AutoML: Artificial Intelligence Democratized
A process known as automated machine learning (AutoML) automates some of the simpler and more complicated steps in the machine-learning lifecycle. This makes it possible for people without a theoretical background or experience with machine learning to help develop AI.
The Advantages of Automated Machine Learning
Efficiency: Users who use AutoML can automatically find the best neural network architecture for a given problem and transfer data to training algorithms. The time it saves data science professionals is enormous. Frequently, using AutoML, tasks that would take hours to complete can be finished in a matter of minutes.
Scalability: By enabling untrained users to use machine learning tools and technologies, AutoML aids in the deomcratization of machine learning. By bridging the talent gap, autoML tools enable businesses to scale their AI implementations.
Error Minimization: Before AutoML, data scientists were compelled to handle their data manually through time-consuming operations. These tiresome tasks frequently lead to errors that are caused by people. Data scientists were able to cut back on or completely do away with the time-consuming, repetitive manual tasks thanks to autoML.
How Important Is AutoML?
Expert-level knowledge in machine learning is in high demand, but supply is not keeping up. This is showing up in the number of open positions relative to the number of qualified applicants. By automating procedures that would otherwise be beyond the capabilities of anyone but a subject-matter expert, AutoML seeks to close this gap.
Because of automation, machine learning software has become user-friendly and approachable, making it possible for analysts, marketers, and IT staff who lack a background in data science to integrate machine learning into their daily operations. Scaling machine learning across various industries benefits all organizations by increasing productivity and effectiveness in the areas where it is most needed.
AutoML is for whom?
Both beginning and experienced AI practitioners can benefit from AutoML. AutoML offers a straightforward wrapper function that handles numerous modeling-related tasks that ordinarily require numerous lines of code.
Users gain from this in a number of industries, including:
- Financial Services:Traditional financial institutions and more recent fintech firms use AutoML technologies to address issues with AML, transaction fraud, failed credit risk lending, failed trades, and customer churn.
- Government: To improve fraud, waste, and abuse, predictive maintenance across agencies and communities, supply chain and logistics, internal and external cyber security, and human resources, government agencies are turning to AI and AutoML.
- Health: AutoML provides optimized data and best practices for hospital operations, clinical applications, life sciences and biopharma, precision medicine, supply chain and transportation, marketing, human resources, and finance to private and public healthcare professionals.
- Insurance: The insurance sector is utilizing AI to optimize customer churn, claims management, precision pricing, automated underwriting, and fraud detection.
- Manufacturing:Top manufacturers use AI and machine learning to cut costs and streamline operations by forecasting demand, predicting stock levels, performing predictive machine maintenance, anticipating returns, and spotting errors in their supply chains.
- Marketing: Marketing firms and agencies use artificial intelligence (AutoML) to generate market forecasts, the best ad placements, investment opportunities, targeted lead generation, upsell and cross-sell promotions, funnel forecasts, customer segmentation, and recommendation systems.
- Telecommunications: To ensure an ever-improving customer experience, the telecommunications sector heavily utilizes AutoML for predictive customer support, fleet management, fraud detection, customer retention, and optimized marketing.
Data Science vs. AutoML
The general consensus is that AutoML will eventually overthrow data science, and as a result, businesses should allocate more funds to it. The mentality of "AutoML vs. Data Science" is fundamentally flawed. While parts of the machine learning pipeline can be used by AutoML without a skilled data scientist, this does not invalidate data science as a field. In fact, by automating its repetitive aspects, AutoML more frequently serves as a data science accelerator. Data scientists can spend more time tackling complex technical problems thanks to AutoML.
Data mining vs. AutoML
Data mining examines patterns in existing data, and AutoML makes predictions based on those patterns.
AutoML vs. Statistics
While statistics concentrate on sample populations and hypotheses, autoML concentrates on predictions.
Conclusion
The benefits of AutoML are clear and many companies have already embraced this technology. If you're looking for a way to make your business more efficient, then it's time to explore the possibilities that Automated Machine Learning has to offer.
Companies using AutoML have had great results with it. Organize a free meeting with QTECH team to learn more about how your business can benefit from AutoML.
