Around 40% of the data science tasks were to be automated by the year 2020. That was two years ago and since then there has been seen a rise in AutoML software. But let’s see why there is a need to automate ML.

A machine learning workflow starts with sourcing the data that you think might be useful to the problems that you are trying to solve, from various sources. Next comes the step of cleaning up this data and removing all the common errors that can be found in any real-world raw data set. Finally, in the data pipeline, comes the step of feature engineering which tries to extract features that will give the best data and an ML model that performs optimally.

After running some analysis on the pre-processed data, the features are sent on to the model for training. The selection and building (mostly in the case of neural networks) of ML models is the next step in the ML pipeline. This is a crucial part and various models can be trained and tested to see which one fits best with the data. After the model is selected and also trained, the model is tested and tuned to remove any overfitting through hyperparameter changes, and then finally comes the step of deploying this model through various means to create an interface for the users to use the model.

All the steps mentioned above show us the entire machine learning lifecycle, that more or less remains the same for every other use case. But implementing this entire process can take months or even years in some cases. Most of the time of a data scientist is spent on the data pre-processing and feature engineering steps to get the data right and even automatic computational tasks such as model training can take up hours and sometimes even days or months in some cases.

Due to such long durations of getting just one project up and running and looking at the cost that was invested into such endeavors, companies fall into a cost-sunk fallacy, and they have to incorporate a potentially faulty model now with limited performance, into their workflows and even if the model performs the best in the first try itself, it will not continue to do so due to various reasons such as data and model drift. Hence, it was necessary to deal with this problem and build a system that implements the entire ML pipeline without the heavy cost and time requirements.

To solve this problem many companies are developing automated ML platforms which do the heavy lifting for all the ML tasks so that businesses can save time and expenses, to ultimately focus more on their business side instead of dealing with the tech and the expertise needed for ML. Many such systems are already out there and most of them come under the term called AutoML platforms. But before understanding them, let us first understand what AutoML is.

What is AutoML?

AutoML is a technique or a framework using which almost all the tasks within an ML lifecycle can be automated and several smaller ML models can be deployed to handle these tasks themselves with a minimum or no input from a human. From data engineering and feature engineering to model training and hyperparameter tuning, every step can be automatized using various methods, saving a lot of user dependency and an increased efficiency by the system.

AutoMLplatforms provide AutoML as a feature but the amount of flexibility and the functionalities can vary from platform to platform. Some of these platforms are optimized for a particular use case and hence work best when used just in that use case.

Features of AutoML

For Data:

AutoML provides various data engineering solutions such as data cleaning and pre-processing on the given data. ML-based data wrangling can modify and transform the data based on the data requirements to generate standardized and usable data automatically without any user input.

Feature Engineering:

Various methods such as Feature tools, AutoFeat are already present in the industry that help with automated feature engineering and gives out reports on which features will be more impactful for the model inference leading to the development of a model that performs in the best possible way.

Analysis:

AutoML can be used to perform a quick analysis on the data by generating data summaries to check for any inconsistencies and by visualizing the data through various charts for users to get more deeper understanding on the data set and help with the data analysis.

Model Training:

AutoML can select ML models based on the data set features automatically and can train them on the data without any input from the user in the form of code or anything else. An ML model will be trained and will be made available to the user for deployment purposes without any need to look into any performance issues.

Model Tuning:

Using AutoML, ML models can also be tuned automatically by selecting the best set of hyperparameters that give the most accurate performance. This helps in building models that are robust and built for the real world without any need for human supervision.

Explaining:

Some AutoML platforms also incorporate model explanations as features within their offering. eXplainable AI (XAI) based solutions within an AutoML platform help with understanding the model decision-making process automatically within a few clicks helping the users to build AI systems that are responsible and fair.

Limits of using AutoML

Explaining the model results has been gaining a lot of importance lately. But automating the entire process itself might lead to more explanatory issues as the ML black box concept persists and is amplified when everything is automated. Hence, the explainability of the entire system is further lost and/or reduced when AutoML is implemented.

Also, automatizing the entire ML lifecycle leads to a decrease in flexibility for many ML developers trying to build models under certain constraints. As the entire process is automatic and mostly follows a low code or a no code approach, the power and customizability that code brings with it is lost, leading to systems that you have to trust that do work.

So, despite having a lot of benefits such as cost and time savings and an ability to run experiments and fail faster, AutoML brings with it a whole bunch of challenges too.

ANAI, an AutoML platform with much more

ANAI is an all-in-one ML-based solutions platform that manages the A to Z of AI, from data engineering to explainability. We offer an AutoML solution that focuses more on a no-code-based approach with a low code solution to be released in the future. ANAI’s AI engine easily outperforms and provides the best performance for any AI-based system.

ANAI comes with over 300 plus Unique ML models, 100 plus data ingestors, and automated data and ML pipeline to make your AI experience smooth and faster. We also have more than 25 plus Explainable AI models to derive explanations from your model’s results. And all this within a few clicks.

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Connect with us for more details on the solutions that we provide and/or other queries that you might have at info@anai.io or visit www.anai.io.