Could you tell us about your interaction with smart technologies like AI, IT, and Computing platforms?

I have over 20 years of experience working with large scale enterprises and high-volume computing systems including AI, Data Analysis, Cloud Computing, and Cybersecurity. Prior to prooV, I Co-founded three startups in the domains of Big Data crash prevention, user engagement algorithms, and performance monitoring.

How did you start in this space? What galvanized you to start at prooV?

I started out as a Data Scientist on small projects, worked my way up to Software Engineer and Team Leader on large enterprise projects with various companies until I became VP of R&D. After that, I became a Serial Entrepreneur and met Toby Olshanetsky in my ventures. We both saw first-hand how enterprises adopted new technologies at a painfully slow rate, which was the impetus to start prooV. We interviewed companies and vendors around the world and realized that the way enterprises conducted the proof-of-concept (PoC) stage was the biggest challenge to innovation. We started prooV as a solution to this problem, by streamlining the PoC process– enterprises can now test multiple technologies in an efficient and secure manner– allowing them to adopt innovative technologies faster than ever before.

What kind of background and specialization one needs to crack the code in modern IT Ops and Data Science industry?

If you want to become an expert in Data Science/IT opps, then you need to start off as a Software Engineer and get a strong understanding of object-oriented programming. Then you can specialize and develop expertise in the latest infrastructure platforms like AWS and Google– it also helps if you learn advanced Data Analysis as well.

What message do you have for professionals just starting with Data Science, AI, Cloud Computing and ITOps technologies?

It’s important to have a basic understanding of the entire spectrum, including Advanced Cloud, Cyber, Decentralized, Crypto, Data Analysis, etc – before you choose a specialty. For AI, I recommend taking some courses since a lot of theoretical mathematics is involved here and it isn’t something you learn by doing simple exercises. As for Cloud Computing, materials from Cloud Service Providers (CSPs) give a high-level understanding that can replace courses, but you need practical technological experience for an in-depth understanding. Learning from other experts in the space can provide unlimited value, as their years of experience can provide context if you’re looking to transition into the industry.

Which industries have been the fastest to adopt the PoC platforms for their businesses?

We found the highly regulated ones such as Pharma, FintTech, Insurance, and Banking have been the fastest. Any multinational that carries a tremendous amount of sensitive consumer data that can’t be risked for proof-of-concepts have either adopted or is considering PoC platforms. The main benefits of PoC platforms are perfect for these industries because it can protect their sensitive data and ensure compliance with regulations by adding multiple security safeguards during the PoC process.

Could you tell us more about prooV Lab and how it bridges the hardware-software gaps?

prooV Lab is the platform in which multiple PoCs from different vendors can be evaluated simultaneously. We use multiple technologies to streamline the PoC process for efficiency and safeguard sensitive company data to ensure security as well as compliance. We specifically made prooV Lab to test software PoCs so we don’t bridge the hardware-software gap and nor did we intend to in the first place. That said, our platform does evaluate how PoCs could potentially interact with data from a company’s hardware. For example, IoT PoCs that rely on linking with hardware are given simulated data on prooV Lab to monitor its reaction, but we are mainly focused on software.

How important is a secure testing platform for modern businesses? What kind of security features do you offer to your enterprise customers and developers?

I like to clarify the terminology first, as there’s a difference between “testing” and PoC. Testing is checking an immature technology in the development stage and PoC is when mature products are being evaluated for compatibility. PoC is a tool for open innovation by allowing the integration of third-party technologies, but access is highly secure and only vetted vendors are invited to PoCs.

The prooV platform uses patented deep mirroring technology that enables a full evaluation of technologies without ever compromising data by using anonymized datasets to generate millions of data records. This allows companies to evaluate a technology using mirrored data that is similar to their actual data without ever putting it at risk. We even offer an Anonymous Data SDK so the data sample used in the PoC is compliant with regulations. There’s also an additional add-on to the prooV platform known as Red Cloud, which provides a secure and tailored Cloud environment that allows for safe red team testing and cybersecurity evaluation.

What is your opinion about the growing competition in secure testing architecture and the kind of technologies developed by Open-source communities?

In the PoC space, we don’t see new competitors within and outside the open-source community. Open-source software, in particular, needs to be carefully tested to ensure that it doesn’t create or facilitate new vulnerabilities to the legacy system. We took this into account and developed SafeZone as another add-on to the prooV platform. It allows companies to integrate open source-solutions into a secure capsule within the company’s environment before deploying it into the entire ecosystem.

Where do you see AI/Machine Learning and other smart technologies heading in your industry beyond 2020?

We’re already seeing the power and potential of AI/Machine Learning today. At prooV, AI is already a critical element of our platform and enables the scalability of PoCs to handle multiple vendors simultaneously. Beyond 2020, we’re going to see more seamless integration with these technologies – in very heavily regulated industries like Insurance and Healthcare – to start incorporating more and more AI and ML into their practices. Many larger companies in regulated spaces aren’t integrating smart technologies as quickly because it takes time to transition from traditional methodologies.

The Good, the Bad, and the Ugly about AI that you have heard or you predict today for the future world?

The Good: Robotic process automation (RPA) will optimize processes in an organization based on AI learning

The Bad: AI in criminal hands is a growing danger and threat to companies in the future.

The Ugly: Countries and organizations using AI in attempts to gain power over the people.

What AI, Cloud and SaaS start-ups and labs are you keenly following?

I follow trends more than companies – specifically the next generation in quantum computing, AI Automation, Cloud-native,

What technologies within your current market are you interested in?

I’ve been really interested in the growing need for Cloud, Multi-Cloud and edge providers in PoC platforms. I always look out for anything that could disrupt the process of understanding data, and everything that is related to future containers and serverless development.

What are the new emerging markets for these technology markets?

Off the top of my head: Innovation adoption, Digital innovation, AIOPS.

What’s your smartest work-related shortcut or productivity hack?

I like to define the essential KPI in every mission so I can focus solely on that and not on anything else on the side.

Tag the one person in the industry whose answers to these questions you would love to read

Max Levchin