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 huge datasets shortly, with assist for SQL and geospatial information. The platform consists of 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 hitch 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 purchasers. I’ve been concerned in GIS initiatives 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 bear in mind vividly my first encounter with what’s now HEAVY.AI, which was when as a marketing consultant I used to be answerable for situation 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 good friend 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 many years and went in search of one thing new. HEAVY.AI sliced by way of and rendered that information so easily and effortlessly that I used to be immediately hooked.
Quick ahead a number of years, and I nonetheless assume 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 large information. This has the info quantity and processing velocity element in fact, basically 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. Nowadays, relatively than spending years studying a fancy interface with hundreds of instruments, you’ll be able to simply begin a dialog with HEAVY.AI within the human language of your alternative. This system not solely generates the instructions required, but additionally presents related visualizations.
Behind the scenes, delivering ease of use is in fact very tough. 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 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 you could account for each totally different units of human wants and the pure world. The overall resolution I discovered early was to pair a technique often known as participatory planning, with the applied sciences of distant sensing and GIS. Earlier than selecting a plan of motion, we’d make a number of eventualities and simulate their optimistic and adverse impacts within the laptop utilizing visualizations. Utilizing participatory processes allow us to mix varied types of experience and resolve very complicated issues.
Whereas we don’t sometimes 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 eventualities shortly.
I suppose my educating expertise has given me deep empathy for software program customers, notably of complicated software program programs. 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 realize you’ve obtained 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 information 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 imagine 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 degree. 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 signifies that no matter query you ask, we are going to attempt to reply together with your information, 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 fantastic tune fashions extensively towards the precise forms of questions enterprise customers ask of enterprise information, together with spatial information. 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 bundle these core capabilities right into a Pocket book interface we name HeavyIQ. IQ is about making information exploration and visualization as intuitive as potential through the use of 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 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 battle with the amount and velocity of this information. As an illustration, in telecommunications, you may need billions of name data, nevertheless it’s the tiny fraction of dropped calls that you could give attention to. HeavyIQ lets you sift by way of that information 10 to 100 instances quicker due to our GPU infrastructure. This velocity, mixed with the power to interactively question and visualize information, 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 could be gradual or tough to jot down 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 information 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 knowledge wanted to reply a question. We additionally use inside firm paperwork the place supplied 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 could 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 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 atmosphere, making certain that delicate info by no means leaves particular tools.
How does HEAVY.AI obtain excessive efficiency and scalability with huge 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 imagine we have now the best-in-class resolution relating to GPU-accelerated analytics.
Even the most effective CPU-based programs run out of steam effectively 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 programs (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 information motion on queries, we additionally keep away from it on many different frequent duties. The primary is that we are able to render graphics with out shifting the info. Then if you would like ML inference modeling, we once more try this with out information motion. And in the event you interrogate the info with a big language mannequin, we but once more do that with out information 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 information motion.
What meaning in apply is that we are able to carry out not solely queries but additionally 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 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 preserve its aggressive edge within the fast-evolving panorama of huge information analytics and AI?
That is an incredible query, and it is one thing we take into consideration continuously. The panorama of huge information analytics and AI is evolving at an extremely speedy tempo, with new breakthroughs and improvements taking place on a regular basis. It actually doesn’t damage that we have now a ten 12 months headstart on GPU database expertise. .
I feel the important thing for us is to remain laser-focused on our core mission – democratizing entry to large, geospatial information. Meaning regularly pushing the boundaries of what is potential 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 growing 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 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 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 give attention to 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 large information 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 way of HeavyEco?
We constructed HeavyEco once we noticed a few of our largest utility prospects having vital challenges merely ingesting as we speak’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 if you end up up towards fast-moving excessive climate circumstances like fires…that’s simply not adequate.
HeavyEco is designed to supply real-time insights in high-consequence conditions, like throughout a wildfire or flood. In such eventualities, you could make selections shortly and primarily based on the absolute best information. So HeavyEco serves firstly as a professionally-managed information pipeline for authoritative fashions akin to these from NOAA and USGS. On prime of these, HeavyEco lets you run eventualities, mannequin building-level impacts, and visualize information 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 quick decision-making.
Finally, our aim is to provide our customers the power to discover their information on the velocity of thought. Whether or not they’re working complicated spatial fashions, evaluating climate forecasts, or attempting to determine patterns in geospatial time sequence, we would like 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 forms 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 vital selections. So, we fine-tuned a spread of open-source fashions and examined them towards business benchmarks.
Our LLM is rather more correct for the superior SQL ideas our customers want, notably 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’ll be able to import or tweak column-level metadata, or add steerage info 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 imagine geospatial and temporal information analytics are going to be vital for the way forward for many industries. What we’re actually targeted on helps our prospects make higher selections, quicker. 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 large gamers with huge sources. 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 information 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 good interview, readers who want to be taught extra ought to go to HEAVY.AI.