MLflow Office Hours: September 1, 2022
The MLflow community Q&A style online event occurs bi-weekly on Thursdays. Ask questions about MLflow and get to learn what we are building, planning to build and know about recently released features. What are your favorites? Come participate, engage with the community and get your voices heard for MLflow!
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Quick to Production with Minimal MLOps with the Best of Spark and TensorFlow
In this session, learn how we can get deep learning and ml models with TensorFlow into production quickly right after prototyping. Using TensorFlow with big datasets in a distributed setting has been an issue for small teams like ours due to complicated MLOps code, but with what we cover in the talk, we could now […]
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Introducing Databricks on Google Cloud
Databricks is now on Google Cloud and it brings an open lake house platform to the open data cloud to unify data engineering, data science, and analytics.
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MLflow Integration with PyCaret and PyTorch
In this talk, hosted by Databricks, learn how to build reproducible AI models and workflows using PyTorch and MLflow that can be shared across your teams, with traceability and speed up collaboration for AI projects.
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Data + AI Summit 2021 – Call for Presentations
The Data + AI Summit 2021 Call for Presentations is closing soon. Submit your full-length session ideas, lightning talk ideas, and more for the world’s largest gathering of Data + AI practitioners. The conference is at the end of May, but the CFP is due on Sunday, February 28. Data engineering, data analytics, AI, data […]
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Introduction to Databricks Unified Data Platform
Simplify your data lake. Simplify your data architecture. Simplify your data engineering. Powered by Delta Lake, Databricks combines the best of data warehouses and data lakes into a lakehouse architecture, giving you one platform to collaborate on all of your data, analytics and AI workloads.
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MLflow Model Serving in Databricks
Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. In this video from a Data + AI Summit Europe 2020 Meetup, Andre Mesarovic introduces […]
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MLOps on Azure Databricks with MLflow
In this session, Oliver Koernig, a Solutions Architect at Databricks, will illustrate and demonstrate how Databricks’ managed MLflow and the Azure ecosystem can be used to effectively implement an integrated MLOps lifecycle for managing and deploying Machine learning models. Oliver will focus on the MLflow Model Registry, a centralized model store, set of APIs and […]
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MLOps Using MLflow
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any […]
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Deterministic Machine Learning with MLflow and mlf-core
Machine learning suffers from a reproducibility crisis. Deterministic machine learning is not only incredibly important for academia to verify research papers, but also for developers in enterprise scenarios. Here’s a great video on how to address this shortcoming. Due to the various reasons for non-deterministic ML, especially when GPUs are in play, I conducted several […]
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