it can slow down your process. Instead, your organization can rely on AutoML to select features for you with its automated evaluation process.
2. Scale Machine Learning in Your Organization
AutoML can also add value to your company by allowing you to scale your use of AI. Almost every department can benefit from machine learning models – whether you use them to analyze customer trends or for data-driven decision-making, they can help move your business forward.
Instead of spending all your time on iterative modeling tasks, AutoML lets you shift your focus to applying this technology at scale. As a result, you can solve problems across your organization with speed and efficiency.
Consider the benefits of a machine learning model that has natural language processing capabilities. These tools can analyze text, pinpoint important details, and categorize documents. The faster you can deploy the ML to various departments the better – but doing this manually would require a significant amount of time and resources.
AutoML solves this problem by giving you the power to develop and deploy these custom algorithms with ease!
3. Minimize the Risk of Human Error
Like we’ve mentioned, the traditional machine learning process is very manual. Your teams must identify problems to solve, manually collect the data and test various algorithms to determine the right fit, and work with other departments to move them to production.
There is a high risk of human error during this process, which can lead to inaccuracies with your predictions and reliability issues. One area that tends to be manual – and prone to human error – without AutoML is model evaluation.
Here, your data scientists must determine whether the model can be trusted to make accurate decisions once new data is presented. The model can be overfitted and align too closely to the data or under-fitted where it doesn’t know enough to pick up on patterns and trends.
However, when you apply AutoML, you reduce human bias and have confidence that your algorithms will benefit your business. These tools will automatically measure the effectiveness of your model and compare it to evaluation metrics, so your team does not have to do so manually.
This also allows you to address skill gaps in your organization. If you don’t have an expert ML engineer on staff to determine when a model is ready to be moved to production, how will you be able to rely on your AI tools?
Simply put, AutoML eliminates the time-consuming processes so that your business can focus on tasks that add value – like making decisions based on the results of your ML model.
About the author
Ekin Keserer is a co-founder of Akkio, where he focuses on driving the company’s mission of democratizing AI by building simple and easy-to-understand user experiences. Prior to Akkio, Keserer worked on making various powerful new technologies accessible such as metal 3d printing and big data analysis at places like Markforged and Palantir.