AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, announced that registration is now open for its second Semantic Layer Summit. This unique event brings together today's top minds in the data, analytics, artificial intelligence (AI), and business intelligence (BI) industries to discuss the evolution of the semantic layer technology category. "The Semantic Layer is a critical, but often poorly understood, component of the rapidly evolving modern data stack" "The Semantic Layer is a critical, but often poorly understood, component of the rapidly evolving modern data stack," said David Mariani, co-founder and CTO of AtScale and conference chair for the Semantic Layer Summit. "We are thrilled to be bringing together a cross-section of industry practitioners and technology visionaries to share perspectives and give practical advice." The inaugural Semantic Layer Summit was held in 2022, drawing over 8,000 registrants and featuring speakers from across the industry.

London-based AI startup, Instill AI, has announced to have raised $3.6 million in a seed round to derive insights from unstructured data, such as images, videos, audio and text. The investment was led by London-based venture capital firm RTP Global. The round is also supported by Lunar Ventures (which also backed iLoF). Further, it also got backing from returning investor Hive Ventures; Charles Songhurst, former corporate strategy executive and M&A execute at Microsoft; Demetrios Kellari, Head of Systems and Technology Integration at Cavnue; and Mehdi Ghissassi, Director of Product for Google's AI/ML Research org. A pre-seed round of funding was provided by Cornerstone Ventures to Instill AI.

A lot has already been said about the modern data stack (MDS) but the situation is significantly more scattered on the machine learning side of the fence: once data is properly transformed, how is it consumed downstream to produce business value? This post is intended for anybody wanting to bridge the gap between working with data and actually delivering business value using machine learning. The modern data stack (MDS) has been consolidated as a series of best practices around data collection, storage and transformation. A lot has been said already about the MDS as such, but the situation is more "scattered" on the other side of the fence: once data is properly transformed, how is that consumed downstream to produce business value? At the end of the day, ingesting and transforming data is not (for most companies) an end in itself: while tech giants figured out a while ago how to "get models in production", most companies still struggle to productionize a model in less than 3 months.

Atlan, the active metadata platform for modern data teams, announced that it has launched a new version of its product built around active metadata. With this, Atlan is pioneering a new idea of data discovery, cataloging, and governance while transforming the role that metadata can play in the modern data stack. "At Atlan, we've always been committed to helping data teams work together better," said Prukalpa Sankar, Co-Founder of Atlan. "We started by building a great collaboration hub for data teams, using metadata to build trust and democratize data. For the last year, we've been working to take our product to a whole new level. We're so excited to share it with the world and help data teams everywhere achieve their dream of an intelligent data stack."

Data AI Summit, the world's largest conference for the open modern data stack community, will bring together tens of thousands of data practitioners in person and online from June 27-30, 2022. All presenters will be expected to speak live from San Francisco, and your talks will be available online and promoted on our channels. We invite you to share your expertise and stories with fellow data scientists, data engineers, data analysts and data leaders. Your solutions using the modern data stack are defined by open technologies that help deliver advanced data analytics, build data pipelines, and develop AI applications and machine learning models. Your experience solving these problems will be extremely valuable to your peers, whether you're using technologies like Apache Spark, Delta Lake and the lakehouse pattern, MLflow, TensorFlow, PyTorch, Scikit-learn, BI and SQL analytics, deep learning or machine learning frameworks.

Amplify Partners invests in early-stage companies pioneering novel applications in machine intelligence and computer science.

Continual brings together second time founders Tristan Zajonc and Tyler Kohn who previously built and sold machine learning infrastructure startups.

It's been a hot, hot year in the world of data, machine learning and AI. Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the last year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II) and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym – Machine learning, Artificial intelligence and Data (MAD) – this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone – a macro view that will hopefully be interesting and approachable to most; and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with deeper familiarity with the industry. This (long!) post is organized as follows: Let's start with the high level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: why is this happening?

Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?

Modern Data Stack (MDS) is a stack of technologies that makes a modern data warehouse perform 10–10,000x better than a legacy data warehouse. Ultimately, an MDS saves time, money, and effort. The four pillars of an MDS are a data connector, a cloud data warehouse, a data transformer, and a BI & data exploration tool. Easy integration is made possible with managed and open-source tools that pre-build hundreds of ready-to-use connectors. What used to take a team of data engineers to build and maintain regularly can now be replaced with a tool for simple use cases.