In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.

Links
FizzBee
FizzBee Github Repo
FizzBee Blog

Chapters
00:00 Introduction and Overview
02:42 JP's Experience at Google and the Growth of the Company
04:51 The Difference Between Engineers and Coders
06:41 The Importance of Rigor and Quality in Engineering
10:08 The Limitations of QA and the Need for Formal Methods
14:00 The Role of Best Practices in Software Engineering
14:56 Design Specification Languages for System Correctness
21:43 The Applicability of Formal Methods in Distributed Systems
31:20 Getting Started with FizzBee: A Practical Example
36:06 Common Assumptions and Misconceptions in Distributed Systems
43:23 The Role of FizzBee in the Design Phase
48:04 The Future of FizzBee: LLMs and Code Generation
58:20 Getting Started with FizzBee: Tutorials and Online Playground

Click here to view the episode transcript.

In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.

Links

DagsHub
The MLOps Podcast
Dean on LI

Chapters

00:00 Introduction and Background
03:03 Challenges of Managing Machine Learning Projects
10:00 The Concept of Experiments in Machine Learning
12:51 Data Curation and Validation for High-Quality Data
27:07 Connecting the Components of Machine Learning Projects with DAGS Hub
29:12 The Importance of Data and Clear Interfaces
43:29 Incorporating Machine Learning into DAGsHub
51:27 The Future of ML and AI

In this episode, Kostas and Nitay are joined by Amey Chaugule and Matt Green, co-founders of Denormalized. They delve into how Denormalized is building an embedded stream processing engine—think “DuckDB for streaming”—to simplify real-time data workloads. Drawing from their extensive backgrounds at companies like Uber, Lyft, Stripe, and Coinbase. Amey and Matt discuss the challenges of existing stream processing systems like Spark, Flink, and Kafka. They explain how their approach leverages Apache DataFusion, to create a single-node solution that reduces the complexities inherent in distributed systems.

The conversation explores topics such as developer experience, fault tolerance, state management, and the future of stream processing interfaces. Whether you’re a data engineer, application developer, or simply interested in the evolution of real-time data infrastructure, this episode offers valuable insights into making stream processing more accessible and efficient.

Contacts & Links
Amey Chaugule
Matt Green
Denormalized
Denormalized Github Repo

Chapters
00:00 Introduction and Background
12:03 Building an Embedded Stream Processing Engine
18:39 The Need for Stream Processing in the Current Landscape
22:45 Interfaces for Interacting with Stream Processing Systems
26:58 The Target Persona for Stream Processing Systems
31:23 Simplifying Stream Processing Workloads and State Management
34:50 State and Buffer Management
37:03 Distributed Computing vs. Single-Node Systems
42:28 Cost Savings with Single-Node Systems
47:04 The Power and Extensibility of Data Fusion
55:26 Integrating Data Store with Data Fusion
57:02 The Future of Streaming Systems
01:00:18 intro-outro-fade.mp3

Click here to view the episode transcript.