This informal CPD article How Auto-ML can help businesses was provided by The Tesseract Academy, offering consultancy services to help your company become data driven, whether you are an entrepreneur, a start-up or a corporate.
How Auto-ML can help businesses
Automation of AI-based ML, or "AutoML," addresses practical issues by replacing the time-consuming, labor-intensive, and repetitive methods of classical ML. It runs the complete ML gamut end-to-end, from the raw dataset to the deployable ML model, using statistical approaches or algorithms to train the computer to improve its performance.
The following are some examples of how businesses might benefit from AutoML:
Saves time and cuts down on mistakes
Expert data engineers and scientists do extensive manual testing, tuning of model hyperparameters, and evaluation to determine the optimal model for a given situation. Unfortunately, human error and prejudice may creep into the implementation process. Automated machine learning (AutoML) technologies may run a wider variety of ML algorithms in order to find the optimal one, which data scientists may have overlooked.
It can search for the ideal neural network design for a problem in a matter of minutes, as opposed to the hours it would take to do it manually. It also helps organizations achieve a better degree of accuracy and a higher return on investment (ROI) on ML initiatives by lowering the likelihood of mistakes that come mostly due to human operations.
Equalizing, closing the proficiency gap
Accelerating the democratization of ML tools by making them available to a wider audience, including ML developers, data scientists, and non-technical consumers. According to a poll of 80 data scientists done by CrowdFlower, just 20% of a data scientist's time is spent on real data analysis, while the other 80% is spent on discovering, cleaning, and reorganizing massive volumes of data.
McKinsey predicts that the rise of AutoML will change the way businesses staff open positions. The use of AutoML will allow practitioners to carry out easier data-science activities, similar to the role biochemists play in the drug discovery process. In light of the dearth of available ML specialists, AutoML will enable individuals with less AI-related skills to develop, implement, and track AI/ML-based models.
In a nutshell, AutoML's vast capabilities will assist in filling the void left by the current lack of qualified workers. It puts the potential of sophisticated ML squarely in the hands of business users, while it won't replace data scientists.
Increase efficiency and allow for easier scaling
To AutoML, data scientists may spend more time solving actual business issues instead of doing tedious iterative modelling. To speed up and scale up the deployment of cutting-edge ML models, use AutoML tables. As an added bonus, it makes it easier to apply ML to real-world challenges by streamlining the process of creating, testing, and releasing ML frameworks, thus increasing output. It has an easy-to-use interface for non-technical users and a full set of APIs for automation. To further guarantee precision and accuracy, AutoML automates data storage by finding vulnerabilities and incorrect settings. In addition, it may provide new possibilities for firms who lack the time and means to develop ML models in-house.
Conclusion
AutoML is a useful tool for improving data science processes in companies that have adopted a data science strategy, especially on teams that lack skilled ML personnel. AutoML is not a substitute for data science, but it may enhance models by automating mundane ML operations. If you don't have a plan, no amount of technology will help. AutoML may help if your company wants to draw conclusions from its data but is having problems hiring suitable ML specialists to fill the vacancies. Using AutoML, a company may drastically minimize the time and energy needed to construct time-series models.
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