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. 

When you think of “deep learning” you might think of teams of PhDs with petabytes of data and racks of supercomputers.

But it turns out that a year of coding, high school math, a free GPU service, and a few dozen images is enough to create world-class models. fast.ai has made it their mission to make deep learning as accessible as possible.

In this interview fast.ai co-founder Jeremy Howard explains how to use their free software and courses to become an effective deep learning practitioner.

Learn More:

Yannic Kilcher retraces his first reading of Facebook AI’s DETR paper and explain my process of understanding it.

OUTLINE:

  • 0:00 – Introduction
  • 1:25 – Title
  • 4:10 – Authors
  • 5:55 – Affiliation
  • 7:40 – Abstract
  • 13:50 – Pictures
  • 20:30 – Introduction
  • 22:00 – Related Work
  • 24:00 – Model
  • 30:00 – Experiments
  • 41:50 – Conclusions & Abstract
  • 42:40 – Final Remarks

Original Video about DETR: https://youtu.be/T35ba_VXkMY

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 

Resources:

Slides: https://www.slideshare.net/databricks/from-pandas-to-koalas-reducing-timetoinsight-for-virgin-hyperloops-data

Koalas Notebook: https://pages.databricks.com/rs/094-YMS-629/images/koalas_webinar_code%20-%20Copy.html

The Bot Framework Composer is an integrated development tool for developers and multi-disciplinary teams to build bots and conversational experiences with the Microsoft Bot Framework.

In this episode of AI show, Seth Juarez is joined by Vishwac Sena Kannan, Program Manager for Bot Framework to introduce and demo Bot Framework Composer. Visit https://aka.ms/BotFrameworkComp to get started.

Index:
[00:47] – Introduction and overview
[01:45] – Demo – Creating a new bot with Bot Framework Composer
[02:25] – Walkthrough – local bot runtime
[03:30] – Demo – triggers, actions
[05:06] – Language generation integration
[06:08] – Sample bot with Language understanding (LUIS)
[09:00] – Handling interruptions
[11:10] – Wrap up

Will Kwan was told he wasn’t beautiful enough to be an Instagram model so he used a generative adversarial network to generate some beautiful Instagram people to pose for me.

Code:

Microsoft for Startups shares this highlight reel from the Spring MLADS conference.

In case you’re not familiar with MLADS, check out Data Driven’s coverage of the most recent one.

Twice a year, Microsoft assembles over 4,000 of our top data scientists and engineers for a two day internal conference to explore the state of the art around machine learning and data science.

Earlier this year, 30 leading startups who are active in the Microsoft for Startups program came to showcase their solutions and engage directly with the engineering teams.