“Machine intelligence is the last invention that humanity will ever need to make.”
– Nick Bostrom
Machine learning has experienced exponential growth, fueled by factors such as the availability of vast amounts of data, advancements in computing power, the rise of big data technologies, the adoption of open-source frameworks, industry-wide implementation, advancements in deep learning, integration with other emerging technologies, and contributions from the research community.
The Global Machine Learning as a Service Market Size in 2022 stood at USD 7.1 Billion and is set to reach USD 173.5 Billion by 2032, growing at a CAGR of 37.9% – GlobeNewswire
This growth has led to a wide range of applications across industries, including healthcare, finance, retail, manufacturing, and cybersecurity. Machine learning’s ability to extract insights, automate processes, and make predictions has revolutionized decision-making and driven innovation. As machine learning continues to evolve and mature, it holds the potential to transform various aspects of our lives, making it one of the most dynamic and promising fields in the realm of artificial intelligence.
Did you know?
20% of C-level executives (across 10 countries and 14 different industries) report that they are using machine learning as a core part of their business. – (Mckinsey)
However, implementing machine learning algorithms often requires specialized knowledge and expertise, making it inaccessible to many individuals and organizations.
Here enters Automated Machine Learning (AutoML) as a solution, an innovative technology that streamlines the development and deployment of machine learning models, allowing individuals without specialized expertise to leverage the capabilities of artificial intelligence.
ML vs AutoML – what’s the difference?
The methods used by ML (Machine Learning) and AutoML (Automated Machine Learning) to create machine learning models are different. Data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation are manual operations that data scientists must conduct in ML and for which they must have specialized knowledge.
On the other hand, AutoML automates these activities, streamlining and allowing non-experts able to use the model creation process. Users supply the data and define the problem, while autoML platforms handle activities like data preparation, feature engineering, algorithm selection, hyperparameter optimization, and model evaluation automatically.
Though it may provide less customization compared to manual ML approaches, autoML promises to streamline the ML process and save time and effort.
“AutoML is making machine learning more accessible to everyone.” – Fei-Fei Li, Stanford University
Let’s dive into the deep story of AutoML
AutoML, the concept of automating machine learning processes, emerged in the 1990s and gained significant traction in the early 2010s due to various factors.
These factors included the increasing availability of large datasets, advancements in powerful machine learning algorithms, and the rise of cloud computing, facilitating easier execution of machine learning models.
Quoc Le, a prominent researcher at Google AI, played a crucial role in AutoML’s development. In 2011, Le introduced Neural Architecture Search (NAS), a groundbreaking technique that enables computers to autonomously search for optimal architectures for machine learning models. NAS acted as a catalyst for the advancement of sophisticated AutoML systems.
Building upon this progress, Google launched AutoML in 2016, a commercial platform empowering users to construct machine learning models without coding. This release marked a significant milestone, popularizing AutoML and bringing it to the forefront of technological innovation.
Since then, the AutoML landscape has witnessed considerable activity. Numerous companies have introduced their own AutoML platforms, and researchers have delved into novel AutoML techniques.
Although AutoML remains a relatively nascent field, its potential to transform the utilization of machine learning is undeniable. By automating the machine learning process, AutoML democratizes the ability to create machine learning models, making it accessible to individuals regardless of their expertise. This democratization has the potential to spur innovation across a wide range of industries.
Here we listed some of the key milestones in the history of AutoML:
- 1990s: The inception of automated machine learning is proposed.
- 2011: Quoc Le pioneers Neural Architecture Search (NAS).
- 2016: Google introduces AutoML commercially.
- 2017: Other companies commence releasing their AutoML platforms.
- 2018: Research on novel AutoML techniques intensifies.
- 2020: Widespread adoption of AutoML by businesses and organizations.
How does the AutoML algorithm work?
Source: Click here
Here is a more detailed explanation of how AutoML algorithms work:
Data preparation: AutoML algorithms typically use a variety of techniques to clean and prepare the data for training the model. These techniques can include:
- Removing noise from the data
- Imputing missing values
- Transforming the data into a format that the model can understand
- Feature engineering: AutoML algorithms can also automatically create new features from the existing data. This can help to improve the performance of the model by capturing more information about the problem. For example, an AutoML algorithm might create a new feature that is the combination of two existing features.
Model selection: AutoML algorithms typically use a variety of machine learning algorithms to train the model. These algorithms can include:
- Linear regression
- Logistic regression
- Decision trees
- Neural networks
Hyperparameter tuning: AutoML algorithms can also automatically tune the hyperparameters of the model. Hyperparameters are the parameters that control the behavior of the model, and they can have a significant impact on the performance of the model. For example, a hyperparameter for a neural network might be the number of layers in the network.
Model evaluation: AutoML algorithms typically evaluate the performance of the model on a holdout dataset. This dataset is not used to train the model, it is used to get an unbiased estimate of the model’s performance. The AutoML algorithm will then select the model that has the best performance on the holdout dataset.
Where can we use AutoML?
Here are some specific sectors where AutoML is now in use:
Health Care: AutoML is being used to create models for disease diagnosis, patient outcomes prediction, and treatment recommendation.
Finance: Fraud detection, consumer behavior prediction, and risk management are all made possible by autoML.
Did you know?
Paypal employed H2O.ai’s AutoML tool to improve its fraud detection model. The accuracy of their model improved from 89% to 94.7% and models were created 6 times faster. (H2O) – AIMultiple
Retail: Product recommendations, personalized pricing, and inventory optimization are all made possible by AI.
Production: AutoML is being used to enhance product quality, streamline workflows, and reduce waste.
FYI: DataRobot Inc., H2O.ai Inc, dotData Inc, EdgeVerve Systems Limited, and Amazon Web Services Inc. are the top five companies in the AutoMl market.
Final Thoughts
“AutoML is the next big thing in machine learning.” – Gartner
Overall, AutoML has become a potent tool in machine learning, altering the way models are created and opening up the process to a larger audience. In recent years, AutoML has been increasingly popular because of the expansion of the number of accessible huge datasets, improvements in algorithms, and the emergence of cloud computing.
Interested in adapting ML to your business? Looking to harness the full potential of AutoML for your organizational growth? Agira Technologies may be the right tech partner for integrating ML magic. Want to learn how? Just text us “hi”.
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