Home  >   Blog  >   Machine Learning / Conference   >   Attending MLconf SF 2018 #mlconf18

2018-11-22

Attending MLconf SF 2018 #mlconf18


I have attended MLconf 2018 in San Francisco. Since awesome speakers came from highly recognized industrial organizations, I can confidently say that MLconf can be a great place to see industrial trends and real-world "successful" use cases.

Surprisingly (and unsurprisingly), all of the following topics were covered in this one-day conference:

  • Interpretability
    • Saliency map for images vs. TCAV
  • Large-scale satellite image data collection
  • Scalable ML with Amazon SageMaker
    • Providing cheaper scalable solution on the cloud
    • Train local states (i.e., partial models) on multiple GPU-enabled instances in parallel in the streaming, incremental fashion, and finally merge them into single shared state
    • Reinventing k-means to make it scalable
  • Uber's NLP efforts on building AI for riders and drivers
    • TF-IDF + LSA vs. CNN
  • ML and deep learning applications
  • Practical lessons on differential privacy
  • and more!

My favorite session was Edo Liberty's one about Amazon SageMaker:

I have a special feeling for Edo because my master's research was strongly motivated by his paper; the paper eventually guided me to the world of scalable ML.

Hearing this session confirmed that I made the right decision by attending this year's MLconf. In fact, inside of SageMaker is still like a black box for me, but I can easily imagine that this out-of-the-box application is based on many advanced studies as Edo mentioned about approximation techniques for streaming data.

At the end of the event, the above tweet luckily won a free book:

Thanks organizers, I got "The Deep Learning Revolution"!

Overall, I really enjoyed this single-track, single-day conference thanks to the high-quality talks and well-organized program, as well as many networking opportunities. I personally believe ML, DL, and data science conferences should be more compact in terms of duration, number of sessions and attendees, just like MLconf, because the recent chaotic situation in those fields easily messes conference program; as an audience, too much input can sometimes be harmful to learning something truly valuable and important.

  Share


  Support

  Buy me a coffee

  See also

2021-08-28
Next "Dot" in Journey: Curiosity-Driven Job Change in Canada (Aug 2021)
2019-07-13
User Modeling, Adaptation, Personalization for Marketing #UMAP2019
2018-10-26
Apache Hivemall at #ODSCEurope, #RecSys2018, and #MbedConnect

  More

  Author: Takuya Kitazawa

Takuya Kitazawa is a sustainability-conscious product developer, minimalistic traveler, ultralight hiker & runner, and craft beer enthusiast. Throughout my career, I have practically worked as a full-stack software engineer, OSS developer, technical evangelist, sales engineer, data scientist, machine learning engineer, and product manager.

  Set up a 1:1 call with me

Opinions are my own and do not represent the views of organizations I am/was belonging to.

  Popular articles

2020-02-07
Why a Data Science Engineer Becomes a Product Manager
2018-10-26
Apache Hivemall at #ODSCEurope, #RecSys2018, and #MbedConnect
2017-02-25
Parallel Programming vs. Concurrent Programming