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 knowledge analytics. It leverages each GPU and CPU processing to question large datasets shortly, with assist for SQL and geospatial knowledge. The platform consists of visible analytics instruments for interactive dashboards, cross-filtering, and scalable knowledge visualizations, enabling environment friendly massive 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 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 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 advisor I used to be liable 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 good friend 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. 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 instructing for a number of a long time and went searching for one thing new. HEAVY.AI sliced by and rendered that knowledge so easily and effortlessly that I used to be immediately hooked.
Quick ahead just a few years, and I nonetheless suppose 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 information 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 enormous language fashions is in making spatial modeling accessible to many extra folks. Nowadays, quite than spending years studying a posh interface with hundreds 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 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 in depth 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 you’ll want to account for each totally different units of human wants and the pure world. The final answer I realized early was to pair a way often called 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 damaging 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 usually do environmental planning at HEAVY.AI, this sample nonetheless works very properly 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 eventualities shortly.
I suppose my instructing expertise has given me deep empathy for software program customers, notably of complicated software program methods. The place one pupil stumbles in a single spot is random, however the place dozens or lots of of individuals make comparable errors, you’ve acquired a design situation. 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 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’ve 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 most recent HeavySQL and Charts. That implies that no matter query you ask, we’ll attempt to reply together 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 shopper genAI is that we tremendous tune fashions extensively in opposition to the precise sorts of questions enterprise customers ask of enterprise knowledge, together with spatial knowledge. So for instance our mannequin is great at performing spatial and time collection 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 potential by utilizing pure language processing (NLP). You ask a query in English—like, “What were the weather patterns in California last 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 giant 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 businesses?
HeavyIQ excels in environments the place you are coping with large-scale, high-velocity knowledge—precisely the sort 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, nevertheless it’s the tiny fraction of dropped calls that you’ll want to concentrate on. HeavyIQ permits you to sift by that knowledge 10 to 100 occasions sooner due to our GPU infrastructure. This velocity, mixed with the flexibility to interactively question and visualize knowledge, makes it invaluable for threat analytics in utilities or real-time situation planning for presidency businesses.
The opposite benefit already alluded to above, is that spatial and temporal SQL queries are extraordinarily highly effective analytically – however may be gradual or troublesome to put in writing by hand. When a system operates at what we name “the speed 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 incorrect at a selected 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 include the data wanted to reply a question. We additionally use inner firm paperwork the place offered to help in what is called retrieval-augmented technology (RAG). Lastly, the language fashions themselves generate additional metadata. All of those, however particularly the latter two may be of excessive enterprise sensitivity.
In contrast to 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 threat of leakage. For organizations that require the best ranges of safety, HeavyIQ may even be deployed in a totally air-gapped setting, 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 information 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 consider we’ve the best-in-class answer relating to GPU-accelerated analytics.
Even the perfect CPU-based methods run out of steam properly 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 properly 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 information. Then if you’d like ML inference modeling, we once more do this with out knowledge motion. And if you happen to interrogate the information 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 information 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 occasions sooner than conventional CPU-based databases and map servers. If 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 sort of efficiency enhance is completely important.
How does HEAVY.AI keep its aggressive edge within the fast-evolving panorama of massive knowledge analytics and AI?
That is an awesome query, and it is one thing we take into consideration always. The panorama of massive knowledge analytics and AI is evolving at an extremely fast tempo, with new breakthroughs and improvements taking place on a regular basis. It actually doesn’t damage that we’ve 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 massive, geospatial knowledge. Which means 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 properly 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 know-how alone is not sufficient. We’ve got 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 retaining 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, regardless of how shortly the panorama evolves.
How does HEAVY.AI assist emergency response efforts by HeavyEco?
We constructed HeavyEco after we noticed a few of our largest utility prospects having vital challenges merely ingesting at the 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 when you’re up in opposition to fast-moving excessive climate situations 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 eventualities, you’ll want to make choices 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 high of these, HeavyEco permits you to run eventualities, mannequin building-level impacts, and visualize knowledge in actual time. This offers 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 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 complicated spatial fashions, evaluating climate forecasts, or attempting to establish patterns in geospatial time collection, we would like them to have the ability to do it seamlessly, with none technical obstacles getting of their approach.
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 sorts 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 in opposition to trade benchmarks.
Our LLM is rather 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 person 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 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 consider 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 choices, sooner. Whether or not you are in telecom, utilities, or authorities, or different – being able to investigate 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 large gamers with large assets. We need to be sure that our prospects can make the most of the information they’ve, to remain forward and remedy issues as they come up. As knowledge continues to develop and change 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.
