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:
Running ML Workflows using Airflow @ Walmart
Session type: Full talk of 40 mins
One of the most critical challenges in bringing Machine Learning to practice is to avoid the various technical debt traps which the data science teams focus on in their day to day jobs. Building a Machine Learning Platform at Walmart has a single agenda i.e. to make it easy for data scientists to use the company’s data to train/build new ML models at scale and making the deployment experience seamless.
The machine learning platform has many components like data connectors (for creating and managing data connections), notebooks (development time environment), workflows (to stich the notebooks together), models (save and load models), jobs (workflow batch and schedule jobs, distributed tensorflow and rapids jobs) and deployments (r shiny, python, tensor serve).
In this talk, I will cover the workflow components which is built on top of Apache Airflow and is one of the most used components in Walmart’s Machine Learning Platform. At the time of writing this, 1000+ DAGs are running in productions.
I would like to share the learning from setting up the airflow cluster in kubernetes and the workflow service written on top of this. From workflow, we are able to execute various batch jobs which includes launching distributed/non-distributed tensorflow jobs, distributed/non-distributed rapids jobs, Jupyter notebooks (python/Scala/spark), R studio jobs etc. The custom airflow plugins gives us capability to launch these notebooks/jobs. We have built a capability of launching parameterized notebooks/jobs using workflow. We have abstracted the complete workflow creation part by providing a GUI to create the workflow definition (DAG) and internally generating the python code to create the Airflow DAGs.
While building these components, the goal was to provide a platform where user can create notebooks and stich these parameterized notebooks together using a GUI based workflow. The workflow creation process is simple drag and drop of various notebook types and allows to set the local and global parameters. It also allows to pass the values from one notebook to another notebook in a workflow. I would elaborate primarily on how we have built the workflow system and how it interact with the notebook system to schedule the notebooks.
This talk reflects our journey over the past 1.5 years – as we went through the journey – starting from a just one notebook type and simple workflow to a system which supports a workflow system which includes operators to execute Jupyter notebooks, R studio notebooks, distributed/non-distributed rapids and tensorflow jobs.
Overview - Machine Learning Platform @ Walmart
Workflow - Requirements
What options we consider for workflow framework?
Workflow – Dag Designer
High Level Architecture: Workflow Service with Airflow Cluster (Celery Executor)
Configure Airflow for High Performance
High Level Architecture: Workflow Notebook Integration
airflow, kubernetes basics
Sachin Parmar is a Senior Architect with the GDAP (Global Data and Analytics Platforms) Group in Walmart Labs. From past 2 years, Sachin is member of the team which builds Machine Learning Platform for Walmart Labs. At Walmart, Sachin is responsible for building components like workflow and tools on top of tensorflow for Machine Learning Platform. Before working with Walmart, Sachin has worked with Yahoo! for around 8.5 years and with IBM Labs around 1.5 years.