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 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 knowledge analytics. It leverages each GPU and CPU processing to question large datasets rapidly, 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 large 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, in the end 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 tasks throughout 17 international locations. 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 keep in mind vividly my first encounter with what’s now HEAVY.AI, which was when as a marketing consultant I used to be chargeable for state of affairs 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 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 instances bigger than what we’d deliberate to make use of. For sure, 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 educating for a number of a long time and went searching 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 just a few 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 large knowledge. This has the info quantity and processing velocity part in fact, basically giving everybody their very own supercomputer. However an more and more vital side with the arrival of huge language fashions is in making spatial modeling accessible to many extra folks. As of late, moderately than spending years studying a fancy interface with 1000’s of instruments, you possibly can simply begin a dialog with HEAVY.AI within the human language of your selection. This system not solely generates the instructions required, but in addition presents related visualizations.
Behind the scenes, delivering ease of use is in fact very troublesome. Presently, 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 clients 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 it is advisable account for each totally different units of human wants and the pure world. The final answer I discovered early was to pair a technique referred to 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 eventualities and simulate their optimistic and detrimental impacts within the pc utilizing visualizations. Utilizing participatory processes allow us to mix varied types of experience and clear up very advanced issues.
Whereas we don’t sometimes do environmental planning at HEAVY.AI, this sample nonetheless works very effectively in enterprise settings. So we assist clients assemble digital twins of key components of their enterprise, and we allow them to create and consider enterprise eventualities rapidly.
I suppose my educating expertise has given me deep empathy for software program customers, notably of advanced software program programs. The place one scholar stumbles in a single spot is random, however the place dozens or a whole lot of individuals make comparable errors, you’ve received a design concern. 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 knowledge exploration and visualization?
As of late 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 consider that accuracy, reproducibility and privateness are important traits for any enterprise analytics instruments, together with these generated with giant language fashions (LLMs). So we have now 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 signifies 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 clients precisely how we course of the queries. However behind the scenes, one other vital distinction relative to shopper genAI is that we advantageous tune fashions extensively in opposition to the precise 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 day by day.
We package deal these core capabilities right into a Pocket book interface we name HeavyIQ. IQ is about making knowledge 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 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 giant 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 knowledge—precisely the type of knowledge telcos, utilities, and authorities companies deal with. Conventional enterprise intelligence instruments usually battle with the amount and velocity of this knowledge. As an illustration, in telecommunications, you may need billions of name data, nevertheless it’s the tiny fraction of dropped calls that it is advisable give attention to. HeavyIQ lets you sift via that knowledge 10 to 100 instances quicker because of our GPU infrastructure. This velocity, mixed with the flexibility to interactively question and visualize knowledge, makes it invaluable for danger analytics in utilities or real-time state of affairs 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 gradual 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 fallacious at a specific facility, and test 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 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 data wanted to reply a question. We additionally use inner firm paperwork the place supplied 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 might be of excessive enterprise sensitivity.
Not like third-party fashions the place your knowledge is usually 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 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 basically in avoiding the info 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 consider we have now the best-in-class answer on the subject of GPU-accelerated analytics.
Even the very best CPU-based programs 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 programs (so-called ‘horizontal scaling’). This works effectively 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 will render graphics with out transferring the info. Then if you would like ML inference modeling, we once more do this with out knowledge motion. And if you happen to 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 wish to interrogate the info from Python, we once more present strategies to do that on GPU with out knowledge motion.
What which means in apply is that we will carry out not solely queries but in addition rendering 10 to 100 instances quicker than conventional CPU-based databases and map servers. If you’re coping with the large, high-velocity datasets that our clients work with – issues like climate fashions, telecom name data, or satellite tv for pc imagery – that type of efficiency increase is completely important.
How does HEAVY.AI preserve 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 continually. The panorama of huge knowledge 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 have now a ten 12 months 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 large, geospatial knowledge. 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. A giant a part of that’s our ongoing funding in growing customized, fine-tuned language fashions that actually 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 have interaction with customers in a pure, intuitive means. However we additionally know that know-how alone is not sufficient. We’ve to remain deeply linked to our clients and their evolving wants. On the finish of the day, our aggressive edge comes right down to our relentless give attention to 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 large knowledge and AI. And we’ll proceed to take action, regardless of how rapidly 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 clients 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 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 eventualities, it is advisable make selections rapidly and primarily based on the absolute best knowledge. So HeavyEco serves firstly as a professionally-managed knowledge pipeline for authoritative fashions similar to these from NOAA and USGS. On high of these, HeavyEco lets you run eventualities, mannequin building-level impacts, and visualize knowledge in actual time. This provides first responders the essential info they want when it issues most. It’s about turning advanced, large-scale datasets into actionable intelligence that may information instant decision-making.
Finally, our aim is to provide our customers the flexibility to discover their knowledge on the velocity of thought. Whether or not they’re operating advanced 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 limitations getting of their means.
What distinguishes HEAVY.AI’s proprietary LLM from different third-party LLMs when it comes to accuracy and efficiency?
Our proprietary LLM is particularly tuned for the varieties of analytics we give attention to—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 essential selections. So, we fine-tuned a variety of open-source fashions and examined them in opposition to trade 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 steerage info and instantly get suggestions.
How does HEAVY.AI envision the position of geospatial and temporal knowledge analytics in shaping the way forward for varied industries?
We consider geospatial and temporal knowledge analytics are going to be essential for the way forward for many industries. What we’re actually centered on helps our clients 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 generally is a game-changer.
Our mission is to make this sort of highly effective analytics accessible to everybody, not simply the large gamers with large assets. We wish to be certain that our clients can benefit from the info they’ve, to remain forward and clear up issues as they come up. As knowledge continues to develop and turn into extra advanced, we see our position as ensuring our instruments evolve proper alongside it, so our clients 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.
