Dr. Mike Flaxman is at the moment the VP of Product at HEAVY.AI, having beforehand served as Product Supervisor and led the Spatial Knowledge Science observe in Skilled Companies. 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 knowledge analytics. It leverages each GPU and CPU processing to question large datasets shortly, with help for SQL and geospatial knowledge. The platform contains visible analytics instruments for interactive dashboards, cross-filtering, and scalable knowledge visualizations, enabling environment friendly massive knowledge evaluation throughout numerous 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 shoppers. I’ve been concerned in GIS tasks throughout 17 nations. My work has taken me from advising organizations just like the Inter American Growth Financial institution to managing GIS expertise 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 chargeable for state of affairs planning for the Florida Seashores Habitat Conservation Program. My colleagues and I had been struggling to mannequin sea turtle habitat utilizing 30m Landsat knowledge and a good friend pointed me to some model new and really related knowledge – 5cm LiDAR. It was precisely what we wanted scientifically, however one thing like 3600 occasions bigger than what we’d deliberate to make use of. Evidently, nobody was going to extend my price range 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 a long time and went on the lookout for one thing new. HEAVY.AI sliced via and rendered that knowledge so easily and effortlessly that I used to be immediately hooked.
Quick ahead a couple of years, and I nonetheless assume what HEAVY.AI does is fairly distinctive and its early wager on GPU-analytics was precisely the place the trade nonetheless must go. HEAVY.AI is firmly focussed on democratizing entry to massive knowledge. This has the info quantity and processing velocity element in fact, basically giving everybody their very own supercomputer. However an more and more vital facet with the arrival of huge language fashions is in making spatial modeling accessible to many extra individuals. Nowadays, moderately than spending years studying a posh interface with 1000’s of instruments, you may 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 present, 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 essentially perceive the wants of our prospects and information our growth 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 it is advisable to account for each completely 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 deciding on a plan of motion, we’d make a number of situations and simulate their constructive and detrimental impacts within the laptop utilizing visualizations. Utilizing participatory processes allow us to mix numerous types of experience and remedy very complicated issues.
Whereas we don’t sometimes do environmental planning at HEAVY.AI, this sample nonetheless works very nicely in enterprise settings. So we assist prospects assemble digital twins of key elements of their enterprise, and we allow them to create and consider enterprise situations shortly.
I suppose my instructing expertise has given me deep empathy for software program customers, significantly of complicated software program methods. The place one scholar stumbles in a single spot is random, however the place dozens or a whole bunch of individuals make comparable errors, you recognize you’ve acquired a design problem. Maybe my favourite a part of software program design is taking these learnings and making use of them in designing new generations of methods.
Are you able to clarify how HeavyIQ leverages pure language processing to facilitate knowledge exploration and visualization?
Nowadays 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 completely different path. We consider 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 constructed these into our providing at a elementary 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 knowledge, 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 client genAI is that we wonderful tune fashions extensively in opposition to the particular varieties of questions enterprise customers ask of enterprise knowledge, together with spatial knowledge. 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 each day.
We bundle these core capabilities right into a Pocket book interface we name HeavyIQ. IQ is about making knowledge exploration and visualization as intuitive as potential by utilizing pure language processing (NLP). You ask a query in English—like, “What had been the climate patterns in California final week?”—and HeavyIQ interprets that into SQL queries that our GPU-accelerated database processes shortly. The outcomes are offered not simply as knowledge 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 typically not the primary query you ask, however maybe the third, that actually 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 businesses?
HeavyIQ excels in environments the place you are coping with large-scale, high-velocity knowledge—precisely the form of knowledge telcos, utilities, and authorities businesses deal with. Conventional enterprise intelligence instruments typically battle with the amount and velocity of this knowledge. As an illustration, in telecommunications, you may need billions of name data, but it surely’s the tiny fraction of dropped calls that it is advisable to concentrate on. HeavyIQ means that you can sift via that knowledge 10 to 100 occasions sooner due to our GPU infrastructure. This velocity, mixed with the power to interactively question and visualize knowledge, makes it invaluable for danger analytics in utilities or real-time state of affairs planning for presidency businesses.
The opposite benefit already alluded to above, is that spatial and temporal SQL queries are extraordinarily highly effective analytically – however will be gradual or troublesome to jot 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 gear failures from a monitoring system, have the instinct that one thing goes flawed at a specific facility, and examine this with a spatial question returning a map.
What measures are in place to forestall metadata leakage when utilizing HeavyIQ?
As described above, we’ve constructed HeavyIQ with privateness and safety at its core. This contains not solely knowledge 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 inner firm paperwork the place offered to help in what is named retrieval-augmented era (RAG). Lastly, the language fashions themselves generate additional metadata. All of those, however particularly the latter two will be of excessive enterprise sensitivity.
In contrast to third-party fashions the place your knowledge is often despatched off to exterior servers, HeavyIQ runs regionally on the identical GPU infrastructure as the remainder of our platform. This ensures that your knowledge and metadata stay underneath your management, with no danger of leakage. For organizations that require the best ranges of safety, HeavyIQ may even be deployed in a totally air-gapped surroundings, making certain that delicate info by no means leaves particular gear.
How does HEAVY.AI obtain excessive efficiency and scalability with large datasets utilizing GPU infrastructure?
The key sauce is actually in avoiding the info motion prevalent in different methods. 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 consider we’ve the best-in-class answer relating to GPU-accelerated analytics.
Even the very best CPU-based methods run out of steam nicely earlier than a middling GPU. The technique as soon as this occurs on CPU requires distributing knowledge throughout a number of cores after which a number of methods (so-called ‘horizontal scaling’). This works nicely in some contexts the place issues are much less time-critical, however typically begins getting bottlenecked on community efficiency.
Along with avoiding all of this knowledge motion on queries, we additionally keep away from it on many different widespread duties. The primary is that we are able to render graphics with out shifting the info. Then in order for you ML inference modeling, we once more do this with out knowledge motion. And for those who interrogate the info with a big language mannequin, we but once more do that with out knowledge motion. Even if you’re a knowledge scientist and need to interrogate the info from Python, we once more present strategies to do that on GPU with out knowledge motion.
What meaning in observe is that we are able to carry out not solely queries but additionally rendering 10 to 100 occasions sooner than conventional CPU-based databases and map servers. If you’re coping with the large, 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 enhance is completely important.
How does HEAVY.AI keep its aggressive edge within the fast-evolving panorama of huge knowledge analytics and AI?
That is a terrific query, and it is one thing we take into consideration consistently. The panorama of huge knowledge analytics and AI is evolving at an extremely fast tempo, with new breakthroughs and improvements occurring on a regular basis. It actually doesn’t damage that we’ve a ten yr headstart on GPU database expertise. .
I believe the important thing for us is to remain laser-focused on our core mission – democratizing entry to massive, geospatial knowledge. Meaning frequently pushing the boundaries of what is potential with GPU-accelerated analytics, and making certain our merchandise ship unparalleled efficiency and capabilities on this area. A giant a part of that’s our ongoing funding in growing customized, fine-tuned language fashions that really perceive the nuances of spatial SQL and geospatial evaluation.
We have constructed up an intensive library of coaching knowledge, going nicely past generic benchmarks, to make sure our conversational analytics instruments can have interaction with customers in a pure, intuitive approach. However we additionally know that expertise alone is not sufficient. We now have to remain deeply related to our prospects and their evolving wants. On the finish of the day, our aggressive edge comes all the way down to our relentless concentrate on delivering transformative worth to our customers. We’re not simply preserving tempo with the market – we’re pushing the boundaries of what is potential with massive knowledge and AI. And we’ll proceed to take action, irrespective of how shortly the panorama evolves.
How does HEAVY.AI help emergency response efforts via HeavyEco?
We constructed HeavyEco once we noticed a few of our largest utility prospects having vital challenges merely ingesting immediately’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 knowledge, and when you find yourself up in opposition to 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, it is advisable to make selections shortly and based mostly on the absolute best knowledge. So HeavyEco serves firstly as a professionally-managed knowledge pipeline for authoritative fashions akin to these from NOAA and USGS. On prime of these, HeavyEco means that you can run situations, mannequin building-level impacts, and visualize knowledge in actual time. This offers first responders the vital info they want when it issues most. It’s about turning complicated, large-scale datasets into actionable intelligence that may information instant decision-making.
Finally, our objective is to offer our customers the power to discover their knowledge on the velocity of thought. Whether or not they’re working complicated spatial fashions, evaluating climate forecasts, or making an attempt to establish patterns in geospatial time sequence, we wish them to have the ability to do it seamlessly, with none technical limitations getting of their approach.
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 vital selections. So, we fine-tuned a spread of open-source fashions and examined them in opposition to trade benchmarks.
Our LLM is far more correct for the superior SQL ideas our customers want, significantly in geospatial and temporal knowledge. 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 may import or tweak column-level metadata, or add steering info and instantly get suggestions.
How does HEAVY.AI envision the function of geospatial and temporal knowledge analytics in shaping the way forward for numerous industries?
We consider geospatial and temporal knowledge analytics are going to be vital for the way forward for many industries. What we’re actually centered on helps our prospects make higher selections, sooner. Whether or not you are in telecom, utilities, or authorities, or different – being able to investigate and visualize knowledge in real-time could be a game-changer.
Our mission is to make this sort of highly effective analytics accessible to everybody, not simply the large gamers with large sources. We need to be sure that our prospects can benefit from the info they’ve, to remain forward and remedy issues as they come up. As knowledge continues to develop and develop into extra complicated, we see our function as ensuring our instruments evolve proper alongside it, so our prospects are all the time ready for what’s subsequent.
Thanks for the nice interview, readers who want to be taught extra ought to go to HEAVY.AI.
