Supercharge your Data Science team

Hosted alongside Google Cloud, New England

Wednesday, March 16, 2022
11:00 am
Boston, Massachusetts

Ever wonder why Data Scientists are one of the most in-demand roles out there? According to Richard Joyce, Senior Analyst at Forrester, “A 10% improvement in data accessibility for the average Fortune 1000 enterprise results in a revenue increase of $65 million dollars”. 

Although today’s enterprise is well aware of the value in data science, there is a disproportionate investment made in infrastructure vs talent. This has been observed first hand and is a recurring theme in conversations with our customers. 

The average data science team lacks a true production grade environment, something critical to work efficiently and productively and ultimately maximize the ROI of the data science practice. If you are going to take data science seriously, you must do the same for your infrastructure. 

The focus of this session is to explore the tools and products Google Cloud has developed and how they power production grade data science environments. You will supercharge your data science team like never before.


12:00 PM – 1:00 PM EDT Problem and Solution Discussions
1:00 PM – 2:00 PM EDT Solution Implementation
2:00 PM – 3:00 PM EDT Office Hours — Kahoot Quiz, Q&A, Strategy

What you'll learn

Explore challenges for Data Science teams:

  • Combating cases where data scientists spend majority of their time cleaning and preparing data, rather than high value activities like model development
  • Managing complexities of diverse datasets, types, and formats from disparate sources with varying update frequencies
  • Dealing with inconsistent methods to manage datasets, models, and model metadata; label, evaluate, and deploy models
  • Reducing manual steps and human intervention needed to train and deploy models
  • Preventing duplicate work and overlap, data quality regressions and drift

Cover Technical Topics

  • Data Unification, Reusability and Extensibility 
  • Pipeline orchestration for fully automated, reproducible pipelines covering data ingestion, cleaning, and pre-processing using Dataflow
  • Data Lakes for machine learning use cases using Cloud Storage, Pub/Sub, and BigQuery
  • Data tracking and model tracking 
  • Data extraction, data preprocessing, model training, and model deployment
  • Data version control

Cover Business Topics

  • Solutions for data science infrastructure shortcomings and team inefficiencies
  • Handling data complexity
  • Reducing manual, tedious low value tasks that computers should automate
  • Producing innovation and model IP faster


Saif Abid

Chief Technology Officer


Vadim Kacherov

Customer Engineer

Google Cloud

Abe Miller

Customer Engineer

Google Cloud
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