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 APIs for batch and real-time scoring. 

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 for a model serving microservice.

RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we’ll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.

[Originally aired as part of the Data+AI Online Meetup (https://www.meetup.com/data-ai-online/) and Bay Area MLflow meetup]

Sascha Dittmann has created a series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, it’s here on Frank’s World and on YouTube.  

Subscribe for more free data analytics videos: https://www.youtube.com/saschadittmann?sub_confirmation=1And don’t forget to click the bell so you don’t miss anything. Share this video with a YouTuber friend: https://youtu.be/mZUdYu345dg

If you enjoyed this video help others enjoy it by adding captions in your native language:https://www.youtube.com/timedtext_video?v=mZUdYu345dg

Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used: https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps: https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service: https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension: https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

✅ Let’s connect:Twitter: https://twitter.com/SaschaDittmannFacebook: https://www.facebook.com/DataDrivenDevInstagram: https://www.instagram.com/saschadittmann/LinkedIn: https://www.linkedin.com/in/saschadittmannGitHub: https://github.com/SaschaDittmann

DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureMLIn this series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, I strongly recommend that you do so:

Subscribe for more free data analytics videos:
https://www.youtube.com/saschadittmann?sub_confirmation=1
And don’t forget to click the bell so you don’t miss anything.

Share this video with a YouTuber friend:

If you enjoyed this video help others enjoy it by adding captions in your native language:
https://www.youtube.com/timedtext_video?v=mZUdYu345dg

Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used:
https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps:
https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service:
https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension:
https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

✅ Let’s connect:
Twitter: https://twitter.com/SaschaDittmann
Facebook: https://www.facebook.com/DataDrivenDev
Instagram: https://www.instagram.com/saschadittmann/
LinkedIn: https://www.linkedin.com/in/saschadittmann
GitHub: https://github.com/SaschaDittmann

DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureML

Sascha Dittmann shows us how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure in this first in a series of videos.

In the first video of this 5-part series, you’ll discover how to create an Azure DevOps project, import sample machine learning code and create a DevOps pipeline to process simple Data Quality Checks.I use services like Azure DevOps and Azure Machine Learning Services for this challenge.

As hard as it is for data scientists to tag data and develop accurate machine learning models, managing models in production can be even more daunting.

Spotting model drift, retraining models with updated data sets, improving performance, and maintaining the underlying technology platforms are all important data science practices.

Without these disciplines, models can produce erroneous results that significantly impact business. The lesson here is that new obstacles emerge once machine learning models are deployed to production and used in business processes.

Developing production-ready models is no easy feat. According to one machine learning study, 55 percent of companies had not deployed models into production, and 40 percent or more require more than 30 days to deploy one model. Success brings new challenges, and 41 percent of respondents acknowledge the difficulty of versioning machine learning models and reproducibility.

ONNX Runtime inference engine is capable of executing ML models in different HW environments, taking advantage of the neural network acceleration capabilities.

Microsoft and Xilinx worked together to integrate ONNX Runtime with the VitisAI SW libraries for executing ONNX models in the Xilinx U250 FPGAs. We are happy to introduce the preview release of this capability today.

Video index:

[06:15] Demo by PeakSpeed for satellite imaging Orthorectification

Related links:

Other links:

The importance of MLOps cannot be overstated.

Once businesses commit to integrating devops practices to their AI projects, what’s the first step that IT teams can take towards successful MLOps?

Here’s an interesting article on how IT teams can create the best foundation for AI project to be successful.

For example, you might start creating Kubernetes clusters for AI workloads. That cluster will run a wide set of applications that need access to a variety of datasets and compute hardware — and likely even a variety of protocols.

Databricks talks about the latest developments and best practices for managing the full ML lifecycle on Databricks with MLflow.

Part 1: Opening Keynote and Demo

  • MLOps and ML Platforms State of the Industry, opening Keynote with Matei Zaharia, Co-founder and CTO at Databricks and Clemens Mewald, Director of Product Management at Databricks – https://youtu.be/9Ehh7Vl7ByM – Slideshare: https://www.slideshare.net/databricks/mlops-virtual-event-building-machine-learning-platforms-for-the-full-lifecycle
  • Operationalizing Data Science & ML on Databricks using MLflow (Demo) with Sean Owen, Principal Solution Architect at Databricks – https://youtu.be/cxAmu9w8BFo
  • Live Q&As – https://youtu.be/AQqqK5hRY5g

Resources:

In this video, learn why and how to track assets and code you’re creating in an end to end machine learning workflow.

Time Index:

  • [01:20] How to track assets and artifacts
  • [04:20] Demo – How to keep track of code
  • [05:58] Why it’s important to manage datasets + Demo

For More Info: