Chris Fregly
where else to find them
x:cfregly
what they talk about
all 9 in the filters
where they keep going back
- Scale By the Bay5 editions2018–2023
- Data Con LA2 editions2020–2021
all 11 in the filters
on a stage with
- Chris Papileonce
- Christopher Bishoponce
- Dean Wampleronce
all 14 in the filters
who talks about the same things
- Rishit Dagli19 talks
- Julien Simon14 talks
- Adrian Boguszewski5 talks
- Rodolfo Bonnin3 talks
- Martin Gorner14 talks
- Daniel Whitenack5 talks
| title | conference | date |
|---|---|---|
| Foundation Models: Blueprints for success | AWS Innovate: Generative AI + Data Edition 2024 | 2024-03-14 |
Describes building and running foundation models using Optimum Neuron, the AWS Neuron SDK and Trainium/Inferentia on SageMaker, with concrete tradeoffs… | ||
| Implementing a Multi-Tenant Generative AI Chatbot. | Scale By the Bay 2023 | 2023-11-13 |
Live demo shows a working multi-tenant RAG chatbot deployed to EKS that uses LangChain + a FastAPI “rag API”, Amazon Bedrock for models (Anthropic Claude for… | ||
| Scaling AI & Machine Learning Workloads With Ray on AWS, Kubernetes, & BERT | Big Data LDN 2022 | 2022-09-21 |
Open source Ray on AWS and Kubernetes shows concrete configs and tools: Ray AIR for training and serving, Ray Tune for hyperparameter search, Ray Serve for… | ||
| 10 Things You Should Know About Quantum Computing and ML | Scale By the Bay 2021 | 2021-10-28 |
Explains practical quantum ML state: NISQ devices run hybrid circuits using Hadamard and CNOT gates and libraries like Qiskit, TensorFlow Quantum, PennyLane… | ||
| Keynotes 09/19/2021 | Data Con LA 2021 | 2021-09-17 |
Five short, concrete briefings: quantum claims that 6000 clean qubits (≈1M noisy) are needed to break RSA, current devices sit at 50–200 noisy qubits, error… | ||
| How to build personalization models using Amazon SageMaker | AWS re:Invent 2020 | 2020-11-30 |
Shows how to train a TensorFlow multitask recommender on MovieLens using Amazon SageMaker Studio, TensorFlow Recommenders and SageMaker hyperparameter tuning… | ||
| Smokey and the Multi-Armed Bandit featuring BERT Reynolds | Scale By the Bay 2020 | 2020-11-12 |
Fine-tuned two BERT variants (TensorFlow and PyTorch, using the Transformers library from Hugging Face) on the Amazon Customer Reviews dataset and deploys them… | ||
| Panel - Tips for Writing an Effective AI and Deep Learning Book | Data Con LA 2020 | 2020-10-23 |
Panel reports practical author choices: publishing route matters (self-publish boosts revenue, a major publisher supplies reviewers and marketing), many… | ||
| Waking the Data Scientist at 2am: Detect... | Global Artificial Intelligence Conference 2020 | 2020-10-20 |
Explains how SageMaker Debugger captures tensors to detect vanishing gradients and “loss not decreasing” and can automatically stop a training job or send an… | ||
| Keynote Panel: AI in Quantum Computing | Global Artificial Intelligence Virtual Conference Sep 2020 | 2020-09-16 |
Cambridge Quantum Computing launches a beta quantum random number generation service on IBM Quantum that returns certified randomness for cryptography and… | ||
| Quantum Computing | Global Artificial Intelligence Virtual Conference Sep 2020 | 2020-09-16 |
Amazon Web Services launches amazon bracket as a pay‑per‑task Jupyter workflow that runs circuits on simulators and real QPUs (IonQ, D‑Wave, Rigetti), logs… | ||
| End-to-End ML Pipelines with KubeFlow and TensorFlow Extended (TFX) | Scale By The Bay 2019 | 2019-11-13 |
Live demo of end-to-end pipelines that actually run on a Kubernetes cluster using Docker images, Airflow or Kubeflow as orchestrators, TFX components for TFX… | ||
| Interview | Scale By The Bay 2019 | 2019-11-13 |
Joined AWS as Technical Evangelist focused on machine learning and AI and describes creating scalable, open-source content from a developer-relations role that… | ||
| Continuous ML Applications in Production | Scale By The Bay 2018 | 2018-11-15 |
PipelineAI shows a production runtime that continuously trains, optimizes, validates and explains models using Continuous VOTE techniques, feeding online… | ||
| Interview | Scale By The Bay 2018 | 2018-11-15 |
Claims a production platform that turns batch ML into continuous training pipelines and serves models with live streaming updates. Implements end-to-end pieces… | ||
| High Performance Distributed Tensorflow | PyData Seattle 2017 | 2017-06-30 |
Live demos apply 8-bit quantization, weight‑rounding and batch‑normalization folding to trained TensorFlow graphs, then loadtest optimized vs unoptimized… | ||
| High-Performance Distributed Tensorflow | PyData London 2017 | 2017-05-05 |
Demos show concrete optimizations: 8-bit quantization and weight rounding shrink a 32k float model to ~13k and activation calibration is needed… | ||
| Deploying and Scaling Spark ML and Tensorflow AI Models | AI By the Bay 2017 | 2017-03-06 |
Shows a working pipeline that trains, packages and serves models with Tensorflow, Spark ML, Jupyter, Docker and Kubernetes plus NetflixOSS microservices, with… | ||
| Interview | AI By the Bay 2017 | 2017-03-06 |
Focuses on the gritty post-training work: how TensorFlow vs scikit-learn require different deployment paths, efforts to optimize models after training, and… | ||
| Real-time, Streaming Advanced Analytics.... | Data By the Bay 2016 | 2016-05-16 |
Live demos show a working pipeline that ingests clicks with NiFi→Kafka→Spark Streaming→Cassandra, trains ALS collaborative filtering (saved to Elasticsearch)… | ||
| DBTB INT Chris Fregly | Data By the Bay 2016 | 2016-05-16 |
Argues that for high-throughput streaming workloads exact answers cost too much and probabilistic approximations often meet production needs, demonstrates this… | ||
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