The Art Of Scalable Intelligence: Building Fair, Unbiased and Distributed Machine Learning Systems

In the age of exponential data growth, mastering the art of scalable intelligence is not just a choice but a necessity! Did you know that 81% of consumers are more likely to trust companies that use machine learning ethically? In this talk, we will unravel the secrets of crafting machine learning models that are not only highly intelligent but also unwaveringly fair and unbiased. Further, we'll explore how to harness the potential of distributed machine learning to design and architect ML systems to new heights, ensuring it serves every corner of our diverse digital landscape! As William Gibson said, 'The future is already here, it's just not evenly distributed.' so, lets delve into the intricate process of sculpting 'scalable intelligence' as every byte of data holds a world of potential.

Nutzen für den Teilnehmer:
Bunch of key takeaways from my talk include:
1) Audience will gain deep insights into the distinct challenges and remarkable opportunities inherent in distributed machine learning architectures, especially in the Python ecosystem!
2) During the talk we will discuss the heart of the enigmatic 'black box' problem, shedding light on why models can yield biased and unfair results, and how to address, architect these issues effectively.
3) Audience will learn how to design, fair, unbiased, and highly efficient machine learning systems through a live demo / implementation.
4) Audience will have a comprehensive understanding of scalable ML architectures, encompassing concepts such as data sharding, model synchronization, and robust fault tolerance mechanisms.

Behandelte Problemstellungen:
How can machine learning models be effectively designed to handle larger datasets and more complex tasks?

What challenges and issues are associated with bias in machine learning models, and what strategies and best practices can be employed to build fair and unbiased models that avoid discrimination?


What challenges and benefits come with distributing machine learning tasks across multiple machines or nodes and what are the requirements for fault tolerance, model synchronization, and data sharding to ensure efficient distributed machine learning?

Vortragssprache: Englisch
Level: Fortgeschrittene
Zielgruppe: Newcomer to Advanced

Unternehmen:
MIT

Research Affiliate Rashmi Nagpal