The 2024 Machine Learning, AI & Data Landscape (w/ Matt Turck)
Apr 7, 2024
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Matt Turck, the publisher of the MAD Landscape map, discusses the 2024 ecosystem with 2,011 logos. Topics include generative AI impact, modern AI vs data stack, Databricks vs Snowflake, and Microsoft's entry into the data landscape.
The 2024 MAD Landscape showcases over 2,000 logos, reflecting the dynamic growth in data infrastructure and AI-focused companies.
The podcast discusses the evolution of the modern AI stack, highlighting the importance of advanced tools for AI model development and deployment.
Deep dives
Matt Turk's Annual AI Landscape Report for 2024
Matt Turk, a partner at First Mark Capital and an investor in the data and AI landscape, recently released his annual report for 2024, showcasing over 2000 logos in the machine learning, AI, and data space. The report delves into the observed trends for the year, highlighting the significant increase in the number of logos compared to previous editions. Turk's report reflects the dynamic growth and diversification within the industry, emphasizing the continuous expansion in both data infrastructure and AI-focused companies.
Evolution of the AI Stack Layers
The conversation explores the core layers of the modern AI stack, drawing parallels to the evolution seen in the data infrastructure space. Discussions encompass the essential components such as data repositories, ETL processes, vector databases for model storage, and evaluation mechanisms. Addressing the progressive complexity in the AI ecosystem, the narrative emphasizes the emerging importance of advanced tools and frameworks for efficient AI model development and deployment.
Strategic Shifts in Business Intelligence and Analytics
The podcast delves into the challenges and pace of change within the business intelligence and analytics sector, particularly focusing on the slow evolution towards semantic layers. Insights highlight the need for semantic layer integration to redefine metric definitions and streamline query processes. The discussion underscores the importance of balancing comprehensive BI feature sets with a seamless user experience, emphasizing the role of embedded semantic layers for data-powered product innovation.
Future Expansion and Scale in the Semantic Layer Space
As the semantic layer landscape matures, the narrative explores pathways for future growth and scalability. Evaluating potential use cases and infrastructure scalability, the conversation delves into overcoming challenges to establish widespread adoption. The strategic alignment of semantic layers with direct embedded use cases and data-powered products emerges as a key driver for technological advancement and expanded market penetration.
The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, “Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI, ML/AI, and applications.”
Matt and Tristan discuss themes in Matt's post: generative AI’s impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric).
For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
The Analytics Engineering Podcast is sponsored by dbt Labs.
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