AutoML: Let AI Design Your AI to Drive Better Results In 2021
AutoML also has the power to help businesses overcome many difficult challenges. Let’s understand how.
2020 was a year of surprises – and a huge wake-up call for business leaders. The global recession caused by the COVID-19 pandemic created unexpected disruptions for companies, leading to unprecedented financial challenges. Now, as businesses adjust to the “New Normal,” many executives are turning to technology to better prepare for unforeseen changes and to manage business operations.
Artificial intelligence and machine learning-based algorithms offer a solution. AI and ML can help companies identify issues and react more quickly by showing what’s happening, why it happened, and what will happen next. The result? Leaders can take critical actions to improve operations, reduce costs, save time or boost prices, profits, and productivity. However, this technology has not been easy to understand and use – until now.
AutoML, the process of automating AI-based machine learning, involves statistical techniques and algorithms that improve programming and enable machines to learn from data to identify patterns that help solve business problems. According to a recent O’Reilly study, “AI Adoption in the Enterprise 2020,” the most critical ML- and AI-specific skills gaps in respondents’ organizations were a shortage of ML modelers/data scientists, a lack of understanding, and difficulties in identifying and maintaining a set of business use cases.
Automated machine learning (AutoML) is helping to fill these gaps. AutoML is resolving real-world problems by self-programming current AI-based systems to discover optimal solutions.
If 2021 is anything like 2020, the ability to manage unforeseen risk and make quick (and smart) decisions will be crucial to any company’s success. Implementing AutoML can help your business make wiser choices and plan for unforeseen risk, all without the manual AI analysis from data scientists.
Empowering Better (and Quicker) Decision-Making
Evolutionary algorithms, powered by AutoML, build a predictive engine, helping executive leaders produce actionable results from complex, multivariate problems that apply directly to their corporate goals.
With traditional computational methods, algorithms are explicitly programmed to answer specific problems. With AutoML, however, the system can recognize data patterns automatically and improve on experience without being explicitly programmed. The machine instantly learns from data, which leads to better recommendations for decision-making. This allows for a less time-extensive and more actionable process.
AutoML is vital to far more effective and widely available applications that complete tasks on their own and generate fundamentally new, profitable business models.
Crisis Preparedness with AutoML
AutoML also has the power to help businesses overcome many difficult challenges, such as the COVID-19 pandemic, natural disasters, and climate change. Using evolutionary algorithms equips companies with the data, forecasts, and recommendations they need to react to changes, anticipate them, and make the right decisions at crucial moments. This allows companies to drive innovation, long-term customer value, and business sustainability despite unpredictable circumstances.
Take, for example, the Louisiana hurricanes and their impact on supply and demand.
For retail businesses that manage their supply chain by forecasting quarterly, historical data would tell them if they have X orders of something like toilet paper last quarter, they’d need to order Y amount this quarter. However, those predictions can be made arbitrary in short order if a hurricane hits. That one occurrence can drastically change the demand for toilet paper. Using AutoML and advanced evolutionary algorithms, businesses can have a more fluid operation that enables them to fluctuate their supply based on real-time market conditions.
Advancing the Role of Data Scientists
Artificial intelligence is something that many business leaders desire, but few understand. According to a recent Cognizant study on responsible AI, 63% of respondents believe AI is already extremely or very important to their company’s success. However, according to Cognizant and Forrester’s study, “The Road to Data Modernization,” roughly a third of organizations are dissatisfied with the implementation of their data governance and management tools. Most of this dissatisfaction stems from a lack of ability to use these AI tools, with 49% citing employee talent and 41% specifying data science talent as major challenges.
With AutoML, you don’t have to be a tech expert to interpret advanced data.AutoML enables-house talent without expertise in the field to implement machine learning models, techniques, and solutions easily.
Optimizing your business process is never easy, especially given today’s challenging circumstances. Technology like AutoML makes business enhancement easy. By using automatic data analysis, companies can make smart decisions more quickly, prepare for crises, and expand the roles of their tech leaders.