Timezone: »
There has been a recent burst in “AutoML” techniques as a means to automate the creation of ML models without necessary domain expertise. This demonstration looks well beyond AutoML’s current narrow focus on automated model building, to tackling automation across the full end-to-end AI/ML lifecycle. In industry settings, the AI/ML lifecycle typically includes a series of labor-intensive tasks such as preparing data, training models, deploying the selected model in cloud, monitoring performance, identifying faults, and taking corrective actions when failures or new business requirements occur. Enormous opportunities exist for scaling, automating, and accelerating this AI/ML lifecycle.
In this session, we demonstrate tools and research results in driving automation across the entire AI/ML lifecycle: from assessing data readiness and recommending mitigations, to semantically-driven automation based on concept discovery and knowledge augmentation, to advanced ML model building with business and fairness constraints, to novel pipelines for industry-critical modalities, to automation for monitoring models in deployment, recognizing deficiencies and recommending corrective actions. We will also demonstrate practical methods for scaling in multi-cloud environments with federated learning, and accelerated cloud-based inference of widely-popular classical ML algorithms such as XGBoost and LightGBM.
Author Information
Lisa Amini (IBM Research)
Nitin Gupta (IBM Research)
Parikshit Ram (IBM Research)
Kiran Kate (IBM Research)
Bhanukiran Vinzamuri (IBM Research)
Nathalie Baracaldo Angel (IBM Research AI)
Martin Korytak (IBM Research)
Daniel K Weidele (IBM Research)
Dakuo Wang (IBM)
More from the Same Authors
-
2021 : FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning »
Yi Zhou · Parikshit Ram · Theodoros Salonidis · Nathalie Baracaldo Angel · Horst Samulowitz · Heiko Ludwig -
2021 : RASL: Relational Algebra in Scikit-Learn Pipelines »
Kiran Kate · Avi Shinnar · Thanh Lam Hoang · Martin Hirzel -
2021 : Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD »
Chen Fan · Parikshit Ram · Sijia Liu -
2021 : Contributed Talk 6: FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning »
Yi Zhou · Parikshit Ram · Theodoros Salonidis · Nathalie Baracaldo Angel · Horst Samulowitz · Heiko Ludwig -
2021 : RASL: Relational Algebra in Scikit-Learn Pipelines »
Chirag Sahni · Kiran Kate · Avi Shinnar · Thanh Lam Hoang · Martin Hirzel -
2021 Poster: Pipeline Combinators for Gradual AutoML »
Guillaume Baudart · Martin Hirzel · Kiran Kate · Parikshit Ram · Avi Shinnar · Jason Tsay -
2020 Workshop: Workshop on Dataset Curation and Security »
Nathalie Baracaldo Angel · Yonatan Bisk · Avrim Blum · Michael Curry · John Dickerson · Micah Goldblum · Tom Goldstein · Bo Li · Avi Schwarzschild -
2020 Poster: Model Agnostic Multilevel Explanations »
Karthikeyan Natesan Ramamurthy · Bhanukiran Vinzamuri · Yunfeng Zhang · Amit Dhurandhar -
2020 Poster: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 Spotlight: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 : Spotlight on women at IBM Research »
Lisa Amini · Francesca Rossi · Celia Cintas · Payel Das -
2019 : Poster Session »
Nathalie Baracaldo Angel · Seth Neel · Tuyen Le · Dan Philps · Suheng Tao · Sotirios Chatzis · Toyo Suzumura · Wei Wang · WENHANG BAO · Solon Barocas · Manish Raghavan · Samuel Maina · Reginald Bryant · Kush Varshney · Skyler D. Speakman · Navdeep Gill · Nicholas Schmidt · Kevin Compher · Naveen Sundar Govindarajulu · Vivek Sharma · Praneeth Vepakomma · Tristan Swedish · Jayashree Kalpathy-Cramer · Ramesh Raskar · Shihao Zheng · Mykola Pechenizkiy · Marco Schreyer · Li Ling · Chirag Nagpal · Robert Tillman · Manuela Veloso · Hanjie Chen · Xintong Wang · Michael Wellman · Matthew van Adelsberg · Ben Wood · Hans Buehler · Mahmoud Mahfouz · Antonios Alexos · Megan Shearer · Antigoni Polychroniadou · Lucia Larise Stavarache · Dmitry Efimov · Johnston P Hall · Yukun Zhang · Emily Diana · Sumitra Ganesh · Vineeth Ravi · · Swetasudha Panda · Xavier Renard · Matthew Jagielski · Yonadav Shavit · Joshua Williams · Haoran Wei · Shuang (Sophie) Zhai · Xinyi Li · Hongda Shen · Daiki Matsunaga · Jaesik Choi · Alexis Laignelet · Batuhan Guler · Jacobo Roa Vicens · Ajit Desai · Jonathan Aigrain · Robert Samoilescu -
2018 Poster: Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization »
Sijia Liu · Bhavya Kailkhura · Pin-Yu Chen · Paishun Ting · Shiyu Chang · Lisa Amini -
2018 Demonstration: Game for Detecting Backdoor Attacks on Deep Neural Networks using Activation Clustering »
Casey Dugan · Werner Geyer · Narendra Nath Joshi · Ingrid Lange · Dustin Ramsey Torres · Bryant Chen · Nathalie Baracaldo Angel · Heiko Ludwig -
2017 Poster: Optimized Pre-Processing for Discrimination Prevention »
Flavio Calmon · Dennis Wei · Bhanukiran Vinzamuri · Karthikeyan Natesan Ramamurthy · Kush Varshney -
2013 Poster: Which Space Partitioning Tree to Use for Search? »
Parikshit Ram · Alexander Gray -
2009 Poster: Linear-time Algorithms for Pairwise Statistical Problems »
Parikshit Ram · Dongryeol Lee · William B March · Alexander Gray -
2009 Spotlight: Linear-time Algorithms for Pairwise Statistical Problems »
Parikshit Ram · Dongryeol Lee · William B March · Alexander Gray -
2009 Poster: Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions »
Parikshit Ram · Dongryeol Lee · Hua Ouyang · Alexander Gray