Machine learning (ML) models and AI applications have transitioned from novelty projects which companies experimented with, to fully mature technologies generating significant returns on investment. Now, all the top leading global companies are using AI and ML in their daily operations to gain a competitive advantage.
The Data and AI Leadership Executive Survey 2022 from New Vantage revealed that 92.1% of companies are witnessing measurable business benefits from AI and ML. American Express, Bank of America, Citigroup, Deutsche Bank, Mastercard, Visa, JP Morgan, Pfizer, the U.S. Department of Defense, Facebook, and more than 80 other blue-chip companies and leading firms revealed how AI impacts their bottom line.
With 97% of companies from that survey stepping up investments in data and AI initiatives, roles like Chief Data and Analytics Officer (CDAO) are becoming more vital. In 2012, only 12% of companies accepted CDAO as an established position. Today, more than 70% of organizations have recognized its importance.
While leading companies excelling in the market have significant resources to build ML models, most data science teams work hard to get buy-in from executives. They are also pressured to deliver results while navigating tight budgets and fighting the clock.
How can data teams improve short-time performance with existing resources in a cost-effective way?
Automated tools that accelerate the final data quality stages and simplify feature discovery, engineering, and MLOps are the way to go. Let’s dive into these new technologies and how they are changing the landscape.
New Tools To Achieve ML-Ready Data Standards
The disruptive impact of new ML tools is unquestionable. Data teams can deploy automated ML data prep and feature discovery tools in the early stages of model building. Preparing data for ML algorithms is a time-consuming process. But, with automated technology, companies can achieve the high standards that AI requires in just hours.
Additionally, ML-ready data quality needs to go beyond the quality of business intelligence dashboards. New tools and technology solutions provide a variety of data prep and cleansing automation. Techniques like automated string value canonicalization, record duplication r******, missing value imputation, and outlier elimination can quickly prepare data for ML model development.
On the other hand, production-ready pipelines control and automate the workflow processes needed to build an ML model. These help data teams quickly integrate pipelines into business systems for rapid deployment.
Model Drifting and the Need for Upskilling
The 2022 Arize Survey reported that 84.3% of data scientists and ML engineers recognize that the lengthy process to detect and diagnose problems with ML models is an issue. Model drifting—when algorithms produce inaccurate predictive analysis results due to changes in real-world data that differ from data used to build the model (for example, market changes during the pandemic)—are a top priority for ML in our volatile and ever-changing socio-political and economic world.
So, how do automated ML drift tools rectify this problem?
The answer to this question leads us into the BIG area known as MLOps: The practices, people, and processes used to deploy and maintain ML models. In simple terms, MLOps tools monitor the ML model.
Depending on how advanced the MLOps tool is, it may provide alerts when specific conditions arise. Therefore, if a model begins to drift, the tool can flag and warn data teams. While the MLOps tools themselves are not responsible for correcting drift, with the right system—and the right ML pipeline—you could retrain your data and update your model.
According to the Arize Survey, more than half of data scientists and engineers are looking for better capabilities to monitor the operations and root causes of ML models that drift. Another AIM survey revealed that more than three in four (77.8%) still use conventional ML models. Being out of touch with ML’s new technologies, tools, and resources represents a severe risk for any data team.
Upskilling has become a must.
Can Synthetic Data Fill In the Gaps?
It’s important to understand that new tools for ML are not designed as shortcuts, nor are they built to replace data experts. Therefore, as part of the wave of ML solution development, several trends have been rising, and these must be considered cautiously.
Gartner estimates that by 2024, 60% of all data used to develop AI and analytics will be synthetically generated. The market for synthetic data—generated artificially as an alternative to real-world data—is blooming with new companies offering solutions.
It is hard to deny the appeal that synthetic data has for ML developers. It solves the “not enough data” issue, does not require consent, has no privacy risks, is abundant, c****, and can be formatted to any requirement.
However, the risks are often underestimated.
Synthetic data can also be biased, may not represent real-world events as it is artificial, and can lack the data quality required by ML standards. When choosing to work with synthetic data, whether to accelerate the database development process, bypass the need for user consent, or solve the “not-enough-information” problem, it’s critical to be vigilant about the quality and diversity of synthetic databases.
No-Code and Black Box AI for Automation Technologies
Another trend gaining momentum is no-code AI.
From the perspectives of executives and decision-makers, it may seem like the ultimate solution. No-code AI is appealing for many reasons. It can reduce the time and costs of producing and deploying AI apps and removes the need for highly technical skills.
However, working with no-code AI and black-box AI applications also presents many risks.
If AI users can only visualize the data they input and the results generated by the model but not the internal operations, they are missing out on critical aspects — how the algorithm works and reaches its predictive analysis results. Therefore, there is no way to check, monitor, or improve results generated by a black box AI because its inner operations are “hidden” or hard to interpret.
Black box and no-code AI are often criticized for this lack of transparency. While new technologies can present shortcuts for data teams, some points in ML must always be measurable, including ethics, transparency, efficiency, and performance.
Automated Feature Discovery, Engineering, and ML Testing
Data experts now have at their disposal automated feature discovery and engineering tools and the ability to automatically test a wide range of ML hypotheses in significantly shorter times than is possible through manual feature discovery.
But how do automated feature discovery and engineering work? At a high level, automated feature discovery is the process of analyzing connected tables of data to find statistically meaningful patterns or “features” that are then evaluated for value for any given ML model.
Using these tools, the user first loads the tables he wants to use for analysis and establishes “Entity Relationships”— or how the tables are connected. Then, the user provides a “Target Variable”—something describing what they are trying to predict. For example, if data scientists are trying to predict churn, they need a column somewhere in the data set that describes whether a client has churned or not.
Using the target variables, the system then discovers all the possible combinations of tables and columns (known in Structured Query Language (SQL) terminology as joins) to build individual features that predict (in this example) churn.
Some simple “brute force” tools do this, but they stop at discovering the (potentially) millions of combinations, leaving it up to the user to determine which features are valuable (a potentially impossible task with millions of features). More advanced tools can evaluate and s**** the features, exposing the ones most likely to impact the target variable.
The New ML Way
Whether working in large, medium or small companies, data teams today have access to the tools to upskill and keep updated on how ML is disrupting the industry. New tools can help them accelerate time to market and deployment while fast-tracking efficiencies and outcomes throughout the entire ML process.
Automation in ML removes the burden of monotonous, time-consuming tasks while augmenting performance. Data experts used to spend most of their time cleaning and finding the correct data. Today, they can focus on what they do best: building, developing, testing, deploying, and maintaining ML models and AI. Automation and new technologies that integrate into holistic pipelines and enhance specific processes are the way forward.
