Databricks is an enterprise software giant in the making. Most recently valued at $28B in a $1B fundraise announced in February 2021, the company has global ambitions in the data and AI space.
An unlikely story of a company started by seven co-founders, most of whom were academics, built around the Spark open source project, Databricks is heading towards a monster IPO that will accelerate its rivalry with its chief competitor, Snowflake.
I had a chance to interview then co-founder and then CEO Ion Stoica at Data Driven NYC back in 2015, when Databricks was a company very aggressively courted by VCs, but still very early in commercial traction.
It was a real treat to catch up with Ali Ghodsi, who took over as CEO in 2015.
Below is the video and below that, the transcript.
A couple of years ago, FirstMark led the Series A of Text IQ, an impressive AI startup focused on the management of unstructured data in the enterprise for legal, privacy and compliance purposes. The company was co-founded by Apoorv Agarwal (CEO, left) and Omar Haroun (COO, right), and had managed to grow both fast and profitably after raising a seed from our friends at Floodgate.
At its core, Text IQ leverages unsupervised learning to identify sensitive information (privileged documents, PII, PHI, etc.) in large amounts of unstructured data – a challenge that AI is uniquely equipped to solve.
After its Series A, Text IQ continued to make strong progress, building a great team, evolving the product into an enterprise-grade platform and securing an impressive list of Fortune 1000 customers.
Not surprisingly, this attracted the attention not just from potential Series B investors, but also acquirers.
Today, Text IQ is announcing its acquisition by Relativity, a leader in the discovery and compliance market, which just announced a large financing led by Silver Lake.
One of the most exciting emerging areas for AI is content generation. Powered by anything from GANs to GPT-3, a new generation of tools and platforms enables the creation of highly customizable content at scale – whether text, images, audio or video – opening up a broad range of consumer and enterprise use cases.
At FirstMark, we recently announced that we had led the Series A in Synthesia, a startup providing impressive AI synthetic video generation capabilities to both creators and large enterprises.
As a follow up to our investment announcement, we had the pleasure of hosting two of Synthesia’s co-founders, Victor Riparbelli (CEO) and Matthias Niessner (co-founder and a Professor of Computer Vision at Technical University of Munich).
Some of topics we covered:
The rise of Generative Adversarial Networks (GANs) in AI
Use cases for synthetic video in the enterprise
Synthetic videos vs deep fakes
What’s next in the space
Below is the video and below that, the transcript.
MongoDB’s path from unlikely NYC enterprise tech startup to global category leader has been amazing to watch.
I’ve had the pleasure of hosting two of MongoDB’s co-founders over the years, first Dwight Merriman back in 2012 (here) and then CTO Eliot Horowitz in 2016 (here). So it was a real treat this time to get to chat with CEO Dev Ittycheria, who has been leading the company since 2014, and it particular has presided over the company’s remarkable ride in public markets since its 2017 IPO.
In addition to being a truly world-class CEO, Dev has had an outsized impact on the New York tech scene, as he’s been playing a central role both at MongoDB and also at Datadog, where he’s been a long time board member (after leading the company’s Series B back in 2014).
We had a wide-ranging conversation where we covered:
Dev’s journey as a CEO and investor
The evolution of enterprise tech in New York
MongoDB’s database as a service offering, Atlas
Newest products and product roadmap
Open source
GTM strategies, bottoms up vs top down
Lessons in scaling the team
Being a student of the game rather than a master of the game
A few weeks ago, my colleague John Wu and I introduced the MAD Index, a new public market index to track the progress of “pure play” machine learning, AI and data public companies. This was an initial group of 13 companies, which has since then increased to 14, following the UiPath IPO.
Today, we’re introducing the Emerging MAD Index, a companion to the public MAD index. The idea is to track a group of private companies that show high potential to join the MAD Index in the future.
Criteria
Just like the Public MAD Index, our goal is to capture “pure play” machine learning, AI and data companies.
In practice, that generally means infrastructure companies offering tools to store, process and analyze data, create and manage machine learning models, and/or automate core processes deep in the stack – broadly horizontal companies serving a variety of business needs across departments, industries and geographies.
Dataiku (in which I’m a proud investor and board member) has had an impressive ride over the last few years. An early entrant in the enterprise Data Science and Machine Learning platform category, the company successfully expanded from its French/European roots to build a very strong presence in the US (where it is company is now headquartered) and, increasingly, Asia.
Along the way, Dataiku:
became a unicorn, most recently raising a $100M Series D in 2020
was named a “Leader” in Gartner’s Magic Quadrant for Data Science and ML Platforms in both 2020 and 2021
collected many accolades, such as CB Insight’s “AI 100” and several of Forbes lists: “Cloud 100”, “AI 50” and “America’s best startup employers in 2021”
It was really fun to host CEO Florian Douetteau at Data Driven NYC once again, after previous appearances in 2016 (here) and 2018 (here). We covered a bunch of different topics, including:
What enterprise AI is about: not flying cars, but optimizing hundreds of business processes
Why enterprises need to move past their fear of data and AI
The key principles behind the design of the Dataiku platform: handling the entire data lifecycle, and democratizing data/AI across teams
Dataiku’s partnership with Snowflake
The upcoming launch of their starter / SMB self-serve product, Dataiku Online
Below is the video and below that, the transcript.
At our most recent Data Driven NYC, we had the great pleasure of hosting Bindu Reddy, CEO and co-founder Abacus AI, and formerly GM & creator of AI verticals at AWS, and an ex-Googler. Bindu also has a very witty and entertaining Twitter account (@bindureddy), where she talks about all things machine learning and AI.
This was a very educational and approachable conversation, where we covered:
some key definitions: neural networks, weights and biases, supervised vs unsupervised learning, feature store
Applying neural networks to structured, tabular data
Abacus’ vision around “autonomous AI”
How companies wait too long to start experimenting in ML/AI
Below is the video and below that, the transcript.
We all have insatiable appetite for video, both in our personal and professional lives. Time and again, video is shown to capture our attention better than any other medium. This is increasingly how we learn, explore, collaborate and get entertained.
However, especially in an enterprise context, creating professional-quality video remains a complex and costly endeavor. For all the capabilities of smartphones, most companies still need studio-level equipment to produce enterprise-grade videos: cameras, sound equipment, actors, post-production editing. The process is time-consuming, and not very scalable. Shooting a video in multiple languages, for example, requires multiple actors or dubbing, Any update requires everyone to go back to the studio.
But what if video could be just… code? What if it could be infinitely flexible and customizable at scale, as simple as an API call?
Today we’re excited to announce that FirstMark led a $12.5M Series A investment in Synthesia – a fast-growing startup that offers exactly that.
Synthesia makes creating a business video as simple as writing an email or putting together a powerpoint presentation – a compelling “text to video” experience.
Today, we are previewing a new public market index – the MAD (for machine learning, AI and data) index.
Readers of this blog know that we have been tracking the data ecosystem since 2012, through annual landscapes (see the 2020 Data & AI Landscape).
Over the last few years, a funny thing happened – some of the small startups we had started tracking grew up, did an IPO and became large public companies.
Not so long ago, public market investors used to say there’s was no good way of “playing” the Big Data and AI trends, due to the lack of public companies in the space. This is less true today.
However, there isn’t much out there in terms of looking at those public companies as a group. For example, see this Seeking Alpha piece, Top 3 Artificial Intelligence ETFs To Consider, where none of the companies listed are actually AI companies.
Hence the idea of the MAD Index. It’s still a small group of companies, but my colleague John Wu and I were curious to see how they fared in public markets, now and going forward.
This is just a start. We anticipate that a number of companies will join this group in the next year or two, and we’re excited to see how this index matures.
Pinterest is near and dear to our hearts at FirstMark because we had the good fortune of being the first institutional investor back in 2009 when the company was just getting started (fun fact: the founders were in New York for a brief moment in time before moving to the Bay Area). Pinterest has had a remarkable ride ever since, and it’s a $49B market cap public company at the time of writing.
So it was a particular pleasure to welcome Dave Burgess, Head of Data Engineering, to come and talk to the Data Driven NYC audience about all things data at Pinterest.
We covered a bunch of interesting topics, including:
Pinterest’s newly open sourced project, QueryBook
The stack Pinterest uses to manage is 400 petabytes of data
The use cases for data analytics and machine learning at Pinterest
Below is the video and below that, the transcript.
Real-time data streaming is an increasingly crucial part of the data ecosystem. While financial services (trading) initially represented the bulk of the demand for streaming, the emergence of more mature technology in the space has unlocked more use cases, which in turn created more demand for better technology.
At a recent Data Driven NYC, we had a very interesting conversation with Arjun Narayan, CEO of Materialize, “the only true SQL streaming database for building internal tools, interactive dashboards, and customer-facing experiences”. Materialize is headquartered in New York and has raised $40M in venture capital money (with a new round rumored to be announced soon, at the time of writing).
This was a very educational discussion, where we covered the following topics:
What is streaming? What is Kakfa?
Why is there a need for a streaming database for analytics?
Why is SQL underrated?
What is Materialize?
Partnering with DBT to make streaming ubiquitous
Materializes’s roadmap
Below is the video and below that, the transcript.
At our most recent Data Driven, we had the great pleasure of hosting Chip Huyen, a writer and computer scientist who also teaches machine learning design at Stanford, for a fascinating and fun conversation.
We covered a range of topics, including:
What is machine learning design?
The MLOps landscape, and how it’s both overdeveloped and under-developed
What is online machine learning?
The divergence between East and West for machine learning and data infrastructure
A couple of book recommendations
Below is the video and below that, the transcript.
While it’s been around for 15+ years, Reddit has been on a tear lately: a $367M Series E round announced a few weeks ago, rumors of an IPO, and plenty of Internet action with r/wallstreetbets in particular.
Interestingly, there was a major gap for many years between the central role Reddit has been playing on the Internet and its relatively small team size. While companies like Facebook are largely AI companies (see our conversation with Jerome Pesenti, Head of AI, Facebook), Reddit’s data team was tiny.
Enter Jack Hanlon, VP Data at Reddit and our guest at our most recent Data Driven NYC event. Jack has been tasked with leading the data team into rapid growth, and we had a really interesting conversations, in particular around the following points:
How is the data team at Reddit organized? (preview: data science, data platform, machine learning, search)
What’s the data stack? (preview: switch from AWS to GCP, Kafka, Airflow, Colab, Amundsen, Great Expectations, Druid/Imply…)
What are the key use cases for data science and machine learning at Reddit?
A book recommendation: “Invisible Women: Data Bias in a World Designed for Men”
Anecdotally, Jack is our second speaker in recent memory who was a regular attendee in the early years of Data Driven NYC, before ascending to leadership responsibilities in a major Internet company! (the other being Alok Gupta, who spoke about leading data at DoorDash).
Below is the video and below that, the transcript.
In just a few years of hyper growth, Snyk has become a $2.7B unicorn, most recently raising $200M in September 2020. A developer-first security company, it has also helped usher the “DevSecOps” category.
At our most recent Data Driven NYC, we had the pleasure of hosting its Founder & President, Guy Podjarny, zooming in late at night from Israel.
We covered many interesting topics, including:
What does DevSecOps mean?
How did Snyk initially get developers to care, and how did they expand horizontally from there?
What is infrastructure as code?
Thoughts Snyk Code and Snyk’s vulnerability database
The nuances of combining a bottoms-up, freemium motion focused on developers, with an enterprise motion focused on economic buyers of Snyk’s products.
Below is the video and below that, the transcript.
If you follow the various talks at Data Driven NYC, and the data ecosystem on general, it’s plenty apparent that the overall tooling for data, data science and machine learning is still in its infancy, particularly compared to the software stack.
While this may feel ironic (yes, I really do think) given the billions in venture capital money that have been poured in the space, it’s worth remembering that the data stack (at least in its “big data” phase) is relatively recent (10-15 years), while the software stack has had several decades of evolution.
In many organizations, the data science and machine learning stack looks a collection of various tools, some open source, some proprietary, glued together with one-off scripts. Teams started experimenting with one tool, then another, then created ad hoc pathways to make it all work together over time, and before you knew it, you ended up with complex environments that are painful to manage.
In response to this situation, various machine learning frameworks have emerged to make abstract away the complexity. Several of those frameworks were developed internally at large tech comapanies to solve their own problems, and then open sourced.
Kedro is one such example. It was developed and maintained by QuantumBlack, an analytics consultancy acquired by McKinsey in 2015. It’s McKinsey’s first open-source product.
Kedro is somewhat hard to categorize. If it had its own category, it might be considered a Machine Learning Engineering Framework. What React did for front-end engineering code is what Kedro does for machine learning code. It allows you to build “design systems” of reusable machine learning code.
At our most recent Data Driven NYC, we had the great pleasure of hosting Yetunde Dada, a Principal Product Manager at QuantumBlack, who has been the key driving force behind Kedro.
Below is the video and below that, the transcript.
