machine learning
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Can artificial intelligence give elephants a winning edge?
Images of elephants roaming the African plains are imprinted on all of our minds and something easily recognized as a symbol of Africa. But the future of elephants today is uncertain. An elephant is currently being killed by poachers every 15 minutes, and humans, who love watching them so much, have declared war on their species. Most people are not poachers, ivory collectors or intentionally harming wildlife, but silence or indifference to the battle at hand is as deadly.
You can choose to read this article, feel bad for a moment and then move on to your next email and start your day.
Or, perhaps you will pause and think: Our opportunities to help save wildlife, especially elephants, are right in front of us and grow every day. And some of these opportunities are rooted in machine learning (ML) and the magical outcome we fondly call AI.
Image Credits: Jes Lefcourt (opens in a new window)
Open-source developers are giving elephants a neural edge
Six months ago, amid a COVID-infused world, Hackster.io, a large open-source community owned by Avnet, and Smart Parks, a Dutch-based organization focused on wildlife conservation, reached out to tech industry leaders, including Microsoft, u-blox and Taoglas, Nordic Semiconductors, Western Digital and Edge Impulse with an idea to fund the R&D, manufacturing and shipping of 10 of the most advanced elephant tracking collars ever built.
These modern tracking collars are designed to deploy advanced machine-learning (ML) algorithms with the most extended battery life ever delivered for similar devices and a networking range more expansive than ever seen before. To make this vision even more audacious, they called to fully open-source and freely share the outcome of this effort via OpenCollar.io, a conservation organization championing open-source tracking collar hardware and software for environmental and wildlife monitoring projects.
Our opportunities to help save wildlife — especially elephants — are right in front of us and grow every day.
The tracker, ElephantEdge, would be built by specialist engineering firm Irnas, with the Hackster community coming together to make fully deployable ML models by Edge Impulse and telemetry dashboards by Avnet that will run the newly built hardware. Such an ambitious project was never attempted before, and many doubted that such a collaborative and innovative project could be pulled off.
Creating the world’s best elephant-tracking device
Only they pulled it off. Brilliantly. The new ElephantEdge tracker is considered the most advanced of its kind, with eight years of battery life and hundreds of miles worth of LoRaWAN networking repeaters range, running TinyML models that will provide park rangers with a better understanding of elephant acoustics, motion, location, environmental anomalies and more. The tracker can communicate with an array of sensors, connected by LoRaWAN technology to park rangers’ phones and laptops.
This gives rangers a more accurate image and location to track than earlier systems that captured and reported on pictures of all wildlife, which ran down the trackers’ battery life. The advanced ML software that runs on these trackers is built explicitly for elephants and developed by the Hackster.io community in a public design challenge.
“Elephants are the gardeners of the ecosystems as their roaming in itself creates space for other species to thrive. Our ElephantEdge project brings in people from all over the world to create the best technology vital for the survival of these gentle giants. Every day they are threatened by habitat destruction and poaching. This innovation and partnerships allow us to gain more insight into their behavior so we can improve protection,” said Smart Parks co-founder Tim van Dam.
Image Credits: Jes Lefcourt (opens in a new window)
Open-source, community-powered, conservation-AI at work
With hardware built by Irnas and Smart Parks, the community was busy building the algorithms to make it sing. Software developer and data scientist Swapnil Verma and Mausam Jain in the U.K. and Japan created Elephant AI. Using Edge Impulse, the team developed two ML models that will tap the tracker’s onboard sensors and provide critical information for park rangers.
The first community-led project, called Human Presence Detection, will alert park rangers of poaching risk using audio sampling to detect human presence in areas where humans are not supposed to be. This algorithm uses audio sensors to record sound and sight while sending it over the LoRaWAN network directly to a ranger’s phone to create an immediate alert.
The second model they named “Elephant Activity Monitoring.” It detects general elephant activity, taking time-series input from the tracker’s accelerometer to spot and make sense of running, sleeping and grazing to provide conservation specialists with the critical information they need to protect the elephants.
Another brilliant community development came from the other side of the world. Sara Olsson, a Swedish software engineer who has a passion for the national world, created a TinyML and IoT monitoring dashboard to help park rangers with conservation efforts.
With little resources and support, Sara built a full telemetry dashboard combined with ML algorithms to monitor camera traps and watering holes, while reducing network traffic by processing data on the collar and considerably saving battery life. To validate her hypothesis, she used 1,155 data models and 311 tests!
Sara Olsson’s TinyML and IoT monitoring dashboard. Image Credits: Sara Olsson
She completed her work in the Edge Impulse studio, creating the models and testing them with camera traps streams from Africam using an OpenMV camera from her home’s comfort.
Image Credits: Sara Olsson (opens in a new window)
Technology for good works, but human behavior must change
Project ElephantEdge is an example of how commercial and public interest can converge and result in a collaborative sustainability effort to advance wildlife conservation efforts. The new collar can generate critical data and equip park rangers with better data to make urgent life-saving decisions about protecting their territories. By the end of 2021, at least ten elephants will be sporting the new collars in selected parks across Africa, in partnership with the World Wildlife Fund and Vulcan’s EarthRanger, unleashing a new wave of conservation, learning and defending.
Naturally, this is great, the technology works, and it’s helping elephants like never before. But in reality, the root cause of the problem runs much more profound. Humans must change their relationship to the natural world for proper elephant habitat and population revival to occur.
“The threat to elephants is greater than it’s ever been,” said Richard Leakey, a leading palaeoanthropologist and conservationist scholar. The main argument for allowing trophy or ivory hunting is that it raises money for conservation and local communities. However, a recent report revealed that only 3% of Africa’s hunting revenue trickles down to communities in hunting areas. Animals don’t need to die to make money for the communities you live around.
With great technology, collaboration and a commitment to address the underlying cultural conditions and the ivory trade that leads to most elephant deaths, there’s a real chance to save these singular creatures.
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Why is GoCardless COO Carlos Gonzalez-Cadenas pivoting to become a full-time VC?
Index Ventures, a London- and San Francisco-headquartered venture capital firm that primarily invests in Europe and the U.S., recently announced its latest partner. Carlos Gonzalez-Cadenas, currently COO of London-based fintech GoCardless and previously the chief product officer of Skyscanner, will join Index in January.
Gonzalez-Cadenas is a seasoned entrepreneur and operator, but has also become a prolific angel investor in the U.K. and Europe over the last three years, making more than 50 angel investments in total. Well-regarded by founders and co-investors, his transition to a full-time role in venture capital feels like quite a natural one.
Earlier this week, TechCrunch caught up with Gonzalez-Cadenas over Zoom to learn more about his new role at Index and how he intends to source deals and support founders. Index’s latest hire also shared his insights on Europe’s venture market, describing this era as the “best moment in entrepreneurship in Europe.”
TechCrunch: Let me start by asking, why do you want to become a VC? You’re obviously a well-established entrepreneur and operator, are you sure venture capital is the career for you?
Carlos Gonzalez-Cadenas: I’ve been an angel investor for the last three years and this is something that has basically grown for me quite organically. I started doing just a handful and seeing if this is something I like and over time it has grown quite a lot and so has the number of entrepreneurs I’m partnered with. And this is something I’ve been increasingly more excited to do. So it has grown organically and something that emotionally has been getting closer and closer as time has passed.
And the things I like more specifically are: One, I’m quite a curious person, and for me, investing gives you the possibility of learning a lot about different sectors, about different entrepreneurs, different ways of building businesses, and that is something that I enjoy a lot.
The second bit is that I care a lot about helping entrepreneurs, especially the next generation of entrepreneurs, build great businesses in Europe. I’ve been very lucky, in the past, to learn from great people, like Gareth [Williams, Skyscanner co-founder] and Hiroki [Takeuchi. CEO at GoCardless], in my journey. I feel a duty of helping the next generation of entrepreneurs and sharing all the things that I’ve learnt. I care a lot about setting up founders as much as possible for success and sharing all those experiences I’ve learned [from].
These are the key two motivations that have led me to decide that it would be a great time now to move to the investing side.
How have you managed your deal flow while having a full-time job and where is that deal flow coming from?
It is typically coming in three buckets. A part of it is coming from my entrepreneur and operator network. So there are entrepreneurs and operators I know that are referring other entrepreneurs to me. Another bucket is other investors that I typically co-invest with. Another bucket is venture capitalists. I basically tend to invest quite a lot with VCs and in some cases they are referring deals to me.
In terms of managing it alongside GoCardless, it takes quite a lot of effort. It requires a lot of dedication and time invested during evenings and weekends.
The good thing is that my network typically tends to send me quite highly curated deals so essentially the deal flow I have luckily tends to be quite high quality, which makes things a bit more manageable. But don’t get me wrong, it still takes quite a lot of effort even if the deal flow is relatively high quality.
Presumably you haven’t been able to be all that hands-on as an angel investor, so how are you going to make that transition and what is it that you think you bring with the operational side to venture?
The way I think about this is, the entrepreneurs I typically invest in and their companies tend to be quite capable in their day-to-day perspective. Where they tend to find more value in interactions with me is what I call the “moments of truth.” Those key decisions, those key points in the journey where essentially it can influence the trajectory of the business in a fundamental way. It could be things like, I am fundraising and I don’t know how to position the business. Or I’m thinking about my strategy for the next 18 months and I will basically welcome an experienced person giving me a qualified opinion.
Or I have a big people problem and I don’t know how to solve that problem and I need that third person who has been in my shoes before. Or it could be that I’m thinking about how to organize my team as I move from startup to scale-up and I need help from someone who has scaled teams before. Or could be that I’m hiring three executives and I don’t know what a great CMO looks like. It’s those high-impact, high-leverage questions that the entrepreneurs tend to find helpful engaging with me, as opposed to very detailed day-to-day things that most of the entrepreneurs I work with tend to be quite capable of doing. And so far that model is working. The other thing is that the model is quite scalable because you are engaging 2-3 times per year but those times are high quality and highly impactful for the entrepreneur.
I typically also tend to have pretty regular and frequent communication with entrepreneurs on Slack. It’s more like quick questions that can be solved, and I tend to get quite a lot of that. So I think it’s that bimodel approach of high-frequency questions that we can solve by asynchronous means or high-impact moments a few times per year where, essentially, we need to sit down and we need to think together deeply about the problem.
And I tend to do nothing in the middle, where essentially, it’s stuff that is not so impactful but takes a huge amount of time for everyone, that doesn’t tend to be the most effective way of helping entrepreneurs. Obviously, I’m guided by what entrepreneurs want from perspective, so I’m always training the models in response to what they need.
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FireEye acquires Respond Software for $186M, announces $400M investment
The security sector is ever frothy and acquisitive. Just last week Palo Alto Networks grabbed Expanse for $800 million. Today it was FireEye’s turn, snagging Respond Software, a company that helps customers investigate and understand security incidents, while reducing the need for highly trained (and scarce) security analysts. The deal has closed, according to the company.
FireEye had its eye on Respond’s Analyst product, which it plans to fold into its Mandiant Solutions platform. Like many companies today, FireEye is focused on using machine learning to help bolster its solutions and bring a level of automation to sorting through the data, finding real issues and weeding out false positives. The acquisition gives them a quick influx of machine learning-fueled software.
FireEye sees a product that can help add speed to its existing tooling. “With Mandiant’s position on the front lines, we know what to look for in an attack, and Respond’s cloud-based machine learning productizes our expertise to deliver faster outcomes and protect more customers,” Kevin Mandia, FireEye CEO said in a statement announcing the deal.
Mike Armistead, CEO at Respond, wrote in a company blog post that today’s acquisition marks the end of a four-year journey for the startup, but it believes it has landed in a good home with FireEye. “We are proud to announce that after many months of discussion, we are becoming part of the Mandiant Solutions portfolio, a solution organization inside FireEye,” Armistead wrote.
While FireEye was at it, it also announced a $400 million investment from Blackstone Tactical Opportunities fund and ClearSky (an investor in Respond), giving the public company a new influx of cash to make additional moves like the acquisition it made today.
It didn’t come cheap. “Under the terms of its investment, Blackstone and ClearSky will purchase $400 million in shares of a newly designated 4.5% Series A Convertible Preferred Stock of FireEye (the ‘Series A Preferred’), with a purchase price of $1,000 per share. The Series A Preferred will be convertible into shares of FireEye’s common stock at a conversion price of $18.00 per share,” the company explained in a statement. The stock closed at $14.24 today.
Respond, which was founded in 2016, raised $32 million, including a $12 million Series A in 2017 led by CRV and Foundation Capital and a $20 million Series B led by ClearSky last year, according to Crunchbase data.
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Abacus.AI raises another $22M and launches new AI modules
AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.
Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI
In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.
“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.
Image Credits: Abacus.AI
Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.
Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.
As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.
Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.
Image Credits: Abacus.AI
It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.
Come January, the company also plans to launch support for more languages and more machine vision use cases.
“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”
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AI-tool maker Seldon raises £7.1M Series A from AlbionVC and Cambridge Innovation Capital
Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize machine learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting does actually end up working.
It has now raised a £7.1 million Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1 million funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy — a new enterprise solution — to market and double the size of the team over the next 18 months.
More accurately, Seldon is a cloud-agnostic machine learning (ML) deployment specialist which works in partnership with industry leaders such as Google, Red Hat, IBM and Amazon Web Services.
Key to its success is that its open-source project Seldon Core has more than 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.
Alex Housley, CEO and founder of Seldon speaking to TechCrunch explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”
“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.
In addition, he said Seldon’s open-source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”
Nadine Torbey, an investor at AlbionVC, added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”
Vin Lingathoti, partner at Cambridge Innovation Capital, said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring machine learning models at scale.”
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Arrikto raises $10M for its MLOps platform
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
Image Credits: Arrikto
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”
Image Credits: Arrikto
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Computer vision startup Chooch.ai scores $20M Series A
Chooch.ai, a startup that hopes to bring computer vision more broadly to companies to help them identify and tag elements at high speed, announced a $20 million Series A today.
Vickers Venture Partners led the round with participation from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and several other unnamed investors. Today’s investment brings the total raised to $25.8 million, according to the company.
“Basically we set out to copy human visual intelligence in machines. That’s really what this whole journey is about,” CEO and co-founder Emrah Gultekin explained. As the company describes it, “Chooch Al can rapidly ingest and process visual data from any spectrum, generating AI models in hours that can detect objects, actions, processes, coordinates, states, and more.”
Chooch is trying to differentiate itself from other AI startups by taking a broader approach that could work in any setting, rather than concentrating on specific vertical applications. Using the pandemic as an example, Gultekin says you could use his company’s software to identify everyone who is not wearing a mask in the building or everyone who is not wearing a hard hat at a construction site.
With 22 employees spread across the U.S., India and Turkey, Chooch is building a diverse company just by virtue of its geography, but as it doubles the workforce in the coming year, it wants to continue to build on that.
“We’re immigrants. We’ve been through a lot of different things, and we recognize some of the issues and are very sensitive to them. One of our senior members is a person of color and we are very cognizant of the fact that we need to develop that part of our company,” he said. At a recent company meeting, he said that they were discussing how to build diversity into the policies and values of the company as they move forward.
The company currently has 18 enterprise clients and hopes to use the money to add engineers, data scientists and begin to build out a worldwide sales team to continue to build the product and expand its go-to-market effort.
Gultekin says that the company’s unusual name comes from a mix of the words choose and search. He says that it is also an old Italian insult. “It means dummy or idiot, which is what artificial intelligence is today. It’s a poor reflection of humanity or human intelligence in humans,” he said. His startup aims to change that.
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Which emerging technologies are enterprise companies getting serious about in 2020?
Startups need to live in the future. They create roadmaps, build products and continually upgrade them with an eye on next year — or even a few years out.
Big companies, often the target customers for startups, live in a much more near-term world. They buy technologies that can solve problems they know about today, rather than those they may face a couple bends down the road. In other words, they’re driving a Dodge, and most tech entrepreneurs are driving a DeLorean equipped with a flux-capacitor.
That situation can lead to a huge waste of time for startups that want to sell to enterprise customers: a business development black hole. Startups are talking about technology shifts and customer demands that the executives inside the large company — even if they have “innovation,” “IT,” or “emerging technology” in their titles — just don’t see as an urgent priority yet, or can’t sell to their colleagues.
How do you avoid the aforementioned black hole? Some recent research that my company, Innovation Leader, conducted in collaboration with KPMG LLP, suggests a constructive approach.
Rather than asking large companies about which technologies they were experimenting with, we created four buckets, based on what you might call “commitment level.” (Our survey had 211 respondents, 62% of them in North America and 59% at companies with greater than $1 billion in annual revenue.) We asked survey respondents to assess a list of 16 technologies, from advanced analytics to quantum computing, and put each one into one of these four buckets. We conducted the survey at the tail end of Q3 2020.
Respondents in the first group were “not exploring or investing” — in other words, “we don’t care about this right now.” The top technology there was quantum computing.
Bucket #2 was the second-lowest commitment level: “learning and exploring.” At this stage, a startup gets to educate its prospective corporate customer about an emerging technology — but nabbing a purchase commitment is still quite a few exits down the highway. It can be constructive to begin building relationships when a company is at this stage, but your sales staff shouldn’t start calculating their commissions just yet.
Here are the top five things that fell into the “learning and exploring” cohort, in ranked order:
- Blockchain.
- Augmented reality/mixed reality.
- Virtual reality.
- AI/machine learning.
- Wearable devices.
Technologies in the third group, “investing or piloting,” may represent the sweet spot for startups. At this stage, the corporate customer has already discovered some internal problem or use case that the technology might address. They may have shaken loose some early funding. They may have departments internally, or test sites externally, where they know they can conduct pilots. Often, they’re assessing what established tech vendors like Microsoft, Oracle and Cisco can provide — and they may find their solutions wanting.
Here’s what our survey respondents put into the “investing or piloting” bucket, in ranked order:
- Advanced analytics.
- AI/machine learning.
- Collaboration tools and software.
- Cloud infrastructure and services.
- Internet of things/new sensors.
By the time a technology is placed into the fourth category, which we dubbed “in-market or accelerating investment,” it may be too late for a startup to find a foothold. There’s already a clear understanding of at least some of the use cases or problems that need solving, and return-on-investment metrics have been established. But some providers have already been chosen, based on successful pilots and you may need to dislodge someone that the enterprise is already working with. It can happen, but the headwinds are strong.
Here’s what the survey respondents placed into the “in-market or accelerating investment” bucket, in ranked order:
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Databricks launches SQL Analytics
AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.
SQL Analytics will be available in public preview on November 18.
In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.
Image Credits: Databricks
“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.
“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”
The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.
The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.
While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and ThoughtSpot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.
Image Credits: Databricks
“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”
In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.
While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.
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Qualcomm Ventures invests in four 5G startups
Qualcomm Ventures, Qualcomm’s investment arm, today announced four new strategic investments in 5G-related startups. These companies are private mobile network specialist Celona, mobile network automation platform Cellwize, the edge computing platform Azion and Pensando, another edge computing platform that combines its software stack with custom hardware.
The overall goal here is obviously to help jumpstart 5G use cases in the enterprise and — by extension — for consumers by investing in a wide range of companies that can build the necessary infrastructure to enable these.
“We invest globally in the wireless mobile ecosystem, with a goal of expanding our base of customers and partners — and one of the areas we’re particularly excited about is the area of 5G,” Quinn Li, a senior VP at Qualcomm and the global head of Qualcomm Ventures, told me. “Within 5G, there are three buckets of areas we look to invest in: one is in use cases, second is in network transformation, third is applying 5G technology in enterprises.”
So far, Qualcomm Ventures has invested more than $170 million in the 5G ecosystem, including this new batch. The firm did not disclose how much it invested in these four new startups, though.
Overall, this new set of companies touches upon the core areas Qualcomm Ventures is looking at, Li explained. Celona, for example, aims to make it as easy for enterprises to deploy private cellular infrastructure as it is to deploy Wi-Fi today.
“They built this platform with a cloud-based controller that leverages the available spectrum — CBRS — to be able to take the cellular technology, whether it’s LTE or 5G, into enterprises,” Li explained. “And then these enterprise use cases could be in manufacturing settings, could be in schools, could be in hospitals, or it could be on campus for universities.”
Cellwize, meanwhile, helps automate wireless networks to make them more flexible and manageable, in part by using machine learning to tune the network based on the data it collects. One of the main investment theses for this fund, Li told me, is that wireless technology will become increasingly software-defined, and Cellwize fits right into this trend. The potential customer here isn’t necessarily an individual enterprise, though, but wireless and mobile operators.
Edge computing, where Azion and Pensando play, is obviously also a hot category right now, and one where 5G has some obvious advantages, so it’s maybe no surprise that Qualcomm Ventures is putting a bit of a focus on these today with its investments in Azion and Pensando.
“As we move forward, [you will] see a lot of the compute moving from the cloud into the edge of the network, which allows for processing happening at the edge of the network, which allows for low latency applications to run much faster and much more efficiently,” Li said.
In total, Qualcomm Ventures has deployed $1.5 billion and made 360 investments since its launch in 2000. Some of the more successful companies the firm has invested in include unicorns like Zoom, Cloudflare, Xiaomi, Cruise Automation and Fitbit.
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