CEO Molham Aref discusses the principles and capabilities of Relational AI, an AI co-processor for data warehouses. They explore the challenges of model building and the advantages of embedding AI in Snowflake. The podcast also covers the versatility of intelligent applications, the evolution away from Hadoop, and the historical context of the relational algebra in relational AI.
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Quick takeaways
Relational AI is a co-processor for data warehouses, adding flexibility to representation and analysis of information.
Relational AI enables features like prescriptive analytics and graph analytics on database platforms like Snowflake.
Intelligent applications leverage predictive and prescriptive analytics to assist in decision-making, and Relational AI simplifies the process of building such applications.
Deep dives
Introduction and Background
Mulham RF is interviewed about his background in AI and machine learning, tracing back to the early 90s. He discusses the evolution of AI terminology and its negative connotations, as well as his early experiences with neural networks and computer vision systems.
Building Relational AI
Mulham explains that his interest in AI led him to realize that model building is just one part of deploying intelligent applications. He discusses the complexities of data management, infrastructure, and workflow that need to be addressed. He highlights the need to combine various technologies and programming languages and shares his decades-long quest to simplify the process of going from model creation to real-world deployment.
Relational AI as Co-Processor
Mulham introduces the concept of relational AI as a software co-processor to database platforms like Snowflake. He explains how relational AI complements these platforms by enabling features and workloads that they don't natively support, such as prescriptive analytics, graph analytics, rule-based reasoning, and more. He emphasizes the seamless integration of relational AI within existing platforms, eliminating the need for data synchronization or switching between multiple solutions.
The AI Co-Processor Analogy
Mulham uses the analogy of hardware co-processors to explain the concept of the AI co-processor in relational AI. Just as hardware co-processors offload specific computations to enhance the CPU's performance, the relational AI co-processor enhances database platforms like Snowflake by enabling tasks such as graph analytics, relational machine learning, and rule-based reasoning. He emphasizes how the co-processor is embedded within the existing platform, respects governance policies, and eliminates manual data synchronization.
Intelligent Applications and AI Terminology
Mulham discusses the concept of intelligent applications and remarks on the evolving definitions and connotations surrounding AI. He explains that while all applications have some form of intelligence, intelligent applications are those that leverage predictive and prescriptive analytics to assist in decision-making. He also highlights the importance of simplifying the process of building intelligent applications by reducing the complexity of combining various technologies and programming languages.
Summary Building machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplifying the work involved. In this episode CEO Molham Aref explains how RelationalAI is designed, the capabilities that it adds to your data clouds, and how you can start using it to build more sophisticated applications on your data. Announcements
Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Molham Aref about RelationalAI and the principles behind it for powering intelligent applications
Interview
Introduction
How did you get involved in machine learning?
Can you describe what RelationalAI is and the story behind it?
On your site you call your product an "AI Co-processor". Can you explain what you mean by that phrase?
What are the primary use cases that you address with the RelationalAI product?
What are the types of solutions that teams might build to address those problems in the absence of something like the RelationalAI engine?
Can you describe the system design of RelationalAI?
How have the design and goals of the platform changed since you first started working on it?
For someone who is using RelationalAI to address a business need, what does the onboarding and implementation workflow look like?
What is your design philosophy for identifying the balance between automating the implementation of certain categories of application (e.g. NER) vs. providing building blocks and letting teams assemble them on their own?
What are the data modeling paradigms that teams should be aware of to make the best use of the RKGS platform and Rel language?
What are the aspects of customer education that you find yourself spending the most time on?
What are some of the most under-utilized or misunderstood capabilities of the RelationalAI platform that you think deserve more attention?
What are the most interesting, innovative, or unexpected ways that you have seen the RelationalAI product used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on RelationalAI?
When is RelationalAI the wrong choice?
What do you have planned for the future of RelationalAI?
From your perspective, what is the biggest barrier to adoption of machine learning today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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