At a time when businesses are looking at adopting Artificial Intelligence (AI) not just for competitive advantage but even for mere survival, it is increasingly challenging to build a successful AI practice with acute skills shortage for data scientists. On the other hand, Machine Learning (ML), which is built for its application involving laborious tasks such as cleaning data, preparing data and training ML algorithms, validation etc. However, there is continuous effort to automate these tasks by built more intelligent ML procedures and algorithms. AutoML , as we call it, can democratize ML by allowing even business users to develop and execute their own data models with little to no training on data science. Other than bridging the skills gap, automation in ML processes can also eliminate data biases, a major concern today, and reduce human errors while improving overall efficiency. Moreover, AutoML would allow domain experts and technical experts like data scientists, ensuring continued focus on business value.
The need for AutoML – Challenges with traditional ML processes
The growing interest in AI and ML means that there is a crippling shortage of data scientists. There were over 2.7 million open positions for data science and analytics jobs, according to a report by the Business-Higher Education Forum.As per the US Bureau of Labor Statistics, the number of jobs in the data science field will grow by 26 percent through 2026, adding nearly 11.5 million new jobs.
However, demand vastly outpaces supply for data scientists given how challenging it had been for several decades to work in this domain. It is impossible to generate hundreds of thousands of new data scientists in an instant, making it tough for organizations to implement their data science plans.Lack of these skillsets is one of the biggest reasons holding back thousands of companies from starting their AI journey. That said, automation is rapidly trying to solve this problem by making data science more accessible to even those without years of data science experience or even a degree in the subject.
Even so, lack of required skills is not the only challenge that organizations looking at machine learning face today. Even if an organization has the right skills, it may still be highly under-utilized because of the sheer amount of time that it takes just to clean the data. Data scientists spend as much as two-thirds of their time just cleaning the data. Just imagine if this is automated, what kind of fillip it will provide to the domain.
Further, data scientists often don’t come with domain and business expertise. However, even if bring domain and business understanding they end up focusing most of their time ingesting and processing data in order to make the models relevant. As a result specific business context often go amiss, leading to unsuccessful adoption of AI/ML.
Traditional ML processes are also highly dependent on human expertise, given the amount of customization that each ML model requires for the specific problem on hand. This makes the entire process inherently time-consuming. To build a new ML model, you still have to through the rigours of data preparation, feature engineering, training the model, evaluation and selection.
Biases in AI and ML models are also a major subject of debate today. Biases often creep in because of manual interventions and the inability of humans to analyze massive data sets for possible biases. The complexity of ML models currently has turned them into black boxes with very little visibility into what goes inside and what is impacting the final results.It is therefore vital to automate the process of machine learning to get better visibility into the models, eliminate all biases, and improve the overall efficiencies.
What is AutoML?
While machine learning continues to evolve, Automated Machine Learning (AutoML) goes beyond automation to accelerate the process of building ML and deep learning models. It automates several aspects of the ML processes, including the identification of the best performing algorithm from the available universe of features, algorithms and hyperparameters.
How Does AutoML Help?
By eliminating repetitive tasks, such as data cleaning, AutoML frees up the highly valued human resources to move towards value-adding analysis and more in-depth evaluation of the best-performing models. This allows enterprises to significantly cut down the time-to-market for the products and solutions built on these ML models.It:
- Eliminates repetitive tasks
- Allows enterprises to bring down time-to-market
- Guided analytics capabilities allow to eradicates biases
- Enables organizations to leverage their existing components
- Inspires trust by providing transparency on how the model functions
- Eliminates human error
However, complete automation also has its own set of challenges. Tesla founder Elon Musk famously said “AI is far more dangerous than nukes.” Apart from Musk, technology leaders like Bill Gates and Steve Wozniak have expressed concern about the dangerous aspect of AI. For instance, anyone with malicious intent can program AI systems to carry out mass destruction. Any powerful technology can be misused and AI is no different. The truth is that as long as AI systems continue to be Black Boxes, it will continue to remain a threat.
Some new age solutions are changing that equation by bringing in transparency and making it easier for users to interact better with AI systems. HyperSense AI Studio , for example, is built with guided analytics capabilities, which is a combination of automated ML and interactive ML. This allows usersto develop applications with a combination of automation and human interaction at any stage of the data science cycle based on task and business user requirements. The solution also generates alerts and gives recommendations to users as they are creating a pipeline.
The process eliminates biases that might have crept in and ensures that the system is not seen as a Black Box by providing details of how it functions and arrives at the results.
Through AutoML, the user can easily automate tasks like data pre-processing, feature engineering and hyper-parameter tuning. Moreover, it allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.
Several Machine Learning processes do not require any human intervention, allowing domain experts to work on building AI models instead of depending solely on the data scientists.
Data scientists, however, do not have to be a rare commodity anymore. Just how the power of a mobile phone camera made citizen journalism possible, the power of AutoML is now creating citizen data scientists . This new breed of professionals will now be able to build their own AI models without any formal education in Machine Learning or AI. Anyone familiar with the usage of Excel and interest in data analysis can potentially become a citizen data scientist.
The role of citizen data scientists will be critical in the growth of AI. In order to scale AI, one needs a massive number of data scientists. Moreover, citizen data scientists don’t just fill the skills gap. The biggest mismatch in ML initiatives is that ML projects are often associated with a lack of domain expertise. Data scientists are great at working on data, but they don’t necessarily come with a good understanding of your business or industry. Connecting the roles of domain expertise and data expertise has been a massive challenge for several firms.
However, by putting the ability to build a data model into the hands of a business user, AI projects can move towards newer dimensions that can only be perceived by a business domain expert.
What are the benefits of AutoML?
Other than democratizing machine learning, AutoML also has several other advantages. Automating the machine learning processes, for example, can tremendously accelerate the speed of training multiple models while also improving accuracy. In addition, AutoML eliminates biases in datasets by limiting human intervention and automating most of the processes in the ML pipeline. The reduced human intervention also cuts down on human errors in the process.
Automation also makes ML more scalable by enabling multiple ML models to be trained simultaneously, and in doing so, it also optimizes the overall ML processes to a great extent.
HyperSense AI Studio is an excellent example of AutoML platform . The platform enables enterprises to build and operationalize AI successfully using automated machine learning. It increases the efficiency of data scientists allowing them to focus on higher-value tasks. It automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.
By leveraging HyperSense AI Studio , data scientists and domain experts can easily build ML models with higher scale, productivity, and efficiency while sustaining the model quality. By automating large part of the ML processes, the platform accelerates the time to get production-ready models with greater ease and efficiency. It also reduces human errors mainly because of manual measures in ML models.
It also makes data science accessible to all, enabling both trained and non-trained resources to rapidly build accurate and robust models, thus fostering a decentralized process. Further, it enhances collaboration between domain and technical experts which encourages the focus to remain on business value and not on technical part of the implementation. This helps in bringing down silos and promotes collaboration in other areas as well.
The quality of the machine learning model is not only based on code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data. HyperSense AI Studio comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline. It allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.
AI projects for long have been stuck at pilot stages due to several challenges that include lack of data scientists, slow progress in ML processes and even lack of coordination between business and data teams.According to a Gartner study, about 75 percent of organizations will shift from piloting to operationalizing AI by the end of 2024. Also, 50 percent of enterprises will devise AI orchestration platforms to operationalize AI. This, however, wouldn’t be possible without leveraging AutoML .
AutoML has the potential of democratizing AI and Machine Learning and finally take AI projects from mere pilots to scaled deployments. AutoML platforms like HyperSense AI Studio increases the efficiency of data scientists by allowing them to focus on higher-value tasks. The platform automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning, ensuring enhanced operational efficiency. In addition, it comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline and even allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.
Get better results from your data with HyperSense AutoML
Tharika Tellicherry is an Associate Marketing Manager at Subex. She has extensive experience in Product Marketing, Content Creation, PR, and Corporate Communications. She is an avid blogger and enjoys writing about technology, SaaS products, movies, and digital customer experience.