AiThority Interview with Alex Fly, Founder and CEO at Quickpath
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Tell us about your journey through technology. What made you found Quickpath?
I’ve been working in AI for the past 20 years, and previous to starting Quickpath, my co-founder, Trent McDaniel, and I worked in a consulting capacity. We realized that many businesses faced similar challenges when implementing Machine Learning projects, so we wanted to create software that could facilitate this process without requiring custom solutions each time. Companies are frustrated by the complexity of using Machine Learning to make business decisions. We wanted to help companies get models out of the lab and into production, so they could actually realize value using the technology. Therefore, we founded Quickpath, which enables businesses to make automated, intelligent decisions using Machine Learning and Artificial Intelligence.
What are the challenges that Quickpath faces in integrating Artificial Intelligence into business applications?
While it’s now easier to build quality Machine Learning (ML) models with popular open source frameworks, AutoML, and citizen data science tools, enterprises continue to struggle with the difficulty of integrating and managing ML to drive real business value. Industry-wide, only 13% of models built are ever used in production decisions (i.e., analytic shelfware). Production implementations average 6-9 months, cost several $100Ks, and the resulting analytic and technical debt taxes future productivity. Left unchanged, the majority of ML initiatives will fail to deliver value.
The reasons for these challenges from existing approaches span the four pillars of organization success:
- People: Lack of critical skilled resources in key data science and data engineering roles within IT. Lack of understanding of what’s possible and not possible by C-level stakeholders.
- Process: Lack of defined, repeatable process for building and deploying AI applications results in heavy analytic and technical debt.
- Technology: Rapidly changing landscape of tools and ML frameworks is hard for enterprises to keep up with and to select the right ones.
- Information: While there is lots of data in enterprise organizations, it’s still siloed and not well cataloged for repeatable use by a data scientist for model development.
How does QuickPath approach a production data science platform?
Quickpath makes it simple for citizen integrators to build, deploy, and manage production ML enabled apps. Our ever-growing connectors provide the data and ML fabric for companies to tie together first and third party data sources, Machine Learning frameworks, graph databases, and SaaS applications for intelligent decision automation. The platform’s low code design studio and open architecture enable the self-service creation of intelligent decision APIs data, models, business rules, and feedback loops. Our unique approach reduces cost and implementation time up to 90% while ensuring confidence and transparency in ML decision-making with model management and patent-pending drift, anomaly, and bias detection.
How can your platform benefit modern enterprise applications?
Quickpath bridges the gap between the environment and the data science team that is used to build models and the deployment environment and standards corporate IT requirements. This approach provides a repeatable and consistent path to production for data and analytics-driven decision-making. It results in more models being deployed more quickly and generating more business value from enterprise data science practices. Ultimately, these capabilities allow organizations to transform from a static, batch, and rule-driven decision-making to real-time, scalable, and highly automated Machine Learning-driven decision-making.
How does Machine Learning bring speed and agility to enterprise AI Initiatives?
Stephen Hawking defined intelligence as “the ability to adapt to change,” which I think really well sums up ML and AI value to the enterprise when compared to the manual or rule-based processing that it aims to improve. Manual and rule-based approaches to customer interactions and process automation have failed to scale with the ever-growing increase in digital, mobile, and IoT signals and wealth of customer data that companies now possess. Machine Learning excels at taking all of these rich signals, detecting patterns within them, and providing algorithms to optimize outcomes to the desired target that would have been impossible using previously available techniques.
Which industries can benefit from leveraging Quickpath’s platform?
We work with numerous industries including, marketing, sales, banking, insurance, and retail. That said, we’ve seen the most activity in two major industries. First, we’ve been able to deliver the significant value within organizations seeking to deliver more relevant customer experiences using the growing number of rich, behavioral information sources that marketing and digital transformation initiatives look to enable. Secondly, heavily regulated industries like financial services and healthcare can benefit from the robust model management and explainability capabilities we provide. In either case, our customers can simplify their intelligent decision automation initiatives while providing stakeholders, auditors, and regulators the visibility and explainability they need to understand and trust how and why decisions are being made.
How have businesses benefitted from Quickpath’s platform?
We’ve led the enablement of a data science factory model at one of our Fortune 500 Financial Services customers where we created a highly repeatable and easily managed path to production for ML enabled applications. We were able to increase their analytic throughput from ideation to production by 16X, resulting in nearly 100 real-time ML enabled decisions a year for a relatively small team.
Which events and webinars would you suggest to our readers as being the best in grasping information on emerging technologies?
There are a wealth of resources available, ranging from AI industry conferences from leaders like O’Reilly, TDWI, Gartner, and Forrester to smaller, local meetups on varying topics like AI research, applied AI and ML, data and ML engineering, algorithms, etc.
Many of the leading cloud and model development platform providers also have great blogs, YouTube channels, and free webinars. Another great resource is the data and engineering blogs that high tech leaders like Uber, Airbnb, Netflix, Intuit, and others have made public recently.
What are your views on the weaponization of AI?
Data collection and AI are a growing force in every aspect of global life in the 21st century. Entrepreneurs, educators, citizens, and legislators should all keep a keen eye on both governments and corporations and their AI ambitions. Where the government, NASA, and military drove innovation for decades with advancements like plastics, GPS, and computing, we’re seeing massive innovation from private industry in things like Computing Processing Power, AI, Cloud Computing (JEDI), encryption and cryptocurrency, and Drone Technology. And much of that innovation, or manufacturing, is happening outside of the US. It’s also a totally different playing field regarding ethics and privacy, meaning there are actually less regulatory restrictions on innovation for China than the US and Europe. We’ve already seen large scale rollouts of facial recognition and social behavior scores in China, while the US government and some leading tech companies are struggling with finding ways to work together and co-exist in the current political landscape.
Where do you see AI/ML in 2025?
I’m an optimist on this front. I think there will be a huge growth in high paying job fields. I also think that like each previous wave of the industrial revolution, more net new jobs will be created than those lost by automation. Although Quickpath is focused on helping companies adopt and scale applied ML and AI across their organizations, I also track advancements in research applications, where I’m hoping we start to see advancements in medical treatments, cost reductions in those treatments, and making quality healthcare more accessible in developing countries.
What start-ups are you keenly following?
We’re very closely aligned in approach and philosophy with what the AutoML providers like H20.ai, DataRobot, Google have set as goals. Namely, making products that make it easier for citizen data scientist and, in Quickpath’s case, citizen integrators to build, integrate, deploy, and manage ML-enabled applications with a fraction of the time and complexity that it takes to do it today.
Which specific spheres in AI are you particularly interested in?
Our primary focus is applied AI, and helping companies adopt and scale the usage of ML and AI to optimize and automate their critical customer interactions and business processes. We continue to try to reduce the inherent complexities of ML and AI using best practices that we’ve developed over decades of building real-time and online Machine Learning decision-making engines.
Tag the one person in the industry whose answers to these questions you would love to read.
Mike Gualtieri, VP & Principal Analyst, Forrester Research
Thank you, Alex! That was fun and hope to see you back on AiThority soon.
Alex Fly is the CEO of Quickpath, a data science platform that bridges the gap between data science analytics environment and the IT operational environment. He is a seasoned AI/ML specialist, focused on enabling businesses to make automated, intelligent decisions throughout their organizations using Machine Learning and artificial intelligence.
Quickpath makes it simple for citizen integrators to build, deploy, & manage production Machine Learning (ML) enabled applications and removes the integration and operations issues that cause the majority of ML initiatives to fail. The low code design studio enables the self-service creation of intelligent decision APIs comprised of data, a wide variety of ML algorithms, business rules, and feedback loops. Our unique approach reduces cost and implementation time up to 90% while ensuring confidence and transparency in ML decisioning with Model Management and patent-pending drift, anomaly, and bias detection. As a result, companies can radically increase critical resource productivity while improving the overall transparency and performance of their key business decisions.