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.
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 MLflow model serving, talks about scoring models with MLflow (including online with MLflow scoring server and offline with Apache Spark), and custom model deployment and scoring.
Rafal Lukawiecki has been actively working in data science, machine learning, and data mining for well over a decade, and he has formally studied and used artificial intelligence long before it was popular, back in the great AI Winter.
Watch this episode to find out how he organizes his reproducible workflow.
Join Seth Juarez as he delves into ethical concerns with AI with Josh Lovejoy, who leads Design for Microsoft Ethics & Society within Cloud + AI, and Sarah Bird, who leads Responsible AI for Cognitive Services.
This video explores how to think about Ethical AI and how to ensure the software you build is designed, developed and deployed ethically. Josh and Sarah describe how Ethics & Society works closely with product teams to make human-centered AI / ML technologies that serve people by appreciating the constraints and measuring accuracy and inaccuracy throughout the product development lifecycle.
Parallel run in Azure Machine Learning enables big data processing in distributed manner.
In this video, learn more about Azure Machine Learning dataset; how it can help manage your data in machine learning workflow; and how to use dataset in parallel run to accelerate your big data processing.
Databricks recently held a webinar on how they worked with Virgin Hyperloop One engineers.
They discuss the goals, implementation, and outcome of moving from Pandas code to Koalas code and using MLflow. Lots of code, notebooks, demos, etc.
Come hear Patryk Oleniuk, Software Engineer at Virgin Hyperloop (VHO) discuss how VHO has dramatically reduced processing time by 95%, while changing less than 1% of previously single-threaded, pandas-based python code. Attendees of this webinar will learn:
How VHO leverages public and private transportation data to optimize Hyperloop designHow to ‘Sparkify’ (scale) your pandas code by using ‘Koalas’ with minimal code changesHow to use ‘Koalas’ and MLflow for sweeping machine learning models and experiment resultsFeatured SpeakersPatryk Oleniuk, Lead Data Engineer, Virgin Hyperloop OneYifan Cao, Senior Product Manager, Databricks