About Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
Every year, Anthill Inside brings together 500+ practitioners to discuss conceptual and practical developments in AI and Deep Learning in an environment facilitated by peer review and peer-to-peer learning.
About the 2019 edition:
The 2019 edition will feature talks, workshops, tutorials and Birds of Feather (BOF) sessions on:
- Interpretability, Explainability, Bayesian Networks, Reinforcement Learning, and Knowledge Graphs.
- Data acquisition and health of data; incorporating label uncertainty while training models.
- Retraining models; detecting and handling concept drifts in the data.
- Human intervention factors, including incorporating learnings from the feedback loop; pointers for moving in the direction of auto-pilot mode.
- AI and cloud strategy.
Sponsorship slots are open, click here for the Sponsorship Deck.
Anthill Inside 2019 sponsors:
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Feast: Feature Store for Machine LearningGOJEK, Indonesia’s first billion-dollar startup, has seen an explosive growth in both users and data over the past three years. Today, it uses big data-powered machine learning to inform decision making in its ride-hailing, lifestyle, logistics, food delivery, and payment products, from selecting the right driver to dispatch to dynamically setting prices to serving food recommendations to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning. Features are at the heart of what makes these machine learning systems effective. However, many challenges still exist in the feature lifecycle. Developing features from big data is often an engineering heavy task, with challenges in both the scaling of data processes and the serving of features in production systems. Teams also face challenges in enabling discovery, reducing duplication, improving understanding, and providing standardization of features throughout organizations. Willem will explain the need for features at organizations like GOJEK and discuss the challenges faced in creating, managing, and serving them in production. He’ll describe how in partnership with Google, they designed and built a feature store called Feast to address these challenges and explore their motivations, the lessons they learned along the way, and the impact the feature store had on GOJEK. Finally, he will talk about the open source plans for Feast and their roadmap going forward.
Vijay Gabale, Co-founder and CTO of Infilect Technologies
Birds of Feather (BOF) session: Myths and realities of data labeling for Deep Learningsetting the context : data labeling for NLP and CV how to define a data labeling task : novice vs expert does crowd sourcing of data labeling really work : adv vs disadv. how to manage in house data labeling teams : adv vs disadv what is the criticality of the correctness of data labels what is the experience and expertise expectation of data labelers how to ensure correctness of data labels : manual vs automated checks how to resolve labeling conflicts how does an engineer know if she has enough labeled data what are the time, cost, correctness trade-offs how to ensure and execute class balanced data labeling how to plan and execute weakly supervised data labeling how to train models on small set of labeled data and generate ‘soft tags’ for the rest of the unlabeled data how does one know if a model is performing well in practice on unseen and real-time inputs how does feedback loop work when some of the unseen and real-time inputs are labeled to fine-tune the models
Nischal HP, VP of Engineering and data science at omni:us
Document digitization - Rethinking it with Deep LearningThis talk will outline: * The problems and approaches we faced when building deep learning networks to solve problems in the information extraction process. * Thought process on why and how we chose certain deep learning strategies * The requirement for supervised learning * Limitations of deep learning networks * Planning and executing research activities in short cycles * Evolution of team structures to support AI product building * Engineering practises required in building AI systems.