The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:
- Model management, including data cleaning, instrumentation and productionizing data science.
- Bad data and case studies of failure in building data products.
- Identifying and handling fraud + data security at scale
- Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
- Feature engineering and ML platforms.
- What it takes to create data-driven cultures in organizations of different scales.
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
Why you should attend?
- Network with peers and practitioners from the data ecosystem
- Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
- Demo your ideas in the demo session
- Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.
Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email email@example.com
JSFoo:VueDay 2019 sponsors:
The last mile problem in ML
“We have built a machine learning model, What next?”
There is quite a bit of journey that one needs to cover from building a model in Jupyter notebook to taking it to production.
I would like to call it as the “last mile problem in ML” , this last mile could be a simple tread if we embrace some good ideas.
This talk covers some of these opinionated ideas on how we can get around some of the pitfalls in deployment of ML models in production.
We would go over the below questions in detail think about solutions for them.
- How to fix the zombie models apocalypse, a state when nobody knows how the model was trained ?
- In Science, experiments are found to be valid only if they are reproducible. Should this be the case in Datascience as well ?
- Training the model in your local machine and waiting for an eternity to complete is no fun. What are some better ways of doing this ?
- How do you package your machine learning code in a robust manner?
- Does an ML project have the luxury of not following good Software Engineering principles?
- Discussion on some of the issues with deploying ML models to production.
- Discussion about
mlflowincluding a quick demo.
- Discussion about
sagemakerBYO algorithms training.
- Discussion about packagining ML code in a robust manner.
- Highlevel understanding of machine learning.
My name is Krishna Sangeeth. I am currently working as a DataScientist @ Ericsson Global AI Accelerator (GAIA) . Prior to Ericsson, I was working @ Indix as an ML Engineer. I am a passionate programmer always on the look out for learning something new. I am an opensource enthusiast and have been able to make successful contributions to some of my favorite projects such as scikit-learn , mlflow, sagemaker etc.
Github : @whiletruelearn
Twitter : @whiletruelearn
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