Alejandro Saucedo
where else to find them
what they talk about
all 13 in the filters
where they keep going back
- PyData Global2 editions2022–2023
- CppCon2 editions2021–2022
- PyData London2 editions2019–2022
all 15 in the filters
on a stage with
all 6 in the filters
who talks about the same things
- Ed Shee5 talks
- Animesh Singh23 talks
- Joshua Arvin Lat20 talks
- Vincent Caldeira16 talks
- Rishit Dagli19 talks
- Ezequiel Lanza21 talks
| title | conference | date |
|---|---|---|
| MLOps 2025: A Journey into the Past and the Future | SREcon25 Europe/Middle East/Africa | 2025-10-07 |
History traces platforms like Michelangelo, SageMaker, TFX and runtimes like Flink, Ray, Metaflow while orchestration and experiment tools such as Kubeflow… | ||
| The State of GenAI & ML in the Cloud Native Ecosystem | Cloud Native + Kubernetes AI Day Europe 2025 | 2025-04-01 |
Overview of production GenAI and ML at scale with concrete claims: enterprises like Zalando run forecasting, pricing, search and a conversational fashion… | ||
| Collaborative Intelligence: The Human & AI Partnership | WeAreDevelopers World Congress 2024 | 2024-07-17 |
Shows concrete examples: ChatGPT schedules a last‑minute train and Llama (Meta) generates a sonnet; GitHub Copilot and Stack Overflow surface combined context… | ||
| The State of Production Machine Learning in 2023 | PyData Global 2023 | 2023-12-06 |
Claims that model lifecycle begins when deployed not when trained and that production ML shifts from model-centric to system- and data-centric; names practical… | ||
| Metadata Systems for End-to-End Data & Machine Learning | PyData Global 2022 | 2022-12-01 |
Claims that metadata must describe entire ML systems not just model artifacts and shows concrete work: adoption of the open inference protocol and an open… | ||
| Industrial Strength DALLE-E:Scaling Complex Large Text & Image Models | PyData Global 2022 | 2022-12-01 |
Demo shows a reproducible pipeline that productionises a DALL-E image model hooked to GPT2, with runnable code, Docker files and a Kubernetes manifest. It uses… | ||
| Welcome + Opening Remarks: The State of Production MLOps in the Cloud Native... | Kubernetes AI Day North America 2022 | 2022-10-25 |
Argues that the ML lifecycle begins at deployment and lists concrete controls: an architectural blueprint separating training, artifact store and inference… | ||
| MetaOps: Metadata Operations For End-To-End Data & Machine Learning Platforms | KubeCon + CloudNativeCon North America 2022 | 2022-10-24 |
Argues that artifact stores and ad hoc schemas fail at production scale where thousands of models and many-to-many deployments exist, shows Seldon Core… | ||
| GPU Accelerated Computing & Optimizations on Cross-Vendor Graphics Cards with Vulkan & Kompute | CppCon 2022 | 2022-09-11 |
Uses Vulkan SDK and Kompute to build a runnable GPU ML pipeline from scratch that executes on over 1000 GPUs across AMD Qualcomm NVIDIA, replaces the 500–2000… | ||
| Accelerating Machine Learning at Scale with | PyData London 2022 | 2022-06-17 |
Takes a GPT-2 model from HuggingFace, converts and quantizes it with Optimum into ONNX conversion artifacts, runs inference locally on MLServer and deploys to… | ||
| Accelerating High-Performance Machine Learning at Scale i... | KubeCon + CloudNativeCon Europe 2022 | 2022-05-16 |
Deploys Distil GPT-2 from the Hugging Face hub, converts it to a quantized ONNX artifact with Optimum, and serves it locally with MLServer before scheduling… | ||
| Secure ML: Automated Security Best Practices in Machine Learning | PyCon DE & PyData Berlin 2022 | 2022-04-11 |
Trains a scikit‑learn logistic‑regression on the iris data set, pickles and deploys it as a Kubernetes microservice via seldon core deployment, then… | ||
| Beyond CUDA: GPU Accelerated Computing on Cross-Vendor Graphics Cards with Vulk... | KubeCon + CloudNativeCon + Open Source Summit China 2021 | 2021-12-09 |
A cross‑vendor GPU compute stack is demonstrated using Vulkan SDK and the Kompute framework with both Python and C++ bindings, running on AMD, Qualcomm and… | ||
| GPU Accelerated Computing on Cross-Vendor Graphics Cards with Vulkan Kompute | CppCon 2021 | 2021-10-24 |
Shows concrete results: uses Vulkan and the Kompute framework to run a simple GPU‑accelerated ML kernel from scratch on AMD, Qualcomm, NVIDIA and mobile GPUs… | ||
| Automated Machine Learning Performance Evaluation | KubeCon + CloudNativeCon Europe 2021 | 2021-05-04 |
Deploys a TensorFlow ResNet32 from a Google bucket into Seldon Core on Kubernetes, runs automated grid searches over CPU, RAM, replicas, threads and workers… | ||
| GPU Computing Made Simple with the C++ Vulkan SDK & the C++ Kompute Framework (AMD, Qualcomm, NVIDIA & Friends) | FOSDEM 2021 | 2021-02-06 |
Many advanced data processing paradigms fit incredibly well to the parallel-architecture that GPU computing offers, which has resulted in the continuously… | ||
| Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance | FOSDEM 2021 | 2021-02-06 |
The lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive of the best practices, principles… | ||
| Beyond CUDA: GPU Accelerated Python on Cross-Vendor Graphics Cards with Vulkan Kompute | FOSDEM 2021 | 2021-02-06 |
This talk will provide practical insights on high performance GPU computing in Python using the Vulkan Kompute framework. We will cover the trends in GPU… | ||
| A CI/CD Framework for Production Machine Learning at Massive Scale (using Jenkins X and Seldon Core) | cdCon 2020 | 2020-10-07 |
Shows a hands‑on pipeline that trains a text‑classification model, packages it with Seldon Core into a Docker microservice, and promotes versions via Jenkins X… | ||
| Guide towards algorithm explainability in machine learning | PyData London 2019 | 2019-07-12 |
Practical walkthrough that trains a loan-approval model on ~8,000 rows, gets 98% vs 54% when tested on production-like labels, and then applies concrete steps… | ||
| The State of AI and Machine Learning Operations in 2019 | KubeCon + CloudNativeCon + Open Source Summit China 2019 | 2019-06-24 |
A 2019 survey of MLOps that names concrete tools and shows working examples: reproducible pipelines using DVC, Pachyderm, Seldon with persistent volumes for… | ||
| The State of AI and Machine Learning Operations in 2019 | KubeCon + CloudNativeCon China 2019 | 2019-06-24 |
Overview of 2019 MLOps focusing on reproducibility, orchestration and explainability with concrete tools and demos: reproducible pipelines using Kubeflow-style… | ||
| Industrial ML – Overview of the Technologies Available for Machine Learning | PyData Berlin 2018 | 2018-07-06 |
A survey of distributed, horizontally scalable Python machine-learning pipelines, covering motivations, technologies, best practices and caveats. It focuses on… | ||
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