Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable technical challenges. In this talk, we will share some common pitfalls, lessons learned, and engineering practices, faced while building customer-facing enterprise ML products. In particular, we will focus on the engineering that delivers real-time audience insights everyday to thousands of marketers via the Helixa’s market research platform.
During the talk you will learn:
- An overview of the Helixa ML end-to-end system
- Useful engineering practices and recommended tools (PyData stack, AWS, Alluxio, scikit-learn, tensorflow, mlflow, jupyter, github, docker, Spark, to name a few..)
- The R&D workflow and how it integrates with the production system
- Infrastructure considerations for scalable and cheap deployment, monitoring, and alerting
- How to leverage modern cloud serverless architectures for data and machine learning applications
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable technical challenges. In this talk, we will share some common pitfalls, lessons learned, and engineering practices, faced while building customer-facing enterprise ML products. In particular, we will focus on the engineering that delivers real-time audience insights everyday to thousands of marketers via the Helixa’s market research platform.
During the talk you will learn:
- An overview of the Helixa ML end-to-end system
- Useful engineering practices and recommended tools (PyData stack, AWS, Alluxio, scikit-learn, tensorflow, mlflow, jupyter, github, docker, Spark, to name a few..)
- The R&D workflow and how it integrates with the production system
- Infrastructure considerations for scalable and cheap deployment, monitoring, and alerting
- How to leverage modern cloud serverless architectures for data and machine learning applications
Video:
Presentation Slides:
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable technical challenges. In this talk, we will share some common pitfalls, lessons learned, and engineering practices, faced while building customer-facing enterprise ML products. In particular, we will focus on the engineering that delivers real-time audience insights everyday to thousands of marketers via the Helixa’s market research platform.
During the talk you will learn:
- An overview of the Helixa ML end-to-end system
- Useful engineering practices and recommended tools (PyData stack, AWS, Alluxio, scikit-learn, tensorflow, mlflow, jupyter, github, docker, Spark, to name a few..)
- The R&D workflow and how it integrates with the production system
- Infrastructure considerations for scalable and cheap deployment, monitoring, and alerting
- How to leverage modern cloud serverless architectures for data and machine learning applications
Videos:
Presentation Slides:
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Videos
Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.