Dr. Mike Flaxman is presently the VP of Product at HEAVY.AI, having beforehand served as Product Supervisor and led the Spatial Knowledge Science apply in Skilled Providers. He has spent the final 20 years working in spatial environmental planning. Previous to HEAVY.AI, he based Geodesign Technolgoies, Inc and cofounded GeoAdaptive LLC, two startups making use of spatial evaluation applied sciences to planning. Earlier than startup life, he was a professor of planning at MIT and Business Supervisor at ESRI.
HEAVY.AI is a hardware-accelerated platform for real-time, high-impact information analytics. It leverages each GPU and CPU processing to question huge datasets rapidly, with help for SQL and geospatial information. The platform consists of visible analytics instruments for interactive dashboards, cross-filtering, and scalable information visualizations, enabling environment friendly huge information evaluation throughout varied industries.
Are you able to inform us about your skilled background and what led you to affix HEAVY.AI?
Earlier than becoming a member of HEAVY.AI, I spent years in academia, finally instructing spatial analytics at MIT. I additionally ran a small consulting agency, with a wide range of public sector purchasers. I’ve been concerned in GIS initiatives throughout 17 international locations. My work has taken me from advising organizations just like the Inter American Growth Financial institution to managing GIS know-how for structure, engineering and building at ESRI, the world’s largest GIS developer
I keep in mind vividly my first encounter with what’s now HEAVY.AI, which was when as a advisor I used to be accountable for situation planning for the Florida Seashores Habitat Conservation Program. My colleagues and I have been struggling to mannequin sea turtle habitat utilizing 30m Landsat information and a good friend pointed me to some model new and really related information – 5cm LiDAR. It was precisely what we wanted scientifically, however one thing like 3600 instances bigger than what we’d deliberate to make use of. Evidently, nobody was going to extend my finances by even a fraction of that quantity. In order that day I put down the instruments I’d been utilizing and instructing for a number of many years and went searching for one thing new. HEAVY.AI sliced by way of and rendered that information so easily and effortlessly that I used to be immediately hooked.
Quick ahead just a few years, and I nonetheless assume what HEAVY.AI does is fairly distinctive and its early guess on GPU-analytics was precisely the place the business nonetheless must go. HEAVY.AI is firmly focussed on democratizing entry to huge information. This has the information quantity and processing velocity element in fact, basically giving everybody their very own supercomputer. However an more and more vital side with the arrival of enormous language fashions is in making spatial modeling accessible to many extra individuals. Lately, quite than spending years studying a fancy interface with 1000’s of instruments, you’ll be able to simply begin a dialog with HEAVY.AI within the human language of your selection. This system not solely generates the instructions required, but additionally presents related visualizations.
Behind the scenes, delivering ease of use is in fact very troublesome. At the moment, because the VP of Product Administration at HEAVY.AI, I am closely concerned in figuring out which options and capabilities we prioritize for our merchandise. My intensive background in GIS permits me to actually perceive the wants of our prospects and information our improvement roadmap accordingly.
How has your earlier expertise in spatial environmental planning and startups influenced your work at HEAVY.AI?
Environmental planning is a very difficult area in that you might want to account for each totally different units of human wants and the pure world. The overall answer I discovered early was to pair a way generally known as participatory planning, with the applied sciences of distant sensing and GIS. Earlier than selecting a plan of motion, we’d make a number of situations and simulate their constructive and destructive impacts within the laptop utilizing visualizations. Utilizing participatory processes allow us to mix varied types of experience and remedy very complicated issues.
Whereas we don’t sometimes do environmental planning at HEAVY.AI, this sample nonetheless works very effectively in enterprise settings. So we assist prospects assemble digital twins of key components of their enterprise, and we allow them to create and consider enterprise situations rapidly.
I suppose my instructing expertise has given me deep empathy for software program customers, notably of complicated software program programs. The place one scholar stumbles in a single spot is random, however the place dozens or lots of of individuals make related errors, you understand you’ve bought a design difficulty. Maybe my favourite a part of software program design is taking these learnings and making use of them in designing new generations of programs.
Are you able to clarify how HeavyIQ leverages pure language processing to facilitate information exploration and visualization?
Lately it appears everybody and their brother is touting a brand new genAI mannequin, most of them forgettable clones of one another. We’ve taken a really totally different path. We imagine that accuracy, reproducibility and privateness are important traits for any enterprise analytics instruments, together with these generated with massive language fashions (LLMs). So we’ve got constructed these into our providing at a basic stage. For instance, we constrain mannequin inputs strictly to enterprise databases and to offer paperwork inside an enterprise safety perimeter. We additionally constrain outputs to the newest HeavySQL and Charts. That implies that no matter query you ask, we’ll attempt to reply along with your information, and we’ll present you precisely how we derived that reply.
With these ensures in place, it issues much less to our prospects precisely how we course of the queries. However behind the scenes, one other vital distinction relative to shopper genAI is that we superb tune fashions extensively towards the particular varieties of questions enterprise customers ask of enterprise information, together with spatial information. So for instance our mannequin is great at performing spatial and time sequence joins, which aren’t in classical SQL benchmarks however our customers use every day.
We bundle these core capabilities right into a Pocket book interface we name HeavyIQ. IQ is about making information exploration and visualization as intuitive as attainable by utilizing pure language processing (NLP). You ask a query in English—like, “What have been the climate patterns in California final week?”—and HeavyIQ interprets that into SQL queries that our GPU-accelerated database processes rapidly. The outcomes are introduced not simply as information however as visualizations—maps, charts, no matter’s most related. It’s about enabling quick, interactive querying, particularly when coping with massive or fast-moving datasets. What’s key right here is that it’s usually not the primary query you ask, however maybe the third, that basically will get to the core perception, and HeavyIQ is designed to facilitate that deeper exploration.
What are the first advantages of utilizing HeavyIQ over conventional BI instruments for telcos, utilities, and authorities companies?
HeavyIQ excels in environments the place you are coping with large-scale, high-velocity information—precisely the form of information telcos, utilities, and authorities companies deal with. Conventional enterprise intelligence instruments usually battle with the amount and velocity of this information. As an illustration, in telecommunications, you may need billions of name data, but it surely’s the tiny fraction of dropped calls that you might want to concentrate on. HeavyIQ lets you sift by way of that information 10 to 100 instances sooner due to our GPU infrastructure. This velocity, mixed with the power to interactively question and visualize information, makes it invaluable for threat analytics in utilities or real-time situation planning for presidency companies.
The opposite benefit already alluded to above, is that spatial and temporal SQL queries are extraordinarily highly effective analytically – however might be sluggish or troublesome to write down by hand. When a system operates at what we name “the velocity of curiosity” customers can ask each extra questions and extra nuanced questions. So for instance a telco engineer would possibly discover a temporal spike in tools failures from a monitoring system, have the instinct that one thing goes unsuitable at a specific facility, and test this with a spatial question returning a map.
What measures are in place to stop metadata leakage when utilizing HeavyIQ?
As described above, we’ve constructed HeavyIQ with privateness and safety at its core. This consists of not solely information but additionally a number of sorts of metadata. We use column and table-level metadata extensively in figuring out which tables and columns include the data wanted to reply a question. We additionally use inside firm paperwork the place supplied to help in what is called retrieval-augmented era (RAG). Lastly, the language fashions themselves generate additional metadata. All of those, however particularly the latter two might be of excessive enterprise sensitivity.
In contrast to third-party fashions the place your information is usually despatched off to exterior servers, HeavyIQ runs domestically on the identical GPU infrastructure as the remainder of our platform. This ensures that your information and metadata stay beneath your management, with no threat of leakage. For organizations that require the very best ranges of safety, HeavyIQ may even be deployed in a very air-gapped setting, making certain that delicate data by no means leaves particular tools.
How does HEAVY.AI obtain excessive efficiency and scalability with huge datasets utilizing GPU infrastructure?
The key sauce is basically in avoiding the information motion prevalent in different programs. At its core, this begins with a purpose-built database that is designed from the bottom as much as run on NVIDIA GPUs. We have been engaged on this for over 10 years now, and we actually imagine we’ve got the best-in-class answer with regards to GPU-accelerated analytics.
Even the perfect CPU-based programs run out of steam effectively earlier than a middling GPU. The technique as soon as this occurs on CPU requires distributing information throughout a number of cores after which a number of programs (so-called ‘horizontal scaling’). This works effectively in some contexts the place issues are much less time-critical, however usually begins getting bottlenecked on community efficiency.
Along with avoiding all of this information motion on queries, we additionally keep away from it on many different widespread duties. The primary is that we will render graphics with out shifting the information. Then if you need ML inference modeling, we once more do this with out information motion. And if you happen to interrogate the information with a big language mannequin, we but once more do that with out information motion. Even in case you are a knowledge scientist and need to interrogate the information from Python, we once more present strategies to do that on GPU with out information motion.
What meaning in apply is that we will carry out not solely queries but additionally rendering 10 to 100 instances sooner than conventional CPU-based databases and map servers. Whenever you’re coping with the huge, high-velocity datasets that our prospects work with – issues like climate fashions, telecom name data, or satellite tv for pc imagery – that form of efficiency increase is totally important.
How does HEAVY.AI preserve its aggressive edge within the fast-evolving panorama of huge information analytics and AI?
That is a terrific query, and it is one thing we take into consideration continually. The panorama of huge information analytics and AI is evolving at an extremely fast tempo, with new breakthroughs and improvements taking place on a regular basis. It definitely doesn’t harm that we’ve got a ten yr headstart on GPU database know-how. .
I feel the important thing for us is to remain laser-focused on our core mission – democratizing entry to huge, geospatial information. Meaning regularly pushing the boundaries of what is attainable with GPU-accelerated analytics, and making certain our merchandise ship unparalleled efficiency and capabilities on this area. An enormous a part of that’s our ongoing funding in creating customized, fine-tuned language fashions that really perceive the nuances of spatial SQL and geospatial evaluation.
We have constructed up an in depth library of coaching information, going effectively past generic benchmarks, to make sure our conversational analytics instruments can interact with customers in a pure, intuitive means. However we additionally know that know-how alone is not sufficient. Now we have to remain deeply linked to our prospects and their evolving wants. On the finish of the day, our aggressive edge comes right down to our relentless concentrate on delivering transformative worth to our customers. We’re not simply conserving tempo with the market – we’re pushing the boundaries of what is attainable with huge information and AI. And we’ll proceed to take action, regardless of how rapidly the panorama evolves.
How does HEAVY.AI help emergency response efforts by way of HeavyEco?
We constructed HeavyEco once we noticed a few of our largest utility prospects having important challenges merely ingesting right now’s climate mannequin outputs, in addition to visualizing them for joint comparisons. It was taking one buyer as much as 4 hours simply to load information, and when you’re up towards fast-moving excessive climate situations like fires…that’s simply not ok.
HeavyEco is designed to offer real-time insights in high-consequence conditions, like throughout a wildfire or flood. In such situations, you might want to make choices rapidly and based mostly on the absolute best information. So HeavyEco serves firstly as a professionally-managed information pipeline for authoritative fashions corresponding to these from NOAA and USGS. On prime of these, HeavyEco lets you run situations, mannequin building-level impacts, and visualize information in actual time. This offers first responders the crucial data they want when it issues most. It’s about turning complicated, large-scale datasets into actionable intelligence that may information rapid decision-making.
Finally, our aim is to present our customers the power to discover their information on the velocity of thought. Whether or not they’re operating complicated spatial fashions, evaluating climate forecasts, or attempting to establish patterns in geospatial time sequence, we would like them to have the ability to do it seamlessly, with none technical limitations getting of their means.
What distinguishes HEAVY.AI’s proprietary LLM from different third-party LLMs by way of accuracy and efficiency?
Our proprietary LLM is particularly tuned for the varieties of analytics we concentrate on—like text-to-SQL and text-to-visualization. We initially tried conventional third-party fashions, however discovered they didn’t meet the excessive accuracy necessities of our customers, who are sometimes making crucial choices. So, we fine-tuned a variety of open-source fashions and examined them towards business benchmarks.
Our LLM is way more correct for the superior SQL ideas our customers want, notably in geospatial and temporal information. Moreover, as a result of it runs on our GPU infrastructure, it’s additionally safer.
Along with the built-in mannequin capabilities, we additionally present a full interactive consumer interface for directors and customers so as to add area or business-relevant metadata. For instance, if the bottom mannequin doesn’t carry out as anticipated, you’ll be able to import or tweak column-level metadata, or add steerage data and instantly get suggestions.
How does HEAVY.AI envision the function of geospatial and temporal information analytics in shaping the way forward for varied industries?
We imagine geospatial and temporal information analytics are going to be crucial for the way forward for many industries. What we’re actually centered on helps our prospects make higher choices, sooner. Whether or not you are in telecom, utilities, or authorities, or different – being able to investigate and visualize information in real-time could be a game-changer.
Our mission is to make this type of highly effective analytics accessible to everybody, not simply the massive gamers with huge assets. We need to be sure that our prospects can benefit from the information they’ve, to remain forward and remedy issues as they come up. As information continues to develop and turn into extra complicated, we see our function as ensuring our instruments evolve proper alongside it, so our prospects are at all times ready for what’s subsequent.
Thanks for the good interview, readers who want to be taught extra ought to go to HEAVY.AI.
