Dr. Mike Flaxman is presently the VP of Product at HEAVY.AI, having beforehand served as Product Supervisor and led the Spatial Information Science follow 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 consists of visible analytics instruments for interactive dashboards, cross-filtering, and scalable knowledge visualizations, enabling environment friendly huge knowledge 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 educating 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 initiatives throughout 17 nations. My work has taken me from advising organizations just like the Inter American Improvement Financial institution to managing GIS know-how for structure, engineering and development at ESRI, the world’s largest GIS developer
I bear in mind vividly my first encounter with what’s now HEAVY.AI, which was when as a marketing consultant 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 knowledge and a pal pointed me to some model new and really related knowledge – 5cm LiDAR. It was precisely what we would have liked scientifically, however one thing like 3600 occasions 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 educating for a number of many years and went on the lookout for one thing new. HEAVY.AI sliced by means of and rendered that knowledge so easily and effortlessly that I used to be immediately hooked.
Quick ahead a couple of years, and I nonetheless suppose 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 knowledge. This has the info quantity and processing pace element after all, primarily giving everybody their very own supercomputer. However an more and more essential side with the arrival of huge language fashions is in making spatial modeling accessible to many extra folks. Lately, somewhat than spending years studying a fancy interface with hundreds of instruments, you possibly can simply begin a dialog with HEAVY.AI within the human language of your alternative. This system not solely generates the instructions required, but in addition presents related visualizations.
Behind the scenes, delivering ease of use is after all 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 in depth 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 that you must account for each completely different units of human wants and the pure world. The final answer I realized early was to pair a way often 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 optimistic and detrimental impacts within the pc utilizing visualizations. Utilizing participatory processes allow us to mix varied types of experience and resolve very complicated issues.
Whereas we don’t usually do environmental planning at HEAVY.AI, this sample nonetheless works very effectively 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 educating expertise has given me deep empathy for software program customers, notably of complicated software program methods. The place one scholar stumbles in a single spot is random, however the place dozens or tons of of individuals make comparable errors, you realize you’ve received 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 methods.
Are you able to clarify how HeavyIQ leverages pure language processing to facilitate knowledge 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 completely 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 supply 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 are going to attempt to reply along with your knowledge, and we are going to 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 essential distinction relative to client genAI is that we effective tune fashions extensively in opposition to the particular kinds 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 have 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 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 knowledge—precisely the form of knowledge telcos, utilities, and authorities companies deal with. Conventional enterprise intelligence instruments typically battle with the amount and pace of this knowledge. As an example, in telecommunications, you may need billions of name data, nevertheless it’s the tiny fraction of dropped calls that that you must deal with. HeavyIQ lets you sift by means of that knowledge 10 to 100 occasions quicker due to our GPU infrastructure. This pace, mixed with the power to interactively question and visualize knowledge, makes it invaluable for danger 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 could be gradual or troublesome to jot down by hand. When a system operates at what we name “the pace of curiosity” customers can ask each extra questions and extra nuanced questions. So for instance a telco engineer may discover a temporal spike in tools failures from a monitoring system, have the instinct that one thing goes improper at a specific facility, and verify 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 knowledge but in addition a number of sorts of metadata. We use column and table-level metadata extensively in figuring out which tables and columns comprise the knowledge wanted to reply a question. We additionally use inside firm paperwork the place offered to help in what is named retrieval-augmented technology (RAG). Lastly, the language fashions themselves generate additional metadata. All of those, however particularly the latter two could be of excessive enterprise sensitivity.
In contrast to third-party fashions the place your knowledge 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 knowledge and metadata stay below your management, with no danger of leakage. For organizations that require the very best ranges of safety, HeavyIQ may even be deployed in a very air-gapped surroundings, making certain that delicate info by no means leaves particular tools.
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 really imagine we’ve got the best-in-class answer in terms of GPU-accelerated analytics.
Even the perfect CPU-based methods run out of steam effectively 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 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 knowledge motion on queries, we additionally keep away from it on many different frequent duties. The primary is that we will render graphics with out shifting the info. Then if you would like ML inference modeling, we once more try this with out knowledge motion. And in the event you interrogate the info with a big language mannequin, we but once more do that with out knowledge motion. Even in case you are an information scientist and need to interrogate the info from Python, we once more present strategies to do that on GPU with out knowledge motion.
What which means in follow is that we will carry out not solely queries but in addition rendering 10 to 100 occasions quicker than conventional CPU-based databases and map servers. Once 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 enhance is totally important.
How does HEAVY.AI preserve its aggressive edge within the fast-evolving panorama of massive knowledge analytics and AI?
That is an ideal query, and it is one thing we take into consideration consistently. The panorama of massive 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 harm that we’ve got a ten 12 months headstart on GPU database know-how. .
I believe the important thing for us is to remain laser-focused on our core mission – democratizing entry to huge, 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 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. We have now 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 deal with delivering transformative worth to our customers. We’re not simply conserving tempo with the market – we’re pushing the boundaries of what is potential with huge knowledge and AI. And we’ll proceed to take action, regardless of how shortly the panorama evolves.
How does HEAVY.AI help emergency response efforts by means of HeavyEco?
We constructed HeavyEco after we noticed a few of our largest utility prospects having vital challenges merely ingesting right this moment’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 if you end up up in opposition to fast-moving excessive climate circumstances like fires…that’s simply not ok.
HeavyEco is designed to supply real-time insights in high-consequence conditions, like throughout a wildfire or flood. In such situations, that you must make selections shortly and based mostly on the absolute best knowledge. So HeavyEco serves firstly as a professionally-managed knowledge pipeline for authoritative fashions corresponding to these from NOAA and USGS. On high of these, HeavyEco lets you run situations, mannequin building-level impacts, and visualize knowledge in actual time. This provides first responders the crucial info they want when it issues most. It’s about turning complicated, large-scale datasets into actionable intelligence that may information fast decision-making.
In the end, our purpose is to present our customers the power to discover their knowledge on the pace of thought. Whether or not they’re operating complicated spatial fashions, evaluating climate forecasts, or making an attempt to determine patterns in geospatial time sequence, we wish them to have the ability to do it seamlessly, with none technical boundaries 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 kinds of analytics we deal with—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 selections. So, we fine-tuned a variety of open-source fashions and examined them in opposition to business benchmarks.
Our LLM is way more correct for the superior SQL ideas our customers want, notably 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 possibly can 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 varied industries?
We imagine geospatial and temporal knowledge analytics are going to be crucial for the way forward for many industries. What we’re actually centered on helps our prospects make higher selections, quicker. Whether or not you are in telecom, utilities, or authorities, or different – being able to research and visualize knowledge in real-time is usually a game-changer.
Our mission is to make this type of highly effective analytics accessible to everybody, not simply the massive gamers with large assets. We need to be sure that our prospects can make the most of the info they’ve, to remain forward and resolve issues as they come up. As knowledge continues to develop and turn 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 study extra ought to go to HEAVY.AI.
