Dr. Mike Flaxman is at the moment the VP of Product at HEAVY.AI, having beforehand served as Product Supervisor and led the Spatial Information 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 large datasets rapidly, with help for SQL and geospatial information. The platform contains visible analytics instruments for interactive dashboards, cross-filtering, and scalable information visualizations, enabling environment friendly large 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 educating 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 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 information and a pal pointed me to some model new and really related information – 5cm LiDAR. It was precisely what we would have liked scientifically, however one thing like 3600 instances 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 educating for a number of a long time and went on the lookout for one thing new. HEAVY.AI sliced via and rendered that information so easily and effortlessly that I used to be immediately hooked.
Quick ahead a number of 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 large information. This has the information quantity and processing pace part after all, primarily giving everybody their very own supercomputer. However an more and more essential facet with the appearance of huge language fashions is in making spatial modeling accessible to many extra individuals. Lately, fairly than spending years studying a fancy interface with 1000’s of instruments, you may 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 tough. 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 clients 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 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 eventualities and simulate their constructive and destructive impacts within the pc 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 properly in enterprise settings. So we assist clients assemble digital twins of key elements 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, significantly 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 obtained a design subject. 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 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 completely 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 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 most recent HeavySQL and Charts. That implies that no matter query you ask, we’ll attempt to reply together 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 clients precisely how we course of the queries. However behind the scenes, one other essential distinction relative to client genAI is that we positive tune fashions extensively in opposition to the particular forms of questions enterprise customers ask of enterprise information, together with spatial information. So for instance our mannequin is superb 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 doable through the use of 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 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 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 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 information—precisely the sort of information telcos, utilities, and authorities businesses deal with. Conventional enterprise intelligence instruments typically wrestle with the quantity and pace of this information. As an example, in telecommunications, you might need billions of name data, nevertheless it’s the tiny fraction of dropped calls that it is advisable to concentrate on. HeavyIQ means that you can sift via that information 10 to 100 instances sooner because of our GPU infrastructure. This pace, mixed with the power to interactively question and visualize information, makes it invaluable for threat 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 might be gradual or tough to write 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 would possibly discover a temporal spike in gear failures from a monitoring system, have the instinct that one thing goes incorrect at a selected facility, and examine 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 contains not solely information 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 inner firm paperwork the place offered 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 regionally 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 totally air-gapped surroundings, making certain that delicate data 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 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 got the best-in-class answer with regards 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 information 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 information 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 information. Then if you need ML inference modeling, we once more try this with out information motion. And when you 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 wish 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 are able to carry out not solely queries but in addition rendering 10 to 100 instances sooner than conventional CPU-based databases and map servers. While 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 sort of efficiency enhance is totally important.
How does HEAVY.AI preserve its aggressive edge within the fast-evolving panorama of huge information analytics and AI?
That is an amazing query, and it is one thing we take into consideration continuously. The panorama of huge information analytics and AI is evolving at an extremely fast tempo, with new breakthroughs and improvements occurring on a regular basis. It definitely doesn’t harm that we’ve got 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 information. Meaning frequently pushing the boundaries of what is doable 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 actually perceive the nuances of spatial SQL and geospatial evaluation.
We have constructed up an in depth library of coaching information, going properly 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’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 concentrate on delivering transformative worth to our customers. We’re not simply protecting tempo with the market – we’re pushing the boundaries of what is doable with large 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 via HeavyEco?
We constructed HeavyEco once we noticed a few of our largest utility clients having important 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 information, and when you’re up in opposition to fast-moving excessive climate circumstances like fires…that’s simply not adequate.
HeavyEco is designed to offer real-time insights in high-consequence conditions, like throughout a wildfire or flood. In such eventualities, it is advisable to make selections rapidly and based mostly on the absolute best information. So HeavyEco serves firstly as a professionally-managed information pipeline for authoritative fashions comparable to these from NOAA and USGS. On prime of these, HeavyEco means that you can run eventualities, mannequin building-level impacts, and visualize information in actual time. This provides first responders the important data they want when it issues most. It’s about turning complicated, large-scale datasets into actionable intelligence that may information speedy decision-making.
Finally, our purpose is to offer our customers the power to discover their information on the pace of thought. Whether or not they’re working complicated spatial fashions, evaluating climate forecasts, or making an attempt to determine 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 forms 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 important selections. So, we fine-tuned a variety 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 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 may 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 consider geospatial and temporal information analytics are going to be important for the way forward for many industries. What we’re actually centered on helps our clients make higher selections, sooner. Whether or not you are in telecom, utilities, or authorities, or different – being able to investigate and visualize information in real-time generally is a game-changer.
Our mission is to make this type of highly effective analytics accessible to everybody, not simply the massive gamers with large sources. We wish to be certain that our clients can reap the benefits of 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 clients are at all times ready for what’s subsequent.
Thanks for the nice interview, readers who want to be taught extra ought to go to HEAVY.AI.