Does The Popularity of AutoML Means the End of Data Science Jobs?
With the strike of the COVID-19 pandemic and urge people showed to understand the pattern of COVID-19 transmission, which was being formulated by the huge data being available in the healthcare community, people were more and more inclined to learn Machine learning and Data analysis than ever before and this urge is going to retain even post-pandemic.
While going through Data preprocessing for Data science projects you would have felt the thirst for an automated method which would have saved your time from the conceivable exhaustive process of preprocessing followed by trying different models which would properly fit your data.
The answer to this need is YES, there exist tools that would not only automate the preprocessing stage but would also choose the relevant model for predictive analysis.
“The need is for Automated Machine learning (AutoML)”
This article will give you a brief idea about the pros and cons of AutoML in present times and try to give you a view on “Does the Popularity of AutoML mean End of Data Science jobs?”
What is AutoML?
Automated machine learning (AutoML) is the process of applying Machine Learning (ML) models to real-world problems by giving just the initial commands and the rest of the pipelines are taken care of by the program itself. More specifically, it automates the selection, composition, and parameterization of machine learning models. Internally, it creates different pipelines by choosing the different hyperparameters and choose that pipeline that gives maximum accuracy for our dataset.
AutoML has become the new trend in the field of machine learning. Its aim to automate the whole cycle of Machine learning and Deep learning projects. Machine learning is definitely the most thriving technology. For a newbie, it takes a lot of time to learn the coding stuff even after being thorough with the theoretical part. AutoML becomes the need of the hour for the researchers and enthusiasts from different domain who want to apply machine learning techniques to their work but does not have much time to spend on learning the coding part in depth.
“Automated machine learning (AutoML) aims to lower the need for skilled Data Scientists to build Machine learning and Deep learning models”. This is what is said by certain sections in the field of machine learning, but despite the ideology of having a negative impact on jobs, there are some people who are optimistic about the development in AutoML and believe that it will revolutionize machine learning sector.
Pros of AutoML
1. Accessibility: Professionals and researchers from other domains of specialization without much experience in Machine learning can use AutoML for their project without worrying much about the exhaustive and redundant processes of data preparation and other processing stages including model selection.
2. Efficiency: For regular Data scientists and Analysts, AutoML can save much of their time in redundant steps which could have been utilized in making the models more optimized by tuning the hyperparameters. Hence, it increases the efficiency of the work.
3. Less Errors: Codes are often prone to errors. AutoML helps to reduce human errors in the common steps. You wouldn’t have to worry about some errors in the former stages which would eventually ruin your future predictions. AutoML is analogous to using a calculator for multiplication, in which rather than doing operations and steps manually we can straight away use a calculator for multiplication to arrive at the same result.
4. Cost savings: This will be extremely useful for small companies or startups that can’t afford to hire an ML professional to build their recommendation or sales forecast systems. But for large projects, you’ll still need people to do the modeling.
5. Fulfill Industry Demands: AutoML will make the process of learning ML, too many other professionals from other domains easier, which would eventually attract people to switch to Machine learning and analyst jobs which would fulfill the ever-growing demand for human resource in this sector.
Shortcoming / Challenges to AutoML
1. Computational power: It will require more computational power to choose the correct model for the data, which will require the data to go through every model and fit it to find the accuracy, but if we try to do it manually we can trade-off this problem by eliminating many models which will certainly not work well with our data.
2. Difficulty with varying Datasets: AutoML is usually generalized to different forms of the dataset at present. But every dataset can vary in the relevance of features, its structure, and datatypes present in features, AutoML can do a significantly satisfactory job with most of the datasets but it can not meet the accuracy and persistence which could be meet manually.
3. Black Box: Although AutoML improves efficiency in producing results it can be difficult to track the flow of the algorithm that has delivered the respective output. Moreover, this also makes it difficult to choose the right model for a given problem, because it can be difficult to predict a result of the process selecting it, is a black box.
What AutoML Platforms are Available?
1. Google Cloud AutoML: Google Cloud AutoML gained popularity due to its user-friendly interface and high performance. Build your own custom machine learning model in minutes.
2. Microsoft Azure AutoML: Azure AutoML offers a transparent model selection process to its users who are not that familiar with coding. It is a cloud-based service for creating and managing machine learning solutions. Azure as a platform can be learned without knowing any programming at all.
3. H2O.ai: It offers both an open-source package and a commercial AutoML service called Driverless AI. Since its inception, this platform has been widely adopted in industries, including financial services and retail. It enables organizations to rapidly build world-class AI models and applications.
4. TPOT: TPOT (Tree-based Pipeline Optimization Tool) is a Python package that is free to use. Albeit free, the package has achieved outstanding performance in various datasets: around 97% accuracy for the Iris dataset, 98% for MNIST digit recognition, and around 10 Mean squared error(MSE) for Boston Housing Prices prediction.
Does AutoML eliminate data science jobs?
The answer to the given question is “NO”. Here are the reasons that support my view.
1. While AutoMLs is quite satisfactory at choosing models most of the time, but they are still not capable of doing most of the work of a Data Scientist. We still need Data scientists/Analysts to apply their domain knowledge to generate more useful features(Feature Engineering)and information that impact the target outcome.
2. AutoML will not replace most of the data science positions, instead, it can help professionals to fast the phase of their projects.
3. Machines are not intelligent enough and the algorithms often don’t generalize and understand the context of a problem.
4. AutoML can help us find a suitable model for a given problem but it can’t come up with a new approach which is often required for emerging real-life problems altogether.
This article introduces you to AutoML and makes great efforts to remove myths behind this emerging method. This article is based on my research, mythologies, and thoughts.
I hope all the things explained in this article are clear to you.
Please don’t forget to share your thoughts on this with me.
If you have any queries, please ask them in the comments below and I will do my best to resolve them.
About the Author
Currently, I pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence. If you want to connect with me here is the link to my Linkedin profile.
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