Ai+ Training

Composition in Machine Learning_ in Models, Tools, and Teams by Dr. Bryan Bischof

Bringing Choice, Automation and Performance to ML Deployment with Apache TVM and the OctoML Platform by Luis Ceze, PhD

Audio Processing and Feature Building for Machine Learning by Jyotika Singh

You Wanna Grab a Cup of Coffee_ A Data-Centric Approach To Deconstructing How Geospatial Patterns Shape Your Cup Of Coffee by Minerva Singh

Relationships Matter_ Using Connected Data for Better Machine Learning by Phani Dathar

Passive Privacy-respecting Collection of DNS Transaction Data by Paul Vixie

Privacy-Preserving Machine Learning_ Split Learning and Privacy Attacks by Grzegorz Gawron

ML Ops: Doing the Things to Preserve Tomorrow’s Machine Learning Sanity Today by Seth Juarez

Azure Machine Learning Enterprise Security Promises and Best Practices by Dennis Eikelenboom

Seeing the Unseen_ Inferring Unobserved Information from Limited Sensory Data by Adriana Romero

Data Science Performance isn’t What You Think: the Journey from Research to Production of the Data Science Workstation by David A. Liu

Scalable Natural Language Processing Using BERT OpenVINO AI Kit and Open Data Hub by Kyle Bader and Ryan Loney

Iterate Automated Feature Engineering_ A data scientist’s guide to faster, better features by Yusuke Muraoka

Think like a human_ Develop intuition in deep learning modeling by Jun Qian

The Matrix_ Networks, Stock Selection and ESG Outcomes by Temilade Oyeniyi, CFA

Information Flow and Deep Representation Learning by Michael Tamir, PhD

(Machine) Learning to Live with Wildfires - Mitigating Risks of Climate Change with Accelerated Analytics by Dr. Mike Flaxman and Abhishek Damera

Fast, Fresh Data for AI at Scale with a Feature Store by Riccardo Grigoletto

What’s Next for Data Scientists_ Auto ML+DO by Lisa Amini, PhD

Towards More Energy-Efficient Neural Networks_ Use Your Brain by Olaf de Leeuw

Teaching Machines Through Human Explanations by Xiang Ren, PhD

Assumption-free, General-purpose Ultra Large Incomplete Data Curing by In Ho Cho, PhD

Data-Centric Design Principles for AI Engineering by Vincent Sunn Chen

A ModelOps Approach to Address Ethical Concerns in AI Systems by May Masoud

Data Scientists & External Data Discovery_ A Match Made in Heaven by Victor Ghadban

Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber by Eduardo Blancas

Personalized Machine Learning by Julian McAuley, PhD

Analyzing the Chemistry of Data by Wendy Nather

A Framework for Identifying Host-Based Artifacts in Dark Web Investigations by Arica Kulm

The future of data science and machine learning at enterprise scale by Brian Flūg

Reasoning About the Probabilistic Behavior of Classifiers by Guy Van den Broeck, PhD

Building Operational Pipelines for Machine and Deep Learning by Yaron Haviv

Denormalization_ A Brief History and Its Role in the Modern Data Stack by James Mayfield

Best Practices for Data Annotation at Scale by Jai Natarajan

3 reasons why ML code is not like software by Conrado Miranda, PhD

Practical Individual Fairness Algorithms by Mikhail Yurochkin, PhD

Applications of Modern Survival Modeling with Python by Brian Kent, PhD

Responsible AI; From Principles to Practice by Tempest Van Schaik, PhD

Large-Scale Video Analytics with Ease by Fisher Yu

Acquiring and Exploiting the Semantics of Data by Craig Knoblock, PhD

Statistical Machine Learning by Quanquan Gu, PhD

Beyond Prediction_ What Makes a Senior Scientist by Arwen Griffioen

How Building a Personal Brand can help you Establish a Career in DS Field by Ken Jee

How to Prepare for the Future of Data Science by Daliana Liu _Allie Miller

Transitioning from an Analyst to Data Science Role by Marwan Kashef

How to improve data workload flexibility while lowering cloud data lake costs by over 50% by Arpan Roy

Trustworthy Decision Management: How Explainable, Predictive Decision Making Can Help Us Trust Our Decision Models by Jacopo Rota

Architecting for Modern Analytics Applications by Zeke Dean

Z by HP’s Workstation Data Science Solutions by Lenny Isler

Profiling and Optimizing PyTorch Applications with the PyTorch Profiler by Sabrina Smai

Vertica Accelerator – The Fastest SaaS Analytics and Machine Learning – from Start to Finish by Michael Bowen

Automate machine learning tasks with OCI Data Science Jobs by Lyudmil Pelov

Deliver AI & ML Models Faster, with Verta by Anthony Lee

Proactive Data Quality_ Why Culture Comes Before Tools by Tim Woods

Building ML and AI Applications with a Purpose-Built Time Series Database by Sam Dillard

Best Practices of Effective ML Teams by Carey Phelps

A Graph Data Science Framework for Enterprise by Stuart Laurie

DataRobot AI Cloud Demo_ Massive Business Impact from Extreme Automation by Andrea Kropp

Getting Started with Dask Using Saturn Cloud by Mitali Sanwal

Federated SQL with LiveRamp Safe Haven by Grzegorz Gawron

An Overview of Arize AI’s ML Observability Platform by Gabriel Barcelos

Streamlining Analytics with the S&P Global Marketplace Workbench by James Olejniczak

Teaching Data Science Effectively by Robert Schroll, PhD

Analyzing NVMO Mobile Signal Data with Accelerated Analytics by Joe Gifford

Weak Supervision in Practice by Patrick Kolencherry

Portable, light-weight, end-to-end autoML_ All the power, none of the pain by Alex Robson, PhD

Real-Time Feature Engineering with a Feature Store by Adi Hirschtein

Metrics Store as an Interface to Data by Allegra Holland

The Role of External Data in ML and BI Success by Victor Ghadban

See How AtScale's Semantic Layer Impacts BI & AI Performance on Popular Cloud Data Platforms by Daniel Gray

Reproducibility and Dependencies for Jupyter Notebooks by Francesco Murdaca

The Power of Data Science - Real World Use Cases by Jay Fraser

Managed AI_ How To Avoid The Pitfalls of No-Code AI by Aaron Cheng, PhD

How to Effectively Scale ML & AI in Any Organization by Ella Hilal, PhD

What do Planes and Machine Learning Have in Common? How Interpretable ML can Improve Decision-Making? by Serg Masis

Data-Driven Innovation for COVID-19 by Kristen Honey

Leadership and AI by Tom Coyle

Beyond the Basics_ Data Visualization in Python by Stefanie Molin

Deep Learning with Graphs - An Introduction to Graph Neural Networks (With Code Examples in Pytorch Geometric) by Sujit Pal

Manipulating and Visualizing Data with R by Jared Lander

MLOps... From Model to Production by Filipa Peleja, PhD

Data Analysis for SOC Survey by Christopher Crowley

Introduction to NLP and Topic Modeling by Zhenya Antić, PhD

WSL 2 in Real-Time with Z by HP by Adam Dettenwanger

In-Database Machine Learning with Python by Pranjal Singh

Natural Language Processing with PyTorch by Yashesh A. Shroff, PhD and Ravi Ilango

Rapid Data Exploration and Analysis with Apache Drill by Charles Givre

Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2 by Oliver Zeigermann

NLP Fundamentals by Leonardo De Marchi

Identifying Deepfake Images and Videos Using Python with Keras by Noah Giansiracusa, PhD

Data Science for Digital Forensics & Incident Response (DFIR) by Jess Garcia

Build a Question Answering System using DistilBERT in Python by Jayeeta Putatunda

apricot_ Taming Big Data by Removing Redundancy by Jacob Schreiber

Using Reproducible Experiments To Create Better Machine Learning Models by Milecia McGregor

Good, Fast, Cheap_ How to do Data Science with Missing Data by Matt Brems

Probabilistic Programming and Bayesian Inference with Python by Lara Kattan

Transferable Representation in Natural Language Processing by Kai-Wei Chang, PhD

Exploring the Interconnected World_ Network-Graph Analysis in Python by Noemi Derzsy, PhD

Building a ML Serving Platform at Scale for Natural Language Processing by Kumaran Ponnambalam

Tutorial on Uplift Modeling_ How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics by Victor Lo, PhD

Data-driven Modeling Approaches in Computational Drug Discovery by Hiranmayi Ranganathan, PhD

Women in Data Science Ignite