Fifth Elephant Monsoon Edition: AI & Product Track videos (yes, all of them) This update is for participants only
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Online ML model performance benchmarking at Linkedin Scale : Implementation & Applications
At LinkedIn, we serve 100000s of inferences per second across 100s of ML models concurrently in our online systems. ML models have different system performance characteristics - ranging from lightweight XGBoosts to memory intensive recommendation models, to the newer Generative AI models, which are both compute and memory intensive. We run these models across different hardware profiles - across different CPU and GPU SKUs. Taking these into account, we have built a performance benchmarking system for ML models at LinkedIn based on the MLPerf Inference Benchmark paper. This system plays a crucial role in ensuring optimal performance and resource utilization. The system streamlines the ML model serving process, allowing ML engineers to launch models seamlessly, without the need to delve into complex hardware configurations.
We further explore the practical applications of the performance benchmarking system, which are as follows:
- Enable ML engineers and data scientists to iterate and experiment faster with models without worrying about hardware, performance characteristics and capacity estimation.
- Reduce costs through increased resource utilization by tuning system configurations.
- Build guardrails to identify and prevent regressions during rollout of new models and system software.
- Landscape of online ML inference at Linkedin
- MLperf Inference benchmarks
- Challenges faced and solutions
- Future work and conclusion
- Karan Goyal
- Hareesh Kumar Gajulapalli
- Ameya Karve