MLOps

AI MLOps

High Level MLOps from Microsoft Data Scientists

On this episode of the AI Show, Seth welcomes Microsoft Data Scientists, Spyros Marketos, Davide Fornelli and Samarendra Panda. They will give a high-level intro into MLOps and share some of the lessons they’ve learned working with customers along the way.

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MLOps

YOLO with Data-Driven Software

This presentation shows how to treat data like code through the concept of Data-Driven Software (DDS). This concept, implemented as a lightweight and easy-to-use python package, solves all the issues mentioned above for single user and collaborative data pipelines, and it fully integrates with a lakehouse architecture such as Databricks. In effect, it allows data […]

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Databricks MLOps

Learn to Use Databricks for the Full ML Lifecycle

Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In this video, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.

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Machine Learning MLOps

Productionalizing Machine Learning Solutions

Here’s an interesting post from the Databricks YouTube channel about productionalizing ML. At Intuit, we have deployed 100’s of Machine Learning models in production to solve various problems as below: Cash Flow forecasting Security, risk and fraud Document understanding Connect customers to right agents With so many models in production, it becomes very important to […]

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Databricks MLOps

Data Collab Lab | CI/CD with Databricks

In this episode of Data Collab Lab hosted by Lee Blackwell and Franco Patano  learn how to build Continuous Improvement and Continuous Delivery pipelines for your Machine Learning products.

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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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Data Databricks MLOps

How to Develop ML-enabled Data Pipelines on Databricks with IDE & CI/CD

Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. Furthermore, we can always have new data with new structural and/or statistical characteristics that can […]

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MLOps

The Data Behind MLOps

Here’s an exceptional conversation with Yaron, co-founder, and CTO of Iguazio. Yaron shares the challenges that still exists in developing Machine Learning based product for production, how having variety of data matters, how the platform supports performance at scale and enables real-time use cases and how Iguazio is fully integrated with Azure ML Studio, Microsoft […]

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MLOps

How to Apply MLOps at Scale

This video is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, […]

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