About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com
Sponsors:
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
Bronze Sponsor
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Community Sponsor
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Hosted by
Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more
Session video
Proposal video
SM
Srujana Merugu
@srujana-merugu
ML application lifecycle: recommendations for each stage
Submitted Nov 1, 2019
Section: Full talk
Technical level: Intermediate
Session type: Lecture
Building good ML systems is not very unlike developing good software. Just as developing good software requires mastering not only programming theory, tools, and design patterns, but also the process of software development itself, building a good ML system entails familiarity with the ML application lifecycle. In this talk, we will discuss the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design &implementation, testing, deployment & maintenance, online evaluation & evolution, and some key learnings that are relevant for each of these stages.
Outline
Building good ML systems is not very unlike developing good software. Just as developing good software requires mastering not only programming theory, tools, and design patterns, but also the process of software development itself, building a good ML system entails familiarity with the ML application lifecycle. In this talk, we will discuss the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design &implementation, testing, deployment & maintenance, online evaluation & evolution, and some key learnings that are relevant for each of these stages.
Requirements
None
Speaker bio
Srujana is an independent machine learning researcher and consultant with over 15 years of experience. Till recently, she was the chief scientist of CuspEra, a software marketplace startup. Prior to that, she was a principal data scientist at Flipkart (Bangalore) and a volunteer for Ekstep, an education startup. She has been employed with the machine learning groups at Amazon (Bangalore), IBM Research (Bangalore/New Delhi/Almaden/Yorktown Hts), and Yahoo Research (Santa Clara). Srujana has published her work in several top-tier conferences and journals on data mining/machine learning and is the recipient of multiple best paper awards. She received her M.S. and Ph.D. from the University of Texas at Austin and her B. Tech. degree from IIT Madras.
Links
- https://developers.google.com/machine-learning/problem-framing/?utm_source=googleAI&utm_medium=card-image&utm_campaign=training-hub&utm_term=&utm_content=problem-framing
- https://cloud.google.com/blog/products/ai-machine-learning/making-the-machine-the-machine-learning-lifecycle
- https://developers.google.com/machine-learning/guides/rules-of-ml
- https://www.slideshare.net/SrujanaMerugu1/ml-basics-204380593
Slides
https://www.slideshare.net/SrujanaMerugu1/ml-application-life-cycle
Comments
BS
Bikram Sengupta
Build an enterprise grade data labelling pipeline to scale your ML/AI pipelines
In Software 2.0, Data is code. A mindful approach to your data annotation pipeline and practices is critical to the outcomes of your ML algorithms. If not done right, your ability to scale this pipeline can often prove to be a major blocker to productionization. more
07 Nov 2019
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