How to successfully implement automated machine learning
 
 

How to successfully implement automated machine learning

4 min read

 

Conveyer belt

 

Machine learning has changed the way we work with data. Lengthy projects are shorter, complicated data is transformed into actionable information, and unforeseen insights are uncovered from sources we thought exhausted. Human involvement has changed as the field of machine learning (and artificial intelligence as a whole) has grown; humans can now spend their energy training machine learning models instead of combing through data by hand. It’s a natural progression to want to automate machine learning—to have a machine manage the machine—because of the immense time and effort it could save. With the shortage of data scientists available in today’s market, simplifying data through new solutions is more attractive than ever. However, as with all artificial intelligence matters, this new strategy deserves awareness and careful planning. Let’s take a closer look.

 

What is automated machine learning (AutoML)?

Regular machine learning includes supervised, unsupervised, and reinforcement learning, each of which requires a different level of human involvement. Automated machine learning is the use of an algorithm to manage the development and deployment of machine learning models. Tools like H20, TPOT, and Autosklearn provide open-source, technical solutions, while other providers like Google AutoML Vision pair automation with plain-language user interface.

 

Two important steps in the machine learning process can be automated: pre-processing and model selection. This includes sub-steps like data ingestion and data preparation; feature engineering, extraction, and selection; and model selection.

 

Let’s look at a real-world example in the field of marketing. Let’s say you want to cut down on the time required for segmenting your audience. Steps for a normal machine learning process might be:

 

1. Collect data

2. Clean data

3. Identify features

4. Select a model

5. Train and retrain the model

6. Optimize parameters

7. Use the model to segment data based on new relationships found in the data

 

Important things to consider

When considering an automated solution, it's important to remember a few things:

 

They simplify and save time. Automated machine learning serves as a bridge between less technical employees and data insights. These tools offer a way to create simpler solutions, faster. For businesses with limited technical resources, this can be especially valuable.

 

They aren’t dynamic. Automated models are created for a particular use case and don’t adjust when needs change. Without human involvement, they are unable to detect new signals in their environment or retrain themselves.

 

How to implement AutoML successfully

With the increase in non-data scientists working in data science, it’s important to follow AutoML best practices. Fortunately, they’re pretty simple:

 

1. Reevaluate periodically. Your dataset and/or use case may change over time, so you must maintain accuracy by checking in periodically to ensure your model still fits.

2. Retrain as needed. When you discover your model no longer fits, retrain it as necessary.

 

Let’s return to our marketing campaign example.

 

Your machine learning has selected an algorithm that successfully processes your cleaned data and has helped you create a handful of new and valuable personas. Now, your goals have changed slightly, and you’ll need to angle your messaging using a slightly different set of keywords. This new data does not fit the once-successful algorithm selected and refined by machine learning. Parameters and goals have changed, so you’ll need to reevaluate and retrain your model on this new dataset.

 

Automation doesn’t eliminate the need for human involvement or a comprehensive, end-to-end procedure. Without these two things, consistently achieving reliable results is not possible. However, when used correctly, automated solutions can save time by expediting iterative processes and allowing data scientists to focus on more complicated tasks.

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