Accelerating the Machine Learning Lifecycle with Data Reply

July 14, 2022 | from 5:00 PM to 6:30 PM
Amazon HQ, London


Before filling out the registration form, please read the Privacy notice pursuant to Article 13 of EU Regulation 2016/679
Invalid Input
Invalid Input
Invalid Input
Invalid Input
Invalid Input
Invalid Input

Privacy

I declare that I have read and fully understood the Privacy Notice and I hereby express my consent to the processing of my personal data by Reply SpA for marketing purposes, in particular to receive promotional and commercial communications or information regarding company events or webinars, using automated contact means (e.g. SMS, MMS, fax, email and web applications) or traditional methods (e.g. phone calls and paper mail).

EVENT OVERVIEW

Join Data Reply and AWS as they explore typical challenges around machine learning productionalisation and scaling and how MLOps helps to accelerate time to value and improve ROI on ML. This informal event will take place in Amazon's trendy Shoreditch HQ and we will be accepting guests on a first come first served basis.

WHO SHOULD ATTEND?

CIOs, CDOs, Heads of Data/Analytics/Data Science/Machine Learning/ Platform Engineering​

WHY ATTEND?

Most organisations have now adopted Machine Learning (ML) and are seeing the business value it could bring but are struggling to scale it. Without proper methodologies in place to introduce automation and governance in ML, approximately 70% of projects never makes it to production. Additionally, data scientists spend around 80% of their time with tasks such as data cleansing.

In partnership with AWS, we at Data Reply have organised this event to highlight the way in which MLOps brings DevOps principles into machine learning projects to drive success through automation, improved governance, repeatability and collaboration. MLOps enables organisations to move past Machine Learning experimentation and into the operationalisation and scaling stages, accelerating time to value and improving ROI on ML.

In this event we will be joined by an AWS MLOps expert and by two of our clients, a marketing and communications agency, Indicia Worldwide and a multinational wealth management company, St. James’s Place, to:
  • Discuss key challenges, trends and best practices in MLOps
  • Hear a real life story about how our clients used MLOps to reduce time-to-market, scale ML-based applications throughout their organisations, improve customer experience and ROI on ML
  • Enjoy peer-to-peer networking over drinks

AGENDA

5:00 PM Drinks reception

5:30 PM Session Begins

    1. Introduction
    2. MLOps Overview: Key Challenges & Trends
    3. Indicia Worldwide  Success Story
    4. St. James's Place Success Story
    5. Data Reply MLOps Approach
    6. Sagemaker Demo
    7. Q&A

6:15 PM Drinks & Networking

Speakers

  • Sokratis Kartakis

    Snr Solutions Architect

    AWS

  • Nil Patel

    Global Data and Analytics Director
    Indicia Worldwide

  • Graham Lannigan

    Head of Data Platform

    Indicia Worldwide

  • James Frew

    Divisional Director - Operational Strategy - Business Development and Advice
    St. James’s Place

  • Luca Piccolo

    Manager

    Data Reply

  • Aishwarya Sutreja

    Senior Consultant

    Data Reply

LOCATION AND TIME

Amazon HQ

1 Principal Place, Worship St, London EC2A 2FA

Thursday July 14th
5:00 – 6:30PM



Data Reply is a Reply Group company, a premier AWS partner, offering a broad range of advanced analytics, AI ML and data processing services. We operate across different industries and business functions, enabling our customers to achieve meaningful business outcomes through effective use of data, accelerating innovation and time to value.



Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centres globally. Millions of customers—including the fastest-growing start-ups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.