https://ift.tt/p51EuCN Data + AI Summit is the global event for the data community, where practitioners, leaders and visionaries come toge...
Data + AI Summit is the global event for the data community, where practitioners, leaders and visionaries come together to engage in thought-provoking dialogue and share the latest innovations in data and AI.
At this year’s Data + AI Summit, we’re excited to share some of the best sessions featuring MLflow. Leading innovators from across the industry – including Doordash, Databricks, and element61 – are joining us to share how they are using MLflow to streamline the ML lifecycle to deliver on their top use cases and goals.
Can’t-miss sessions featuring MLflow
As the adoption of data and AI continue to skyrocket across industries, the need for an easy-to-use tool to streamline the ML lifecycle can’t be underestimated. MLflow was created to help data scientists and developers with the complex process of ML model development, which typically includes the steps to build, train, tune, deploy, and manage machine learning models.
If you are a data scientist or ML engineer, here are three sessions highlighting the use of MLflow worth the price of admission.
MLOps at DoorDash
Tuesday, 5:30 PM (PDT)
Hien Luu, DoorDash
Streamlining ML development and productionization are important ingredients to realize the power of ML. But doing so is easier said than done as infrastructure complexities can slow progress. Join this session to learn how DoorDash is approaching MLOps, the challenges they faced, how Databricks and MLflow fit into their infrastructure, and lessons learned from their experiences.
MLOps on Databricks: A How-To Guide
Tuesday, 5:30 PM (PDT)
- Niall Turbitt, Databricks
- Rafi Kurlansik, Databricks
- Joseph Bradley, Databricks
Building and deploying machine learning (ML) models can be complex. At Databricks, we see firsthand how customers develop their MLOps approaches—those that work well, and those that do not. In this session, we show how your organization can build robust MLOps practices incrementally. We will unpack general principles which can guide your organization’s decisions for MLOps, presenting the most common target architectures we observe across customers.
Implementing an End-to-End Demand Forecasting Solution Through Databricks and MLflow
Thursday, 8:30 AM (PDT)
- Ivana Pejeva, element61
- Yoshi Coppens, element61
In the retail industry, understanding customer demand is critical to driving revenue and profitability. With massive volumes of data captured daily, retailers are leveraging ML to streamline operations by forecasting customer demand and optimizing supply chain management. This session focuses on how element61 is helping top retailers improve efficiencies and sharpen fresh product production and delivery planning. By leveraging the Lakehouse Platform, they benefit from the power of Delta Lake, Feature Store, and MLflow to build a highly reliable ML factory.
Expert MLOps trainings featuring MLflow
Ready to streamline the ML lifecycle with Databricks Machine Learning, MLflow, and other Databricks capabilities? Check out the following training sessions tailored to your level of experience and topic of interest.
Training: Managing Machine Learning Models
Monday, 8:00 AM (PDT)
- Audience: Machine learning engineers, data scientists
- Duration: Half-day
- Hands-on labs: Yes
Build the foundation for efficient model management and operations at scale — from model tracking to automating the ML lifecycle — using Databricks ML, MLflow, Databricks Autologging, and more.
Training: Deploying Machine Learning Models
Monday, 8:00 AM (PDT)
- Audience: Machine learning engineers, data scientists
- Duration: Half-day
- Hands-on labs: Yes
Model deployment is arguably the most painful and time-consuming stage in the ML lifecycle. This training compares various model deployment strategies and provides a hands-on lab using MLflow and Spark UDFs to deploy an ML model in an incrementally processed streaming environment.
Training: Managing Machine Learning Models
Monday, 8:00 AM (PDT), 1:00 PM (PDT)
- Audience: Machine learning engineers, data scientists
- Duration: Half-day
- Hands-on labs: Yes
The key to accelerating the ML lifecycle is to automate the most time-consuming manual, repeated and error-prone processes. This training will teach learners how to automate the ML lifecycle and streamline model management using MLflow Tracking, MLflow Model Registry, MLflow Model Registry Webhooks, and Databricks Jobs.
Training: Advanced Machine Learning with Databricks — Bundle: Day 1
Monday, 8:00 AM (PDT), 1:00 PM (PDT)
Training: Advanced Machine Learning with Databricks — Bundle: Day 2
Thursday, 8:00 AM (PDT)
- Audience: Machine learning engineers, data scientists
- Duration: 2 days
- Hands-on labs: Yes
Ready to take your ML engineering skills to the next level? Learners will gain advanced ML engineering skills enabling them to organize, scale, and operationalize ML applications using Databricks.
Productionizing Ethical Credit Scoring Systems with Delta Lake, Feature Store and MLFlow
Tuesday, 4:00 PM (PDT)
Jeanne Choo, Databricks
This talk aims to illustrate how ethical principles can be operationalized, monitored and maintained in production using tools such as Delta Lake and MLflow, thus moving beyond only accuracy-based metrics of model performance and towards a more holistic and principled way of building machine learning systems.
Sign up for MLflow talks at Summit!
Make sure to register for the Data + AI Summit to take advantage of all the amazing sessions and trainings featuring MLflow. Registration is free!
--
Try Databricks for free. Get started today.
The post Can’t-miss Sessions Featuring MLflow appeared first on Databricks.
from Databricks https://ift.tt/HaoRQv3
via RiYo Analytics
ليست هناك تعليقات