Benefits of automated machine learning for your organization

What is automated machine learning?

Automated machine learning (autoML) — sometimes referred to as meta-learning — automates the end-to-end process of applying machine learning to real-world issues. It is an emerging alternative to manual development. Automation enables data scientists, AI engineers, and analysts to build high-performing ML models without sacrificing quality for scalability or speed.

Historically, only midlevel data scientists or competent AI engineers could build one. However, automation has democratized the process, combining user-friendly interfaces, no-code tools, and autonomous algorithms into an out-of-the-box solution for non-experts.

The traditional development approach is time-consuming and repetitive. Data scientists and developers must devote most of their workday to preparation, preprocessing, and model selection, leaving little time for training and deployment. This pain point will continue to worsen as the complexity of ML models increases.

How does AutoML work?

AutoML follows the typical steps of building a machine-learning model but does so with no manual intervention and minimal human oversight. It leverages algorithms and statistical modeling to train, tune, and test its AI. Automated machine learning tools typically offer no-code or low-code tools — meaning no explicit programming is required — for a hands-off approach.

This automation tool can handle all machine learning techniques for business — meaning supervised, unsupervised, reinforcement, or semi-supervised — making it particularly versatile. Although most companies use it for supervised learning applications like classification and regression, the other techniques are common.

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To begin, an individual provides a large preprocessed dataset. From there, automation takes over. It automates feature engineering — a data science technique to transform raw data into more effective artificial features for ML model development — to save data scientists’ time.

One of the main aspects of autoML involves ML pipelines. The platform automates the engineering workflow with a systematic sequence of steps. It can leverage thousands of algorithms and parameters during model selection and training to identify the optimal development strategy, enabling it to output increasingly superior options.

AutoML automates model tuning by leveraging algorithms to autonomously identify optimal hyperparameter values to maximize model performance. It evaluates all iterations upon completion, assigning each one a score based on the AI engineer’s predefined criteria. The higher the score, the more it meets their goal.

At this point, the decision-maker selects the highest-performing ML model and proceeds with deployment. While various platforms offer distinctive development features and workflows, automated machine learning methods generally follow this process.

Types of data AutoML can handle

When building a machine learning model with an automated platform, data scientists and decision-makers must consider what kinds of information autoML models support since selection is highly dependent on the dataset type.

Text

AutoML models can perform sentiment analysis or text classification on structured or unstructured information by using data science techniques to transform raw data into numerical or nominal features. For example, it can categorize reviews as positive, neutral, or negative.

AI engineers use ML pipelines to train and test several iterations simultaneously, optimizing these models for multiple languages and enabling translation. This approach is ideal for natural language processing algorithms.

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Video

Users can use autoML to train models to perform object tracking and recognition on video segments or snapshots. This enables them to select the most suitable model architecture for video analysis or summarization.

Image

Decision-makers can use autoML to train a deep neural network to perform image classification, object detection, and object recognition. It trains them to extract pattern, texture or form information, enabling them to label pictures with predefined markers or categorize them into predetermined classes.

Tabular

AutoML tools can train decision trees, large language models, or neural networks to perform classification and regression on tabular data — information stored in tables or columns — by improving quantitative relations analysis.

What is the difference between ML and AutoML?

The main difference between standard ML and autoML is the algorithm development process. Traditional AI software development relies on time-consuming manual tasks that a team of highly skilled AI developers and data scientists must conduct separately. It often requires team leaders to manage the process and translate jargon to non-technical decision-makers.

In comparison, building a machine learning model with automated machine learning is relatively hands-off. Anyone from a nonexpert entrepreneur to a senior engineer can use a platform or open-source library to develop autoML models. A set of algorithms and statistical models handle every step, from feature engineering to deployment.

Automated machine learning for business

AI adoption for business applications is on the rise. For reference, 50% of senior-level professionals on sales and marketing teams report they already use it — and another 29% plan to use it in the future. As demand and model complexity increase, leveraging autoML becomes increasingly important.

Business owners can use machine learning automation to develop several model types:

• Forecasting: Decision-makers can track purchasing behavior, market trends, or turnover patterns to predict when a specific event or change will occur. This enables them to act proactively and potentially grants them a competitive edge.
• Personalization: Since a single ML model can track or interact with numerous individuals simultaneously, businesses can use it in customer service or relationship management. They can deploy a chatbot, virtual assistant, or recommendation algorithm.
• Anomaly detection: AutoML platforms can produce an anomaly detection model to identify and address discrepancies in datasets.
  Asset cataloging: An autoML system can produce a model that catalogs, classifies, or labels image, video, or text data. Business owners can use it to index, analyze, or organize their information assets.
• Fraud prevention: ML models evolve over time and can quickly analyze a large volume of data, detecting inconsistencies indicative of fraudulent activity. Decision-makers can deploy them internally in accounting or for end-user protection.
• Insight extraction: Algorithms can monitor real-time data feeds or analyze documents to extract valuable, business-specific insights.

Complex applications like neural networks, deep learning, and machine learning aren’t just for large enterprises anymore. Organizations can use automated machine learning for business to minimize their reliance on data scientists to accelerate development and deployment. This way, they can seamlessly integrate advanced AI into their daily workflows.

Why organizations use AutoML

Decision-makers from the smallest startup to the largest enterprise can benefit from leveraging machine learning in business if they’re involved in model development. Automating time-consuming processes like preprocessing, training, and evaluation means they can accelerate their time to completion, enabling them to generate AI-driven business value sooner.

Employees spend much of their time on non-value-adding labor. While data scientists spend approximately 39% of their time on data preparation, they only dedicate 12% of their time to training. When they spend most of their workday filling in missing values, eliminating duplicates, and standardizing, they delay the positive return on investment.

Moreover, building a machine learning model requires a team of highly skilled professionals with, at minimum, intermediate expertise. ML development is less accessible to entrepreneurs, startups, and small businesses who lack qualified staff and adequate resources.

Combining machine learning and automation eliminates guesswork, enabling swift ML model development — even if the users are nonexperts who have never attempted such a task. Midsized businesses and larger firms can also benefit from using user-friendly platforms for complex applications like building deep learning algorithms or neural networks.

Another one of the benefits of autoML is efficiency. The resource hours companies save by automating their most time-consuming development lighten data scientists’ workloads and enable management to reduce labor and decrease their reliance on overtime. These minor improvements add up quickly since automated development is democratized.

Automated machine learning examples

Since autoML automates the process of applying machine learning to everyday business issues, it stands to reason that numerous real-world success stories exist. Several businesses have reported having successful machine learning projects after leveraging these platforms.

Back in 2018, PayPal used H2O.ai’s automated machine learning systems to improve fraud prevention. Even though its team had over 10 years of feature engineering experience, it still experienced significant post-implementation gains. It raised model accuracy from 0.89 to 0.947 — a 6% increase — and accelerated its development timeline by six times.

California Design Den, an online bedding retailer, experienced a 50% reduction in inventory carryover after utilizing Google Cloud AutoML. Leveraging this automation technology for E-commerce substantially improved its profit margins and increased its resilience to quarterly market fluctuations.

Trupanion, a pet insurance provider, is another company that utilized automated machine learning for a business use case. It increased its productivity by over 10 times by saving — and then reinvesting — model training and testing resource hours into value-adding labor. Automation enabled employees to divert their attention to complex and high-priority tasks.

Whether firms need automated machine learning for marketing automation or sales forecasting, evidence indicates that utilizing an autoML platform to streamline development is a wise strategy for businesses of all industries and sizes.

Examples of AutoML platforms

Companies searching for a machine learning solution should consider utilizing an automated machine learning platform to streamline their time to completion and minimize expenses. Several automated machine-learning companies exist.

Google Cloud AutoML

Google’s platform is a common choice for decision-makers who want to leverage autoML for business. This suite of ML products helps them build, deploy, and scale their AI — whether they need a tabular, image, video, or text model.

H2O.ai

H2O.ai is a leading platform provider pioneering AI democratization. Its user-friendly autoML tool is purpose-built for non-experts. This hands-on automated machine learning tool is ideal for users with minimal data science knowledge but must produce complex ML models like deep learning algorithms or neural networks.

Auto-PyTorch

Auto-PyTorch is automated machine learning software made to optimize training parameters and network architecture. It is primarily useful for tabular or time series data, not image or video — somewhat limiting its use cases.

Azure AutoML

Microsoft Azure’s automated machine learning platform provides a no-code solution for non-experts who want to develop a custom-built ML model. This suite of tools offers extensive functionality for midsize businesses.

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Auto-SKLearn

Auto-SKLearn is an automated machine-learning Python solution. This open-source software library provides out-of-the-box tools to automate supervised ML model development. Since it is free to download and use, it is ideal for entrepreneurs and early-stage startups that lack adequate resources.

Amazon AutoML

Amazon SageMaker, the company’s fully managed ML service, is its version of automated machine learning on AWS. This suite of development tools enables businesses to produce custom models on text, image, or video datasets.

Considerations of machine learning automation

While real-world automated machine learning examples indicate leveraging autoML is generally advantageous, it does have limitations. After all, even advanced technology cannot fully replace human data scientists with years of expertise.

Model performance largely depends on training — an algorithm’s accuracy is entirely related to the underlying dataset. Errors during preparation may contribute to faulty insight extraction or incorrect asset categorization post-deployment. Notably, some platforms capable of automating preprocessing may make mistakes.

A human-in-the-loop is essential to successful machine learning automation. Business owners should consider assigning an individual to the role of auditing and evaluation to prevent algorithmic errors from causing untraceable discrepancies down the line. Since many complex models suffer from the black box problem, proactively establishing explainability is ideal.

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AI management is one of the most significant automated machine learning challenges. While out-of-the-box tools can simplify post-deployment monitoring, usage, and retraining, they aren’t comprehensive. Of course, changing fundamental characteristics of the underlying dataset to adjust a model is complicated — and likely not something non-experts can accomplish.

Organizations — especially small businesses and startups — should consider the AI consulting avenue. Seeking expertise from machine learning consultants can minimize management issues and streamline ongoing usage.

The future of AutoML

Automated machine learning’s prevalence will continue increasing as the automated machine learning market expands. While it is still a relatively novel solution, the fact that technology giants like Google, Amazon, and Microsoft now center entire platforms around it should indicate its probable trajectory.