MLFlow

Databricks Google

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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Data Science Databricks

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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AI Big Data Data

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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Databricks

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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Databricks

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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Databricks MLOps

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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Databricks MLOps

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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Machine Learning

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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Databricks Machine Learning

What’s New in MLflow? Accelerating the Machine Learning Lifecycle

In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management. Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking. Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference […]

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AI Databricks

Taking Deep Learning to Production with MLflow & RedisAI

Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution […]

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