InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
/presentations/deploing-models-gpt3-openai-clip/en/mediumimage/roland-meertens-m-1655444707687.jpg)
-
ML Panel: "ML in Production - What's Next?"
The panelists discuss lessons learned with putting ML systems into production, what is working and what is not working, building ML teams, dealing with large datasets, governance and ethics/privacy.
/presentations/ml-production-2021/en/mediumimage/qcon-plus-m-1653893424852.jpeg)
-
Machine Learning at the Edge
Katharine Jarmul discusses utilizing new distributed data science and machine learning models, such as federated learning, to learn from data at the edge.
/presentations/ml-edge/en/mediumimage/Katharine-Jarmul-medium-1653645901440.jpg)
-
Unified MLOps: Feature Stores and Model Deployment
Monte Zweben proposes a whole new approach to MLOps that allows to scale models without increasing latency by merging a database, a feature store, and machine learning.
/presentations/mlops-model-deployment/en/mediumimage/monte_zweben-m-1652696083026.jpg)
-
MLOps: the Most Important Piece in the Enterprise AI Puzzle
Francesca Lazzeri overviews the latest MLOps technologies and principles that data scientists and ML engineers can apply to their machine learning processes.
/presentations/mlops-tech/en/mediumimage/francesca-lazzeri-m-1651751321874.jpg)
-
Developing and Deploying ML across Teams with MLOps Automation Tool
Fabio Grätz and Thomas Wollmann discuss the MLOps Automation tool, and how it can be used to perform DevOps tasks on ML across teams.
/presentations/mlops-automation/en/mediumimage/mlops-automation-m-1651223644847.jpg)
-
Iterating on Models on Operating ML
Monte Zweben and Roland Meertens discuss the challenges in building, maintaining, and operating machine learning models.
/presentations/operating-ml-models/en/mediumimage/Untitled-1-1650979197460.jpg)
-
Building & Operating High-Fidelity Data Streams
Sid Anand discusses building high-fidelity nearline data streams as a service within a lean team.
/presentations/building-high-fidelity-data-streams/en/mediumimage/Sid-Anand-m-1648803206604.jpg)
-
Data Pipelines & Data Mesh: Where We Are and What the Future Looks Like
Zhamak Dehghani, Tareq Abedrabbo and Jacek Laskowski discuss the current challenges for building Modern Data Pipelines and applying Data Mesh in the real world, what the future looks like, and tools.
/presentations/data-pipelines-mesh/en/mediumimage/data-pipelines-mesh-m-1648200228421.jpg)
-
Federated GraphQL to Solve Service Sprawl at Major League Baseball
Olessya Medvedeva and Matt Oliver discuss how they have begun to implement a Federated GraphQL architecture to solve the issue of service discovery, sprawl and ultimately getting the data needed.
/presentations/graphql-major-league-baseball/en/mediumimage/graphql-major-league-baseball-1648197928012.jpg)
-
Experimenting with WASM for Future Audience Experiences in BBC iPlayer
Tim Pearce discusses how they used WebAssembly to deploy their iPlayer across various web browsers, what advantages this approach had and how they intend to use WebAssembly outside the browser.
/presentations/iplayer-wasm/en/mediumimage/tim_pearce-m-1646128105781.jpg)
-
Panel: Future of Language Support for ML
Jendrik Jördening, Irene Dea, Alanna Tempest take a look at the state of the art of ML/AI development and how advances in language technology (specifically differentiable programming langs) can help.
/presentations/ml-language-support/en/mediumimage/qcon-plus-m-1646128204616.jpg)