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
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
