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:
How We Built a ML Model to Predict Proteins for Insecticidal Activity?
Session type: Short talk of 20 mins Session type: Full talk of 40 mins
To improve the crop plant yield, agriculture companies have successfully adopted development of insect resistant crops by expressing insecticidal (insect killing) proteins in plants. As a leader in Agriculture Biotechnology industry, Bayer tests hundreds of genes every year for insecticidal activity in their proprietary pipeline to develop next generation of insect control solutions. Identification and nomination insecticidal proteins using traditional methods like blast and structure similarity have some drawbacks because of which more than 90% of the nominated proteins end up displaying no or less activity against insects. The testing of these proteins consumes enormous amount of time and resource. So we adopted machine learning (ML) approach to identify these proteins. We generated numerous features for more than 5000 amino acid sequences using a Python toolkit, iFeature, developed by Chen et al, in 2018 and built ML models to identify proteins with insecticidal activity. Proteins identified using this method are tested in the pipeline to check their efficacy against insect pests. Challenges faced while building the model and methods to overcome those challenges are discussed in this presentation. The information in this presentation can be helpful for building models for bio-medical research (example cancer-related proteins, proteins in age-related diseases), agriculture and other domains.
- What are insecticidal proteins?
- Why machine learning for protein activity identification?
- Different approaches used by researchers
- Why not traditional methods?
- iFeature - a Python tool kit
5a. Why did we choose iFeature?
5b. What features iFeature has?
5c. How we adopted it for our need?
- Model performance
- Model managament
- What were the challenges?
- How did we overcome those?
- Where else this study can be applied?
Dr. Karnam Vasudeva Rao is presently working as Senior Scientist-Data Science, Bayer, Bengaluru, India since 2009. Prior to this Vasu has pursued his PhD from Max-Planck Institute For Biochemistry, Munich, Germany. He has enormous experience working in research organizations in India and abroad. He is involved in developing data science products in his organization and in mentoring budding Data Scientists within and outside the organization.