Using Databricks, we built a “Unified Talent Solution” backed by a robust data and AI engine for analyzing skills of a combined pool of permanent employees, contractors, part-time employees and vendors, inferring skill gaps, future trends and recommended priority areas to bridge talent gaps, which ultimately greatly improved operational efficiency, transparency, commercial model, and talent experience of our client. We leveraged a variety of ML algorithms such as boosting, neural networks and NLP transformers to provide better AI-driven insights.

One inevitable part of developing these models within a typical DS workflow is iteration. Databricks' end-to-end ML/DS workflow service, MLflow, helped streamline this process by organizing them into experiments that tracked the data used for training/testing, model artifacts, lineage and the corresponding results/metrics. For checking the health of our models using drift detection, bias and explainability techniques, MLflow's deploying, and monitoring services were leveraged extensively.

Our solution built on Databricks platform, simplified ML by defining a data-centric workflow that unified best practices from DevOps, DataOps, and ModelOps. Databricks Feature Store allowed us to productionize our models and features jointly. Insights were done with visually appealing charts and graphs using PowerBI, plotly, matplotlib, that answer business questions most relevant to clients. We built our own advanced custom analytics platform on top of delta lake as Delta’s ACID guarantees allows us to build a real-time reporting app that displays consistent and reliable data - React (for front-end), Structured Streaming for ingesting data from Delta table with live query analytics on real time data ML predictions based on analytics data.

Talk by: Nitu Nivedita

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Streaming is the future of all data pipelines and applications. It enables businesses to make data-driven decisions sooner and react faster, develop data-driven applications considered previously impossible, and deliver new and differentiated experiences to customers. However, many organizations have not realized the promise of streaming to its full potential because it requires them to completely redevelop their data pipelines and applications on new, complex, proprietary, and disjointed technology stacks.

The Databricks Lakehouse Platform is a simple, unified, and open platform that supports all streaming workloads ranging from ingestion, ETL to event processing, event-driven application, and ML inference. In this session, we will discuss the streaming capabilities of the Databricks Lakehouse Platform and demonstrate how easy it is to build end-to-end, scalable streaming pipelines and applications, to fulfill the promise of streaming for your business.

Talk by: Zoe Durand and Yue Zhang

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Streaming is the future of all data pipelines and applications. It enables businesses to make data-driven decisions sooner and react faster, develop data-driven applications considered previously impossible, and deliver new and differentiated experiences to customers. However, many organizations have not realized the promise of streaming to its full potential because it requires them to completely redevelop their data pipelines and applications on new, complex, proprietary, and disjointed technology stacks.

The Databricks Lakehouse Platform is a simple, unified, and open platform that supports all streaming workloads ranging from ingestion, ETL to event processing, event-driven application, and ML inference. In this session, we will discuss the streaming capabilities of the Lakehouse Platform and demonstrate how easy it is to build end-to-end, scalable streaming pipelines and applications, to fulfill the promise of streaming for your business. You will also hear from Erica Lee, VP of ML at Upwork, the world's largest Work Marketplace, share how the Upwork team uses Databricks to enable real-time predictions by computing ML features in a continuous streaming manner.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

Kedro is an open-source framework for creating portable pipelines through modular data science code, and provides a powerful interactive visualisation tool called ‘Kedro-Viz’, a webapp that magically generates a highly powerful and informational visualisation of the pipeline.

In 2020, the Kedro project introduced an important set of features to support Modular Pipelines, which allows users to set up a series of pipelines that are logically isolated and re-usable to form higher level pipelines.

With this paradigm shift comes the need to reimagine the visualization of the pipeline on Kedro-viz, in that it needs to introduce a series of redesigns and new features to support this new representation of pipeline structure.

As a core contributor and team member to the Kedro-viz project throughout the past year, I have witnessed the journey of this transition through shipping the core features for modular pipelines on Kedro-viz.

This talk will focus on my experience as a front end developer as I walk through the unique architecture and data ingestion setup for this project. I will deep-dive into the unique set of problems and assumptions we have to make in accommodating this new modular pipeline setup, and our approach for solving them within a Front End(React + Redux) context.

Not to say I will definitely share the mistakes and learnings along the way, and how this paved the path towards the app architecture choices for our next set of features in ML experiment tracking.

This talk is for the curious data practitioner who is up for exposure to a fresh set of problems beyond the typical data science domain, and for those who are up for a ride through the mind-boggling details of the unique set up of front end development and data visualisation for data science.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/