In the ever-evolving landscape of technology, a new star has emerged on the horizon: AutoML. This stands for Automated Machine Learning and it as of the beginning was introduced to the data science community as a way of enhancing automation and efficiency. Which brings the question – what is AutoML and should data scientists start searching for a new job? It is, therefore, time to get to the bottom of this interesting field and separate what is true from what is false.
Understanding AutoML: The New Frontier to the Science of Big Data
AutoML or Automated Machine Learning is like having a personal assistant to data scientists. Sometimes, you are preparing a very competitive meal and instead of having to peel all the vegetables manually, you have a helper who can prepare the ingredients in seconds. That’s what AutoML does for data science tasks.
AutoML, in its simplest sense, is an array of smart tools and techniques to help some portion of the process of machine learning to be accomplished by the system. It is important not to see the change as a substitution of the chef (data scientist) but as a process that gives him/her the ability to concentrate only on the value-added part, which is creativity and strategy. AutoML is not a threat to data scientists. More like an economic partner to help them in their own crusade of finding the myriad of secrets in oceans of data.
What can AutoML do, in fact?
Data Preprocessing
AutoML tools are also capable of sorting time-consuming or boring tasks like data cleaning, managing missing values, and one hot encoding of categorical features. This used to consume a lot of time for data scientists, while with AutoML it takes seconds.
Feature Engineering
Filtering signals from noise and constructing features with the rich data available from inputs is something of an art form. AutoML can never imitate creativity on its own but it can propose what it considers the best feature and check many variations in features in what would take one ages to do manually.
Model Selection
Selecting the best of all the available machine learning algorithms is a challenge. AutoML is capable of feeding multiple models and provides the analyst with a list of the best performing models in relation to a given dataset.
Hyperparameter Tuning
As for selecting settings for a model in machine learning, well, it used to be as easy as finding a needle in a haystack. AutoML tools can exhaustively search for the hyperparameters in a more systematic way and sometimes find a set of configurations that might be easily missed by the human operator.
Model Evaluation
AutoML is not only able to construct the models, but also can evaluate the models’ results with different evaluation methods and cross-validation.
Many existing AutoML platforms such as AutoKeras, Google Cloud AutoML, TPOT, and H2O.ai provide these functions to as many people as possible as it caters not only to professional data scientists but also beginners.
The Human Touch: Why Big Data Scientists Still Hold Their Ground
Problem Framing
As with any analysis, before we can get to any and we’re done, someone somewhere needs to frame the situation in a way that’s manageable and useful. This implies that the person needs to have a good understanding of the business environment and have the capacity to define the business aim within the domain of data science.
Domain Knowledge
Because every business is unique, there are idiosyncrasies that may or may not be shared with other establishments in the same trade. A data scientist with backgrounds in finance will know which variables are more likely to affect stock price movement, different from a data scientist who specializes in the field of healthcare, he/she will understand all about patients’ records. Such knowledge is important when it comes to the analysis of results as well as to the evaluation of whether the generated models are sensible and reasonable in the real-world context.
Ethical Considerations
With a rising utilization of artificial intelligence and machine learning concepts, issues of bias, fairness, and model interpretability cannot be overemphasized. It should be noted that human data scientists always act as a backstop to ensure that these automated systems are used appropriately and legally.”
Communication and Storytelling
Of course, data does not speak for itself. It also requires a perfect data storyteller, who will be able to turn complicated data driven insights into business cases. They are a human intervention that helps to link theory and practice to ensure dissemination of technical information.
Innovation and Creativity
AutoML can add value into already existing practice but it is human data scientists who advance the state of the art. It is them who come up with unique ideas, use methods together that no one would have thought of, and who advance the field.
Here’s how data scientists and AutoML can work together in harmony
Rapid Prototyping
AutoML is also useful to data scientists so that they can run through multiple techniques and get a quick benchmark. It can distinguish values which can be beneficial for additional research.
Handling Routine Tasks
AutoML was found to enhance productivity by avoiding the time that data scientists would spend on repetitive tasks.
Democratizing Machine Learning
AutoML is a great way to bring an intuitive and clear understanding of basic machine learning algorithms to people. This enables data scientists to communicate more with other teams and allows them to work on more complex problems.
Continuous Improvement
This is because with updates that are done to AutoML tools, then the best of practices and techniques can be included. This enables data scientists to be in touch with the ever-evolving field.
Scalability
AutoML can then take over some of the work so data scientists can work on more projects and help more organizations.
Future data scientists may spend less time on the nuts and bolts of model building and more time on:
- Strategy formulation and organization strategic management
- Availing result elucidation and meaningful analysis.
- Creating new algorithms for the company’s specific issues
- Protecting ethical and appropriate application of AI and machine learning
In this dynamic context, the ones who will manage to benefit from AutoML in the most efficient manner possible are those who see it as a tool more than an adversary.
Conclusion: Welcoming the Introduction of AutoML
To state that, as the future of data science, it’s a war between man and machine is and always has been entirely and utterly wrong. AutoML gives the necessary speed and efficiency, while human data scientists add the creativity, understanding of the problem area, and critical, or ethical, thinking that are required for true innovation.
So, to all the aspiring and current data scientists out there: don’t fear AutoML. Tackle it, understand it, and utilize it to enhance your competency portfolio. Data is a continuously growing field, and there is no better time than the present to be in the middle of these advancements.
The idea to describe the dichotomy of AutoML is to get rid of AutoML vs data scientists discussion. It is about synergistically leveraging heart and mind to get the most out of data and improve the prospects of the world for all stakeholders.
Frequently Asked Questions (FAQ)
What is AutoML?
A: AutoML or Automated Machine Learning in its entirety, is a systems’ approach that can itself be described as the automation of most of the tedious tasks that need to be performed in a machine learning process.
Will AutoML try to replace Data scientists?
A: Instead of eliminating data scientists, AutoML will instead assist them in their daily work. However, advanced human intervention is needed for formulating the problems, identifying the relevant domain knowledge and necessary code of ethics, embracing the outcomes, and analyzing them in practical scenarios.
Q3: AutoML tools have become a trend over recent years; what are some types of AutoML tools?
A: Some AutoML tools are AutoKeras, Google Cloud AutoML, TPOT, H2O.ai, AutoViML, and Microsoft Azure AutoML. Here are some of the tools which are designed to provide a number of functions to facilitate the ML process and fit all types of users and projects.
Q4: In what way does AutoML help the data scientist?
A: AutoML advantages include speed, the ability to do lower-left brain jobs, the ability to generate many prototypes, and the handling of repetitive work for data scientists.
Q5: Is AutoML feasible for non-professional personnel?
A: To some extent, we have seen that AutoML can be used by non-expert users in order to build models because the interface of tools like AutoKeras is quite intuitive and similar to that of sci-kit-learn. For that reason, while not as comprehensive as other texts, Machine Learning for Dummies democratizes the basics by simplifying them.
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