What we’re about
This is a group for women and non-binary persons interested in Machine Learning and Data Science. We meet to socialize, and to discuss machine learning and data science in an informal setting with the purpose of building a community around women in these fields. We are openly inclusive of anyone who identifies as female, genderqueer or non-binary. Men who support our mission are invited to attend our meetups as guests of female members or with permission from the organizers (please send a message to introduce yourself!), though priority will be given to female members if an event is overbooked.
Our Code of Conduct applies to all our spaces, both online and off.
• The official twitter account for the Bay Area meetup is @WiMLDS_BayArea and our email is email@example.com.
• Women & non-binary folks are invited to join the global WiMLDS Slack group by sending an email to firstname.lastname@example.org.
Upcoming events (2)See all
- Building and deploying RAG Pipelines in the EnterpriseLink visible for attendees
You will learn about:
- What is RAG
- How to build a RAG pipeline
- How to deploy it at an enterprise level
More about the event
Large Language Models (LLMs) have transformed the landscape of the tech world in not just the ability of being a fancy chatbot, but as universal translators across different domains to generate code, understand complex datasets and simplify user experiences across roles and for customers. They have the ability to process a huge diversified dataset to simplify tasks such as summarizing data, translating code to text and vice versa.
Despite LLMs value proposition, extracting business value needs more than just deploying these LLMModels as-is. Retrieval Augmented Generation (RAG) is a novel method to augment the base LLM model by integrating context via data relevant to the business problem, making the GenAI pipeline more adaptable to new information.
For example, AI chatbots using RAG can provide detailed and customized product information, bring semantic context to a conversation etc. Similarly, customerservice can be significantly improved, with customer service reps having access to accurate and recent information for optimized customer interactions.
In the applicability of enterprise search, RAG assists employees, often acting as agents,to bring together contextual information for various internal resources, such as documentation, company regulations, IT support blogs etc. Retrieval Augmented Generation presents a great opportunity in the LLM realm to cater to increased efficiency and accuracy while staying aligned to the business proposition. The use of RAG pipelines productively is one of the simplest approaches for enterprises to drive their LLM powered operations.
Rashmi is the Senior Director of Enterprise Generative AI Platform at Capital One. In this capacity, she leads the organization to democratize and scale Generative AI powered applications across the enterprise, through secure, reliable, robust and responsible enterprise-wide GenAI platform. In her past roles, she has led several enterprise product initiatives including the development of the auto-ML platform for SAP’s Digital supply chain business. Rashmi also dedicates her time to academia including delivering instruction on AI to graduate engineering students at CALCE, University of Maryland.
- Introduction to Autonomous Vehicles - PerceptionLink visible for attendees
By the end of this talk, you will:
- Gain a deeper understanding of how autonomous vehicles operate.
- Learn how computer vision is applied to perceive the vehicle's surroundings.
- How the Autonomous Vehicle Perception Team ensures vehicles make decisions that enhance safety and convenience.
About this event
Autonomous vehicles represent a groundbreaking shift in moving from one place to another. These vehicles are not merely self-driving; they are equipped with advanced technologies that allow them to perceive, understand, and react to their surroundings.
In this talk, we will embark on a journey into the world of autonomous vehicles, with a special focus on the role of camera data in object detection, tracking, and classification.
At the heart of their operation lies the intricate process of using camera data for object detection, tracking, and classification. Cameras are the eyes of the autonomous vehicle, capturing a continuous stream of visual data from the environment.
Deep learning algorithms process this data in real-time, enabling the vehicle to identify and categorize objects - from pedestrians and cyclists to other vehicles, traffic lights, and road signs. We'll delve into the technical intricacies of this process and how it enables autonomous vehicles to make split-second decisions that prioritize safety and efficiency.
We will also talk about the challenges in object detection, tracking, and classification using camera data and the overall challenges in model development and deployment for real-time systems like autonomous vehicles.
Our Speaker: Manika Kapoor
She's a Senior Systems Software Engineer at NVIDIA, currently a part of the Autonomous Vehicle Perception Team. Her work revolves around the fascinating world of computer vision, where she's deeply involved in developing and fine-tuning cutting-edge algorithms that power the visual intelligence of autonomous vehicles. With a keen focus on algorithm integration, she ensures that these advanced computer vision techniques seamlessly combine to enable our vehicles to perceive and interact with their surroundings.