Automated machine learning (AutoML) is a set of tools and techniques that automate the design, training, and deployment of machine learning models. AutoML has become essential due to the amount of data involved when creating ML models, helping to save a significant amount of time, human resources, and money.
Although manual machine learning is not obsolete, automating parts of the process is necessary to help boost efficiency. Manual ML may be required when complex or specialized data is being processed, or if the data set is small enough to handle.
In this article, we will discuss when to use AutoML over manual ML, focusing on the advantages and disadvantages of both in this full guide.
What Is the Purpose of Automated Machine Learning?
Automated machine learning is growing at a rapid rate, aiming to significantly improve Data Science to make it more effective and accessible. All stages of the machine learning workflow can be automated, from the initial data preparation to selecting the right model. AutoML tools can work in just a few clicks, resulting in impressive savings in terms of both time and money.
Machine learning algorithms are designed to solve problems and assist humans to provide more accurate solutions. Developing these algorithms can take a lot of time, which is why data scientists and machine learning engineers have looked to reduce manual tasks within the pipeline as much as possible. Without automation, many projects simply wouldn’t be viable.
AutoML is especially important for data scientists or organizations that are new to the world of machine learning, or perhaps lack the funds to hire enough human resources to deliver a project successfully. The time and money saved by automation can also lead to more innovation, allowing engineers to explore new opportunities and be more creative, instead of being bogged down by completing manual tasks.
What Is Manual Machine Learning?
Manual machine learning avoids the use of an automated platform, relying on experienced data scientists and engineers to use a manual workflow. Tasks such as data collection, data manipulation, model training, and model evaluation are all taken care of manually. The initial stages of this process may be even performed by a data scientist on a single, locally run computer before access is provided to engineers to create the API endpoint.
There are some drawbacks when it comes to manual pipelines, especially when various stages of the pipeline need to be repeated and documented numerous times, resulting in a time-consuming process. Collaboration can also prove troublesome if a data scientist has a particular way of working that requires engineers to decipher their notes.
The key characteristics of a manual ML pipeline include:
- The ML model is often the product
- Processes are script-driven
- Iteration cycles can be slow
- Collaboration between data scientists and engineers can be problematic
- Testing and performance monitoring is not automated
- There is no version control
AutoML vs. Manual ML
Depending on an engineer’s expertise, there may be certain stages of the ML pipeline that may benefit from manual input instead of being automated. Therefore, it is important to understand the advantages and disadvantages of AutoML and manual ML.
Level of Expertise
AutoML makes machine learning more accessible, allowing individuals that have a relatively limited level of experience to build working models. However, experienced engineers can also benefit from auto ML, enabling them to work quickly and reallocate their time to explore new opportunities.
Building models manually, on the other hand, requires a high level of expertise and a full understanding of the different ML algorithms, techniques, and concepts. This also includes a strong knowledge of the chosen subject area so the correct algorithms can be selected.
With an AutoML platform, the entire ML pipeline is automated, from the initial data preprocessing to the model selection and evaluation. These automated tools make the process as simple as possible, greatly increasing accessibility.
Without an AutoML platform, every step of the machine learning process requires manual input, which means it can only be performed by someone with expert knowledge. In a manual MLOPs environment, there is also a range of processes that can be adopted to improve workflows.
Customization and Control
One trade-off when it comes to AutoML is the level of control and customization that is available. As AutoML focuses on being accessible, it reduces the number of available options that could be confusing. As such, this lack of control may not provide experienced engineers and data scientists with the options they need to make models more bespoke, complex, and better performing.
AutoML is designed to save both time and human resources, speeding up certain tasks that can be laborious. This greatly reduces timescales when building ML pipelines and is preferable when working with large datasets, or if quick turnarounds are required.
Alternatively, manual ML may result in a better end product when built by an expert who can customize the model for optimal performance and fine-tune data input.
When to Use AutoML
AutoML can improve ML performance in a lot of ways, but an engineer with expert knowledge and years of experience may find automated processes somewhat limiting. Therefore, it is important to understand when to use manual techniques over an automated solution.
Let’s look at when to use AutoML:
1. Structured Data: Auto ML is recommended for projects that use structured data, featuring organized rows and columns that are preformatted to be used with AutoML tools. These tools will handle the imputation of structured data regardless of whether some of the data is missing. In addition, AutoML tools will also encode any categorical variables and normalize numerical variables.
2. Small-to-Medium Datasets: AutoML is ideal for small-to-medium datasets as training ML models that use large datasets can become time-consuming and costly. AutoML is trained to manage multiple different models, so it is much more efficient to deploy models that rely on smaller datasets. As a rule, datasets containing up to 50 features (columns) and up to 100,000 rows are considered medium-sized.
3. Rapid Prototyping/ Proof of Concept: The majority of ML projects take shape with an initial proof of concept, and with AutoML, these concepts can quickly develop into a working prototype. Built-in data analysis tools provide full visibility of a project, allowing an engineer to determine if it is viable. Engineers can also rely on dashboard templates that can break down complex datasets into easy-to-follow data visualizations and more manageable chunks of information. This is a great way to help simplify data analysis so data-driven decisions can be made much easier to make.
When to Use Manual ML
Below are a few situations where manual ML makes more sense:
1. Large Datasets: As touched upon in the previous section, large datasets are not completely suitable for AutoML. It may prove much more effective to execute experiments manually, allowing hyperparameters to be selected, and giving engineers much more flexibility to customize datasets to their needs.
2. Deep Learning: Most AutoML tools cannot engineer deep learning features from unstructured data, though there are a few that may be integrated with a deep neural network. The hyperparameters involved with deep learning are often too vast to be viable for Auto ML platforms and manual customization is required to evaluate models successfully.
3. Complex Use Cases: Some use cases can be considered too complex to be suitable for AutoML, as some of the metrics can be difficult to analyze. Custom logic needs to be applied to judge performance, allowing data scientists to experiment and configure the best solution based on their knowledge and experience.
If you are relatively new to the world of ML, then an AutoML platform is recommended to introduce to building ML models. AutoML is also recommended for organizations that may not have the time and resources to build ML models manually.
For experienced engineers or large organizations that have the required resources, building models manually may be the better option, as this can usually result in higher performance and a more effective end product.