Drawing on the increasing availability of data and higher levels of computing power, machine learning is making a huge impact on our everyday lives. The impact will continue to increase rapidly with the introduction of automated machine learning (auto ML).
While you can look around and point at some of the more conspicuous applications of machine learning especially in intelligent assistants like Siri, Google’s self-driving cars and IBM’s Doctor Watson, auto ML is also quietly transforming business operations across the world.
Models built with machine learning are enabling businesses to leverage their own data to make decisions faster. But, to date, only large companies and organizations have really been able to harness this power.
This is because machine learning is only fully understood by a small group of advanced engineers and statisticians called data scientists. Due to the fact that they are some of the most sought after professionals at the moment, only the biggest companies and organizations can afford to attract them.
But auto ML is changing all that. It makes it possible for organizations and companies of all sizes to access the benefits of machine learning without having to establish their own resident team of experts. How does it do this? Let’s take a look.
What Is Automated Machine Learning?
Since auto ML is the automation of processes or steps that enable machine learning, we need to start with the goal of machine learning—with machine learning we try to teach computers how to accomplish tasks without human supervision (unsupervised).
It achieves all this by using statistical methods and algorithms that help the machine process data and make intelligent predictions.
Compared to traditional computer programming, consisting of a set of basic rules that governed every single action of a machine, machine learning represents a huge step forward. Now, computers can not only act on their own, they can actually “learn” from their actions.
This allows data scientists to create models that can make accurate predictions from the data fed into it. That’s why we now have applications that can accurately point out fraudulent activities or which transactions are less risky to enter.
What auto ML does in all this is it makes the process of machine learning simple and repeatable. Basically, it simplifies machine learning by making the entire process automated. This saves a lot of time, effort and makes the fruits of machine learning readily accessible. Here’s how it works.
The Process of Automated Machine Learning
Machine learning involves teaching machines how to make accurate predictions from data sets. The process typically starts with raw data and ends with a predictive model that can be used to make predictions.
There are several stages to the process. Although it’s a bit complicated, a simplified process will involve the following steps:
- Data gathering
- Data cleansing
- Feature engineering
- Model training
- Model validation
- Model deployment
There’s a lot of work involved in getting from the point of data gathering to the finished model. The process involves a lot of repetitive tasks. Especially between feature engineering and model training. This is where data scientists have to move back and forth trying additional approaches to make the model perform better.
Ordinarily, the process requires significant expertise and it usually takes data scientists months to make perfect. It may also require frequent revision with the availability of new data, and this only extends the project timeline.
Auto ML however ensures that most or, in many cases, all of the stages involved in machine learning are automated. You won’t need to manually cleanse, process or find the best formats for processing the data.
Of course, once auto ML enters into play, it means you won’t ordinarily be able to draw on the intuition of an experienced data scientist in the process. But auto ML compensates for this by its ability to try a range of approaches very quickly.
It can rapidly build powerful models that would have taken weeks or even months for skilled data scientists to develop the traditional way. This means you can deploy your models very quickly and revisions in model performance can be completed with little more than a thought.
Why Is Automated Machine Learning So Important?
The process of manually constructing a machine learning model is very skill-intensive and time consuming. It requires domain knowledge, mathematical expertise and computer science skills which can be a lot to ask from one person or team.
Apart from this, there’s still the task of actually being able to find and attract top data science talent. The prospects of being able to attract and hire this rare breed of data experts are slim.
Auto ML however makes it possible for companies and organizations in every industry to reap the benefits of insight driven progress that is possible with machine learning.
By automating those tasks that are necessary to develop and deploy machine learning models, auto ML allows businesses to implement machine learning solutions with ease.
Even better, it reduces the possibilities of model inaccuracies that may arise due to human errors and bias, thus ensuring that businesses can innovate with confidence
Automated Machine Learning with RapidMiner
At RapidMiner, we believe that automated data science and machine learning can exponentially reduce the time and effort required to create predictive models for all businesses and organizations regardless of size, resources or industry. This is why we created Auto Model.
With Auto Model, it’s possible to build predictive models (pretty much complete the entire machine learning lifecycle) in just 5 clicks. Once you load your data, Auto Model will automatically analyze your data and recommend the best machine learning techniques for the outcomes you want.
There’s no need for technical expertise. All you need do is upload your data and specify the outcomes you want, then Auto Model will produce the high value insights you need.
RapidMiner is a comprehensive data science platform that fully automates and augments the data prep, model creation, model operations processes. Request a demo for your enterprise today.