June 20, 2024
by Kavya Ram Mohan / June 20, 2024
Artificial intelligence (AI) is significantly transforming every industry today.
However, many companies, especially in the small and midsize enterprise (SME) and mid-market segment, face several roadblocks to AI adoption.
The first is the high cost of hiring quality data scientists to work on AI projects. And even with a budget in place, finding the right people can be tough.
Complexities of traditional machine learning (ML) development is the next barrier. If not carried out correctly, errors and failures can occur in several areas.
Another constraint in implementing AI is the need for more imagination around its potential. Many businesses are unable to grasp the creative possibilities AI offers. This hinders the effective utilization of AI, limiting its impact and hindering its full potential.
This is where advancements in automated AI (AutoAI) and automated ML (AutoML) are changing things.
AutoML automates key steps, such as feature selection and model training, in the ML lifecycle. This makes it possible for businesses without extensive data science resources and expertise to leverage AI.
AutoAI takes this a step further.
AutoAI automates the entire AI lifecycle, including data preparation, model deployment, and even ongoing optimization. While AutoML focuses narrowly on the model development part of the process, AutoAI is an end-to-end process that takes a user from start to finish with ideating the project, building AI, and simplifying every function needed.
This new wave of automation is empowering businesses of all sizes to democratize AI and unlock its potential for solving real-world business problems. The market for automated machine learning is projected to grow from $1 billion in 2023 to $6.4 billion by 2028.
AI is a vast field focused on creating intelligent machines capable of performing tasks that require human-like intelligence. These tasks include learning, reasoning, problem-solving, and so on. On the other hand, AutoAI represents the intersection of automation and AI.
So how do AI and AutoAI intersect?
AI and AutoAI work together.
AI is the vast field of creating intelligent machines, while AutoAI lies at the intersection of automation and AI. Imagine AI as a complex engine, and AutoAI as the tools that make assembling and using that engine easier. AutoAI leverages AI advancements to automate tasks and make AI more accessible to a wider range of users.
Traditionally, building and deploying ML models was like tailoring a bespoke suit. A skilled data scientist had to analyze the data, identify the right model architecture, and meticulously fine-tune its parameters to achieve the desired outcome.
However, this approach restricted the adoption of AI to businesses that could afford expensive data science teams and tools.
Enter AutoAI. AutoAI automates the entire lifecycle of AI development, including tasks like data preparation, feature engineering, model selection, hyperparameter tuning, model deployment, and data app or dashboard creation to showcase the results.
It uses AutoML when the data is ready for model development, making it one of many steps in the AutoAI pipeline.
This shift towards automation and, subsequently, AutoAI is driven by several key factors:
Businesses globally generate enormous volumes of data, which is difficult to analyze manually. AutoAI helps organizations extract valuable insights from this data. On the contrary, some companies have too little data, and this, too, needs to be solved during the AI process.
AutoAI can help companies with limited data by using techniques like data augmentation and transfer learning to get the most out of their data and build models faster.
In a competitive market across industries, businesses are seeking and adopting AI-powered solutions as tools to automate tasks, optimize processes, and provide companies with a competitive edge.
The demand for skilled data scientists far exceeds the current supply, driving up costs and limiting access for many businesses.
growth in the number of data scientist roles is projected between 2022 and 2032.
Source: U.S. Bureau of Labor Statistics
AutoAI helps bridge this gap by democratizing AI development, allowing non-experts to build and deploy models without extensive data science expertise.
Even with access to data science teams, it is commonly accepted that data scientists and practitioners spend 80% of their time finding, cleaning, organizing, and preparing data for analysis. AutoAI streamlines these processes through automation, freeing up valuable time.
By automating the complexities of the AI process, AutoAI is paving the way for a future where AI is not just for tech giants and other large enterprises with multi-member data science teams but a powerful tool accessible to businesses of all shapes and sizes.
AutoAI offers a range of benefits for businesses of all sizes, transforming the way they approach data analysis and decision-making. Here's a closer look at some key advantages.
AutoAI automates mundane, time-consuming tasks like data cleaning, feature engineering, and model selection. This frees time for data scientists and business analysts to focus on higher-level activities like strategic planning, model interpretation, and business process optimization.
Traditionally, AI development was an exclusive domain limited to companies with significant financial resources. AutoAI breaks down this barrier by offering user-friendly, no-code interfaces.
This allows domain experts from various departments, including marketing, finance, and operations, to explore AI solutions for their problems. For example, a marketing team can use AutoAI to analyze customer data and curate targeted campaigns without deep technical knowledge.
AutoAI automates the process of model selection and hyperparameter tuning. This eliminates human bias and ensures businesses leverage the most suitable models for their specific needs.
Additionally, AutoAI often explores a wider range of algorithms than human data scientists might consider, leading to the discovery of potentially better-performing models.
These high-performing models generate more accurate predictions and insights, empowering businesses to make data-driven decisions that optimize processes, improve customer targeting, and mitigate risks.
AutoAI's streamlined process significantly reduces the time it takes to develop and deploy ML models. Businesses can iterate on different models quickly, identify the best solution, and implement it faster. This translates to a quicker ROI.
AutoAI platforms incorporate explainability features that help users understand how models make decisions. This transparency can be crucial for regulatory compliance, allowing businesses to demonstrate that their AI systems are fair and unbiased.
As AutoAI matures, explainability will likely become a key differentiator, ensuring responsible AI adoption across all sectors.
A leasing company wanted to assess applicants' credit risk and make real-time decisions. Previously, they used a rule-based system with slabs for different leasing amounts.
With AutoAI, the company now makes precise credit risk assessments and fine-tunes decisions down to the final dollar, breaking free from slab constraints. This shift has provided them with accurate risk assessments and the opportunity to maximize their business.
AutoAI integrates data from diverse sources, including internal records, external databases, and user-provided information. It analyzes this data to identify patterns and anomalies in applicant profiles.
It also derives new features, such as financial ratios and email trust scores, to enhance the risk assessment process. The platform then builds predictive models that clearly explain their decisions, fostering trust and transparency. This streamlined process allows the company to make more accurate and data-driven credit risk assessments.
of the company's revenue was positively impacted by their ability to detect risky lease applications.
Source: RapidCanvas
AI is no longer restricted to a few businesses or technical users within an organization. With the availability of AutoAI platforms, businesses of all sizes can leverage the power of ML to solve real-world problems.
Here's a step-by-step guide to get you started with AI while leveraging the power of AutoAI.
The first step is clearly defining the problem you're trying to solve with AI. Here are some questions to consider:
You can tailor your AI exploration to find the most suitable solution by clearly outlining your business needs.
There's a growing landscape of user-friendly AutoAI platforms available, each with its own strengths and target audience. Here are some key factors to consider when choosing a platform:
Many AutoAI platforms offer free trials or limited-functionality demos. This allows you to experiment with the platform's interface, test its capabilities with your specific data type, and assess its ease of use for your team before committing financially.
Utilize these trials to explore various platforms and identify the one that best aligns with your needs and skill set.
Don't attempt to tackle large-scale projects right away. Begin with a well-defined, focused problem within your organization.
This allows you to learn the platform's functionalities, gain confidence in AutoAI's capabilities, and showcase the value proposition to stakeholders before scaling up.
Garbage in, garbage out is a well-known rule of thumb in the AI universe. Ensure your data is clean, well-organized, and relevant to the problem you're trying to solve. Common data preparation steps include:
Leading AutoAI platforms offer built-in data preprocessing tools to simplify this step and allow users to obtain a ready-to-use dataset with a few simple clicks.
AI is an iterative process. Don't be afraid to experiment with different models, settings, and data pre-processing techniques. Most AutoAI platforms allow you to compare the performance of different models and visualize their results.
This experimentation phase helps you better understand your data and identify the best AI solution for your specific needs. With AutoAI, the process of experimentation gets shortened, improving the overall performance.
The power of AI doesn't stop at deployment. Continuously monitor your model's performance in production. As your data evolves or business needs change, you may need to retrain or refine your model to maintain optimal performance.
A wealth of online resources can empower your AutoAI journey. Many platforms offer comprehensive documentation, tutorials, and online courses to guide users. Industry communities and forums also provide valuable peer-to-peer learning opportunities and insights from experienced users.
By following these steps and leveraging the wealth of available resources, businesses of all sizes can overcome traditional barriers and embrace AutoAI's transformative power.
With AutoAI streamlining the AI lifecycle, businesses of all sizes can access it without data science expertise. AutoAI is a constantly evolving invaluable tool, facilitating increased efficiency, better decision-making abilities through high-performing models, and faster time to value.
Starting with AutoAI is easier than ever. Businesses can now identify their AI problems, explore user-friendly AutoAI software, process data, and experiment with various models. We can expect to see even more user-friendly interfaces, advanced automation, and smoother integration with existing business intelligence tools.
However, as with all technological advances, ethical considerations like bias and transparency must be considered alongside the benefits. It's essential to employ responsible development and implementation practices to ensure that AutoAI benefits all its users.
AutoAI is democratizing access to AI, empowering businesses to push boundaries and contribute to societal development and economic growth.
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Edited by Supanna Das
Kavya Ram Mohan is a senior content marketing specialist at RapidCanvas, the AI platform for business users. A journalist-turned-marketer, she writes about AI, and technology.
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