Machine LearningA Complete Guide to Automated Machine Learning

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Automated machine learning is an aspect of data science that helps businesses around the world make data-driven decisions.

According to industry reports, the autoML market is expected to reach $14 billion by 2030. The advances in AI algorithms and the increasing popularity of automation technologies might be the reason for this growth.

This complete guide to automated machine learning will help you understand AutoML, why it is essential, how LogicPlum can help you implement it, and much more!

What is Automated Machine Learning?

Automated machine learning, also called AutoML for short, refers to the process that allows businesses to apply machine learning to real-world challenges. These tools use artificial intelligence to learn how to recognize trends and patterns from a raw data set – once the model is trained, it can apply that knowledge to new data introduced later on.

Historically, machine learning methods were difficult to apply to business problems because they were very time consuming and resource-draining. Customarily data scientists and experts in the field were required to develop the algorithms and design the models. Your organization can now automate these tasks due to the rise of AutoML.

Businesses no longer need a dedicated team of data scientists to use automated machine learning models because the solutions available can perform these processes automatically. Not only does automating the machine learning process allow models to be developed much faster, but also, these models can produce more straightforward solutions that tend to outperform those that were manually created.

Simply put, automatic machine learning takes the best machine learning practices from top data scientists worldwide. It packages it into a solution that makes data science more accessible to all organizations.

So, how does automated machine learning work? We can break it down into nine steps, where steps one through three are completed manually, and the remaining steps are processed through AutoML systems.

The manual steps are as follows:

  1. Determine the goal of the machine learning model
  2. Collect data from all relevant sources
  3. Label the data that will train the model

Once these are complete, you can automate the remaining steps:

  1. Select the features from the raw data
  2. Split up the dataset so that you have the training, validation, and holdout data
  3. Choose an accuracy metric and performance threshold
  4. Train the automated machine learning model
  5. Analyze the results produced by the model
  6. Integrate the machine learning model into your decision-making process

As you can imagine, manually performing all of these steps would be very difficult and time-consuming. Automated machine learning software has cut down the time it takes for businesses to develop models and has made it more appealing for them to be incorporated into the decision-making process.

Why is Automated Machine Learning Important?

Automated machine learning is essential because it makes artificial intelligence and other data science tools accessible to organizations that may not have computer scientists or mathematical expertise in-house.

Likewise, automating most of the machine learning process helps eliminate human error and bias, which can reduce a model’s accuracy and make the insights that you gain less valuable. With Machine learning automation, you can benefit from the knowledge built-in by data scientists without spending the time and effort to hire them yourself. This saved time also significantly improves your return on investment!

Without automated machine learning, Netflix would not predict which movie you should watch next, and Amazon would be unable to recommend other products based on your purchase history.

There are also applications in industry like finance. AI automation is used to prevent and detect fraud and money laundering or healthcare, where machine learning models can help diagnose patients more accurately and efficiently.

Automated Machine Learning + LogicPlum

At LogicPlum, we are experts in automated machine learning. Our goal is to connect your business with some of the most sophisticated automation and artificial intelligence platforms in the industry.

Our experts will help you leverage AI automation’s power to solve the problems that your business is facing, including everything from customer retention and operational effectiveness to product design and marketing.

Not only will you have access to automatic machine learning that will provide you with valuable insights into your customers and organization, but you will also have an understanding of how your models arrived at their predictions.

LogicPlum’s automated machine learning platform will help transform your organization into one that makes data-driven decisions!

What Can We Automate in Machine Learning?

Let’s dive into what we can automate in machine learning, starting with choosing the model itself.

Since automated machine learning models can be put together and tested rather quickly, artificial intelligence vendors like LogicPlum can test out your data with several different algorithms to determine which one would work best. Each algorithm will have the parameter set by default, and then based on the results, you can decide which one performs the best.

Feature selection is another aspect that we can automate in machine learning. Features are the variables that the machine learning model is analyzing to produce an output or prediction. When data is collected, it needs to be cleaned up to include only the relevant features in the artificial intelligence platform. With this part of the automated process, you can automatically see which features have the most impact on your predictions.

The reason that auto-machine learning is meaningful is that it helps a machine model become smart. In other words, the model doesn’t know anything when it’s built – it has to train and be exposed to existing data so that it can start understanding underlying patterns and trends.

As machine learning algorithms learn how to read and understand data, they will correct themselves until they make more accurate predictions. Training a data model can take several iterations. Still, you will know once the model is ready because it will produce accurate predictions when it is fed new information that resembles what it is trained with.

Sometimes you may be able to train an AI model on a set of data and then use it for a different data set later on, but this is not always the case. If the data sets are too dissimilar, the model may have trouble producing accurate results – meaning that most of the time you want to analyze new data, you will have to build a new model and train it.

With AutoML, you can utilize pre-trained models to save you ample time and resources on preparing data solely for training purposes. These models are widely available and very useful to tackle common problems that businesses face across industries.

Automating the training process eliminates some of the tedious work that would previously be assigned to data scientists, so more businesses can utilize this technology to gain valuable insights and grow their firms.

Will AutoML Replace Data Scientists?

Although AutoML is capable of automating several aspects of the machine learning process, it will not replace the need for data scientists.

Artificial intelligence automation is great for building models and allowing businesses to use them to make decisions. However, data scientists are still essential to defining business problems, and understanding your organization’s areas could benefit the most from automated intelligence.

Also, automated machine learning models still need to be interpreted, which generally requires data scientists’ expertise. Since building the model itself is mostly automatic, data scientists will have time to do more value-added work to model interpretation and other complex problems that the company is facing.

Data scientists are often one of the scarcest resources in your business, so if they’re spending their days manually building models, you cannot utilize them to the best of their abilities. With automated machine learning, they can spend time working with product managers and clients to determine where and how to incorporate additional artificial intelligence tools.

Data scientists will also be needed to maintain existing machine learning models, as often, business goals and criteria will evolve. Automated machine learning will not eliminate the need for data scientists but rather allow them to focus on the tasks that will produce the maximum value for your organization.

Automated machine learning allows businesses to quickly process large amounts of data to gain valuable insights about their customers in the industry. This knowledge can then be used to make data-driven decisions, such as when to extend a loan to a customer or what diagnosis should be made.

There are endless applications for artificial intelligence in business, and the automation of these processes will allow more organizations to take advantage of them. However, this does not mean that data scientists will become obsolete. They do not have to spend hours training data models to focus on value-added work rather than the tedious tasks associated with building machine learning models.

By working with LogicPlum, your company will have access to tools that will allow you to create a competitive advantage. Our top-ranked automated machine learning models will allow you to discover trends and patterns that were previously unrecognizable so that you can further grow your business!

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