The digital transformation is driven primarily by the data. So today, companies are searching for as many opportunities to gain as much value from their data as they can. In reality, in recent years, machine learning (ML) has become a fast-growing force across industries.
ML ‘s effect on driving software and services in 2017 was immense for companies like Microsoft, Google, and Amazon. And the utility of ML continues to develop in companies of all sizes: examples include fraud prevention, customer service chatbots at banks, automated targeting of consumer segments at marketing agencies, and suggestions for e-commerce goods and retailer personalization.
Although ML is a hot subject, there is another popular trend: automated machine learning platform (AutoML).
The AutoML field is evolving so rapidly, according to TDWI, there’s no universally agreed-upon definition. Basically, by adding ML to ML itself, AutoML gives expert tools to automate repetitive tasks. The aim of automating ML, according to Google Research, is to build techniques for computers to automatically solve new ML issues, without the need for human ML experts to intercede on each new question. This capability will lead to genuinely smart systems.
Furthermore, possibilities are generated thanks to AutoML. These types of technologies, after all, require professional researchers, data scientists and engineers, and worldwide, but such positions are in short supply. Indeed, those positions are so poorly filled that the “citizen data scientist” has arisen. This complementary position, rather than a direct replacement, hires people who lack specialized advanced data scientist expertise. But, using state-of-the-art diagnostic and predictive software, they can produce models. This capability stems from the emergence of AutoML, which can automate many of the tasks that data scientists once perform.
To counter the scarcity of AI/ML experts, the AutoML example has the potential to automate some of ML’s most routine activities while improving data scientists’ productivity. Tasks that can be automated include selecting data sources, selecting features, and preparing data, which frees marketing and business analysts time to concentrate on essential tasks. For example, data scientists can fine-tune more new algorithms, create more models in less time, and increase the quality and precision of the model.
Organizations have turned toward amplifying the predictive capacity, according to the Harvard Business Review. They’ve combined broad data with complex automated ML to do so. AutoML is marketed as providing opportunities to democratize ML by enabling companies with minimal experience in data science to build analytical pipelines able to solve complex business problems.
To illustrate how this works, a current ML pipeline consists of preprocessing, extraction of features, selection of features, engineering of features, selection of algorithms, and tuning of hyper-parameters. But because of the considerable expertise and the time it takes to enforce these measures, there is a high barrier to entry.
One of the advantages of AutoML is that it removes some of these constraints by substantially reducing the time it takes to usually execute an ML process under human control, while also increasing the model’s accuracy as opposed to those trained and deployed by humans. Through enacting this, it encourages companies to join ML and free up ML data practitioners and engineers’ resources, allowing them to concentrate on more difficult and challenging challenges.
About 40 percent of data science activities should be automated by 2020, according to Gartner. This automation would result in a broader use by citizen data scientists of data and analytics and improved productivity of skilled data scientists. AutoML tools for this user group typically provide an easy-to-use point-and-click interface for loading ML models for data building. Most Automl tools concentrate on model building rather than automating a whole, particular business feature, such as marketing analytics or customer analytics. However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data.
However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data. It proves to be a problem for people who are data scientists, who have to keep up with large amounts of streaming data and recognize trends that are not apparent. They are still not able to evaluate the streaming data in real-time. And poor business decisions and faulty analytics can arise when the data is not analyzed correctly.
Some businesses have switched to AutoML to automate internal processes, especially building ML models. You may know some of them-Facebook and Google in particular. And Facebook is widely on top of every month’s ML, training, and testing around 300,000 ML models, essentially building an ML assembly line to handle so many models. Asimo is the name of Facebook’s AutoML developer, which produces enhanced versions of existing models automatically. Google also enters the ranks by introducing AutoML techniques to automate the process of discovering optimization models and automating machine learning algorithm design.
In certain instances, it is possible to automate entire business processes once the ML models are developed, and a business problem is identified. It needs the data pre-processing and proper function engineering. Zylotech, DataRobot, and Zest Finance are companies that primarily use AutoML for the entire automation of different business processes.
Zylotech was developed for the entire customer analytics automation process. The platform features a range of automated ML models with an embedded analytics engine (EAE), automating customer analytics entering the ML process such as convergence, feature development, pattern discovery, data preparation, and model selection. Zylotech allows data scientists and citizen data scientists to access full data almost in real-time, allowing for personalized consumer experiences.
DataRobot was developed for predictive analytics automation as a whole. The platform automates the entire lifecycle of modeling, which includes transformations, ingestion of data, and selection of algorithms. The software can be modified, and it can be tailored for particular deployments such as high-volume predictions, and a large number of different models can be created. DataRobot allows citizen data scientists and data scientists to apply predictive analytics algorithms easily and develop models fast.
ZestFinance was primarily developed for the complete automation of different underwriting activities. The platform automates model preparation, deployment, and assimilation of data and enforcement explanations. It uses ML to evaluate conventional and non-traditional credit data to rate potential lenders who do not have any files next to them. AutoML is used to provide borrowers with the tools to train and deploy ML models for different use cases such as marketing and prevention of fraud. It also lets financial analysts and investors make better risk judgments and make better decisions on lending.
The new oil might be info, but even crude oil needs to be “cracked” before it becomes usable molecules. Likewise, for embedded molecules, consumer data must be refined before conclusions can be derived from it. Consequently, data is not immediately essential but is useful after it is processed, cleaned, refined, and made ready for study.
The AutoML approach helps companies effectively use ML, as future market insights are concealed where only ML can hit. No matter what industry you’re in, AutoML is the technique required to extract this valuable resource and exploit it.
And as companies are increasingly reliant on civilian data scientists, Gigabit Magazine predicts that 2020 is likely to be the year that AutoML will move into the mainstream of enterprise adoption. The strategies and tools of AI and ML will become more ingrained in the everyday operations and thinking of companies as they become more motivated to recognize projects whose expertise will drive better decision making and innovation.
Businesses everywhere are searching for ways to gain as much information as possible from their data as the fuel that powers their ongoing digital transformation efforts. Also, the resulting increased demand for advanced predictive and prescriptive analytics has led to a call for more skilled data scientists with the latest tools for artificial intelligence (AI ) and machine learning (ML).
But these highly trained data scientists are expensive and in short supply. In reality, they ‘re such a valuable resource that the “citizen data scientist” movement has recently emerged to help close the skill gap.
Citizen data scientists lack specialized advanced data science skills in terms of a complementary role rather than a direct substitute. They are, therefore, able to produce models through state-of-the-art diagnostic and predictive analytics. And this flexibility is partially due to the introduction of open new technology such as “automated machine learning ” (AutoML), which now automates many of the tasks that data scientists once performed.
According to a recent article in Harvard Business Review, “Organizations have moved toward that predictive capacity by combining big data with complex automated machine learning. AutoML, which uses machine learning to produce better machine learning, is marketed as “democratizing machine learning” opportunities by allowing businesses with limited data science knowledge to develop analytical pipelines capable of solving complex business problems.
With a set of algorithms that automatically write other ML algorithms, AutoML automates the end-to-end phase of applying ML to real-world issues. By way of example, a typical ML pipeline consists of: pre-processing of data, extraction of features, selection of features, selection of features, selection of algorithms, and tuning of hyper-parameters. But the substantial experience and time it takes for these measures to be made means the entry barrier is secure.
AutoML does away with some of these restrictions. It not only dramatically decreases the time it would usually take to develop an ML process under human oversight, but it can also increase the model’s accuracy compared to human-made models, trained and implemented by humans. In doing so, it provides companies with a path into ML, as well as freeing up ML engineers and data practitioners’ resources, enabling them to concentrate on higher-order issues.
The movement towards merging ML and Big Data for advanced data analytics started back in 2012 when “deep learning” became the dominant approach to solving ML problems. This approach heralded the development of a wealth of new tools, tooling, and techniques that, on a large scale, changed both the workload and workflow associated with ML. Entirely new ML toolsets have been developed, such as TensorFlow and PyTorch, and people have increasingly started to engage more with graphics processing units ( GPUs) to accelerate their work.
Until this point, the efforts of companies had been impeded by the problems of scalability associated with running ML algorithms on large datasets. Yet now, they have been able to solve these issues. By developing rapidly sophisticated internal technologies capable of creating world-class AI applications, the BigTech powerhouses quickly exceeded their Fortune 500 peers when it came to an understanding of the advantages of smarter data-driven decision-making and software.
AutoML represents the next step in the evolution of ML, promising to help non-tech businesses access the skills they need to develop ML applications quickly and cheaply.
For example, in 2018, Google launched its Cloud AutoML. Based on Neural Architecture Search (NAS) and transfer learning, Google executives described it as having the potential to “make AI experts even more competitive, advance new AI fields, and help less-skilled engineers develop powerful AI systems they had only dreamed of before.”
The one drawback of AutoML on Google is that it’s a proprietary algorithm. However, there is a range of alternative open-source AutoML libraries, such as AutoKeras, created by Texas University researchers and used for driving the NAS algorithm.
Technological breakthroughs like these have provided companies the opportunity to create production-ready models quickly without the need for costly human capital. By leveraging AI, ML, and deep learning capabilities, AutoML offers organizations across all industries the ability to benefit from data-driven applications guided by mathematical models-even. However, specialized expertise in data science is scarce.
With companies relying increasingly on civilian data scientists, 2020 is likely to be the year that AutoML ‘s enterprise acceptance will begin to become mainstream. This easy access would push business leaders to eventually open ML’s “black box,” thereby increasing their awareness of its processes and capabilities. The methods and practices of AI and ML will become deeply embedded in the daily thought and operations of companies as they become more motivated to recognize those projects whose insightful expertise will drive better decision making and innovation.
By 2019, Machine Learning ( ML) had moved from a hyped state to many practical use cases, especially in the B2B room, resulting in increased consumer trust and belief. Today ML has been one of the most sought after innovations across both vertical and horizontal industries. This transition was mainly due to the growing availability of vast collections of open data, host of ready-to-use algorithms, and open-source programs that were freely available. AI (Artificial Intelligence) and ML become an essential part of the digital transformation journey of any organization.
The conventional process of building a machine learning solution follows a resource-intensive iterative process that requires significant domain knowledge and plenty of time to produce and compare dozens of models. The shortage of qualified data scientists and training sample data has led to numerous failed long-term ML projects and a lack of adequate ROI in the ML industry.
Traditional ML automates a business process from a customer. Still, the process of designing such ML models itself requires large-scale automation due to the emergence of connected devices, IoT, Big Data, and other emerging technologies. This automated development of ML models can be accomplished by AutoML (Automated machine learning ), which helps data scientists to generalize and automate some of the most challenging tasks such as feature engineering, hyperparameter tuning, and other ML practices tasks.
AutoML allows developers with a minimal machine learning experience to train business-specific, high-quality models without requiring them to go through the complicated conventional ML workflow.
The technology is expected to create a new class of ‘citizen data scientists’ whoever is familiar with Excel and has some form of connection to the data but not necessarily a data scientist who puts the power of advanced ML directly in the hands of business users. With AutoML ‘s application, developers around the world do not need to design new neural networks for their specific needs again and again, but they can easily leverage existing AutoML products by making only the necessary modifications.
AutoML provides a range of incentives for marketers, allowing for deeper personal customer interaction. If the company needs to empower data scientists/business users or improve efficiency, AutoML solutions are increasingly becoming a must-have for any enterprise that wants to maximize its use of ML.
There has been an increase in the interest of AutoML software over the past few years. The current market for AutoML is small and still in the early stages, but has the potential to expand very quickly. In many cases, AutoML is currently not outperforming hand-constructed models in terms of precision. Yet there is evidence that AutoML can achieve the best performing model ‘s efficiency by as much as 95 percent.
Any business that works on AI / ML will soon have a stand-alone AutoML tool, according to Forrester. Gartner also predicted that by 2020, more than 40 percent of data science activities would be automated.
Several off-the-shelf packages to provide automated machine learning have been built in recent years. Many commercial organizations, including Auto-sklearn, Auto-Weka, Prodigy, H2O.ai’s Driverless AI, etc., have also attempted to automate machine learning. At present, startups in the AutoML market such as DataRobot, dotDATA, Edgeverve, etc. are pushing for increased market awareness of AutoML products and targeting citizen data scientists.
There are also several AutoML solutions available on the cloud, with AWS Sagemaker among them all being the most common. Google Cloud AutoML and Microsoft Azure’s Machine Learning Service are both expected to see plenty of practical use cases in the coming months with the cloud ‘s growth.
There is currently no uniformly optimal AutoML solution, and the Automated Machine Learning (AutoML) frameworks presently available are still far from being able to solve many of the real-world data science issues, where the projects are multifaceted and require complicated and subjective tasks that don’t allow for simple automation. Another big problem facing AutoML is that no single machine learning approach works best on all datasets, so, for now, data scientists need to experiment with different methods. There’s also a lack of clarity on why the model makes a particular decision, how the model is selected, etc.
Nonetheless, AutoML applications will grow as tools automating more of the data science workflow in the next three to five years and can be provided as AutoML-as-a-solution. With the introduction of AutoML, the role of data science will become more and more a function of business science. Businesses have just started experimenting with AutoML and are planning to apply this to some of their non-critical tasks and focus on the AutoML systems’ accuracy output level.
Similar to Cloud Marketplace, in the future, there may be an Automated Machine Learning (AutoML) marketplace where ML users can simply plug and play algorithms and run models based on the data that they have. When the industry moves towards discovering the best solutions to AutoML, practitioners, and data scientists are expected to provide input on the right methods to build a road to a robust AutoML 2.0, where AutoML can be leveraged as an assistant tool that lowers reliance on highly skilled data scientists.