Machine Learning Applications Key to Run Business-Critical Systems, Says Davor Bonaci, CEO at Kaskada
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Hi Davor, please tell us about your journey in technology and how you started at Kaskada.
I feel there were a great number of decisions and events that enabled me to be here. More directly related to Kaskada, I joined Google in the early days of building Google Cloud and was part of the team building Cloud Dataflow, a distributed data processing system for streaming and batch data. We ended up open sourcing portions of this product as Apache Beam, and I then spent several years leading and building out the Beam community.
During this journey, I realized that many companies struggled to get to success with big data projects and that lower-level data processing systems are very difficult to use outside of massive tech companies. This was the inspiration to start a company where we could make these big data technologies more accessible across industries and to companies who don’t have the luxury of having large data engineering organizations. We are currently focusing on solving big data issues as they relate to machine learning systems.
Is it safe to say that Machine Learning applications development has finally outgrown its IT predecessors? Is it now an industry and a service in itself?
Machine learning has obviously experienced massive growth in recent years and is now a core component of many business-critical systems and customer facing applications. This change has introduced tremendous change in a range of industries and especially in the quality of personalization available to end-users. It used to be that when you and I would use an application, or visit a website, or play a game, we would be given the exact same experience.
Machine learning has allowed companies to begin tailoring these experiences to our personal needs and interests. We believe that the future of machine learning applications, software, and devices is personalized and that this will be driven by the large-scale adoption of machine learning in enterprises.
You secured a sizable funding recently. How do you intend to capitalize on this development to build Machine Learning Applications?
Kaskada will use the Series A funding to accelerate company growth, expand our team of software engineers, and fulfill more customer demand. We are delivering our product in the first half of 2020 and are in a growth mode with our team, customers, and partners.
AIOps and Machine Learning Studio– How are these two specializations different from each other?
AIOps is the application of ML/AI technologies to internal IT systems management.
It is a specific use case that is powered by ML/AI. Our machine learning studio comes a step earlier and companies could actually build the data pipelines for ML use cases like AIOps using Kaskada. Our system’s output (machine learning features) could be used as data input to an AIOps service, for instance.
AIOps is just one example of a wide range ML/AI powered services that could use Kaskada under the hood.
Who are you targeting with your machine learning studio? What does your ideal customer profile look like?
We plan to help enterprise companies who either have ML use cases running in production environments or who are actively working to build these capabilities. We see companies outside large technology players as major beneficiaries of our product, since they often don’t have the resources to build out robust ML systems in-house.
What makes Machine Learning technology one of the most sought-after investments in the tech market?
Machine learning itself is not new, but its widespread use in enterprises is. Easy access to more powerful cloud compute has allowed companies to create applications that can make ML predictions on the fly with very low latencies. This, in turn, powers better, more personalized experiences for end users and a more responsive, intelligent automation for internal systems.
Running ML systems in production is still quite new and most data teams don’t have the right tools or processes in place to make ML scalable, accurate, and repeatable. These trends make ML/AI companies a prime area for investment.
AI, Blockchain, low-code DevOps, and RPA techniques are making a huge impact on the current tech markets. Where is the overall AI-ML market heading to in the next 4 years?
Many analysts report on ML/AI market growth, and they are better sources for this kind of high-level data.
Our belief is that ML/AI is poised for tremendous growth – hence our focus on this area as a startup.
Tell us more about the team you work with? What kind of skills and abilities does one need to be part of your technical product development team?
Our development team has back-end and front-end teams focused on solving different problems for our customers.
Our back-end team is building a large-scale data processing system, and our engineers typically have deep experience with distributed systems and big data. Our front-end team is building the “feature studio” for data scientists and has a strong background in data visualization and building great user experiences.
In both teams, we typically hire candidates with years of experience in software engineering at top-tier companies.
What other technologies actually integrate with Machine Learning engineering to give it a sharper edge in the IT industry?
Countless technologies either use ML to power their experiences or conversely are used as the building blocks of ML systems.
What is your opinion on “Weaponization of AI technologies”? How do you promote your ethical AI ideas in the modern Digital economy?
Ethical AI is an important consideration for any machine learning company and something we keep top of mind.
What unique AI / emerging technology start-ups and labs are you keenly following?
This is an exciting time to be involved in the ML/AI space. There are lots of companies doing great, ground-breaking work. We shouldn’t call out one or two at the expense of others.
What other emerging technologies within your industry are you interested in?
Many companies across the industry are pushing state-of-the-art technologies forward.
It’s always exciting to see at industry conferences how much progress there has been year after year. Recently, ML/AI has emerged as a hot topic. We expect this trend to continue with increased focus on the next generation of high-level data products.
Thank you, Davor! That was fun and hope to see you back on AiThority soon.
Davor is a co-founder and CEO of a Seattle-based startup Kaskada. Previously, he served as the chair of the Apache Beam Project Management Committee and software engineer in Google Cloud since its early days and the inception of Cloud Dataflow.
Kaskada is a machine learning company that enables collaboration among data scientists and data engineers. The company develops a machine learning studio for feature engineering using event-based data.
Kaskada’s platform allows data scientists to unify the feature engineering process across their organizations with a single platform for feature creation and feature serving.