Tori Tompkins

Tori Tompkins

Senior Data Science Consultant at Advancing Analytics

London, United Kingdom


Tori is a Senior AI Consultant at Advancing Analytics and Microsoft AI MVP. Specialising in MLOps and LLMOps, Tori has worked on many ML and data science projects with Azure and Databricks at all stages of the ML Lifecycle. She is a co-presenter of the Data & AI podcast, Totally Skewed, founder of Girls Code Too UK and regular contributor with Girls in Data.

Area of Expertise

  • Information & Communications Technology


  • Machine Leaning
  • Data Science
  • Graph Analytics
  • Microsoft Azure
  • MLOps

Unlock your MLOps potential with Azure Machine Learning Studio

MLOps is essential for bridging the gap between data science and operations, enabling organizations to deliver reliable and scalable machine learning solutions that drive real-world impact.

In this session, we will explore the powerful capabilities of Azure Machine Learning Studio (AML) and learn how to leverage its features to build robust and efficient end-to-end MLOps solutions. AML provides a comprehensive set of tools and services to support the entire ML lifecycle, from data preparation and model training to deployment and monitoring including advanced deployment techniques such as green/ blue deployment.

By the end of this session, you will possess a comprehensive understanding of Azure ML Studio and its potential for constructing end-to-end MLOps solutions. Whether you are a data scientist, ML engineer, or a DevOps professional, this session is designed to equip you with the essential tools, knowledge, and best practices needed to harness the true power of Azure ML Studio and drive successful MLOps initiatives in your organization.

Beyond the Model: POC to Production

"Despite the growing adoption of machine learning, research has shown that as many as 50-90% of machine learning models fail to make it into production due to a lack of planning, inadequate data, and the complexity of the models themselves."

This session is designed to provide attendees with an in-depth understanding of how to take a model from POC to production following the entire machine learning lifecycle. From model training to deployment and monitoring, covering all essential topics including feature store, MLflow, testing for accuracy and fairness, code vs model deployment, multiple patterns for batch and real-time deployment, and monitoring for drift.

During this day-long session, attendees will learn about the latest tools available in Databricks and Azure and hints and tips for best practices at every step in the process. They will also have the opportunity to engage in hands-on exercises and real-world examples to reinforce their understanding of the concepts discussed.

After this session, attendees will be equipped with the knowledge and practical skills needed to run successful MLOps projects and overcome the productionisation challenges faced by the industry.

Getting started with MLOps in Azure

We are being asked more and more to work with various aspects of data, regardless of our core skill set. This is particularly the case when productionising Machine Learning Models.

In this session we will talk about various Azure technologies we can use in our day jobs to achieve this, including ML Studio, Databricks & AKS.

We will look at the various components needed and different architectures that can be implemented, including how to manage Feature Stores and monitoring Model Life Cycles.

When working with data it is vital that different Data Professionals, Software Developers/Engineers & others in tech work in harmony together for successful outcomes. Consequently we will also cover how we can try to best achieve this, and how this has been done in the real world.

Detecting and Managing all types of Model Drift

Over time, machine learning models will degrade for a number of reasons. Maybe you have a book recommendation model but your customers preferences are changing, or maybe your customers behaviour has changed since the Covid-19 Lockdown. In this talk, I will cover the 4 types of model decay and the steps you can take to detect and mitigate against them.

Data Science and Analytics from the Trenches: Real-World Experience from Diverse voices in the field

In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.

Join this lively and interactive session to hear from the speakers to learn practical examples on how to be a more successful data scientist. Bring your questions for discussion!

Want End-to-End MLOps? Look no further than Databricks!

Arguably the largest challenge in ML today is effectively deploying reliable and efficient models into production, with experts quoting that as many as 90% of model created never make it to production. MLOps streamlines the process of taking machine learning models to production, and then maintaining and monitoring them. With new MLOps micro-venders popping up every day, is there a tool that does everything?

In this session, we will consider Databricks as an end-to-end MLOps tool, exploring collaborative workspaces, feature stores, model registries and model serving. We will also touch upon other critical MLOps practices such as model fairness, explainability and monitoring.

Including practical demos of Databricks Feature Store, MLFlow and real-time Model Serving, this session is suitable for Data Scientists and Machine Learning Engineers of all levels.

Empowering MLOps with Feature Stores

One rising challenge in ML is how can we manage and serve features at scale, enabling data scientists and engineers to efficiently create, store, and share features across different stages of the machine learning pipeline.

In this session, we will delve into the world of Feature Stores and their emerging role in MLOps. We will explore important concepts including feature engineering, feature versioning, feature serving, and feature metadata management. With practical demos in two leading Feature Store implementations, Databricks and Feast, we will explore the benefits, best practices, common challenges and pitfalls and how to address them.

Whether you are a data scientist, machine learning engineer, or data engineer, this session will provide valuable insights and practical demos to help you harness the power of Feature Stores in your organisation's MLOps journey.

SQLBits 2024 - General Sessions Sessionize Event

March 2024 Farnborough, United Kingdom

DATA:Scotland 2023 Sessionize Event

September 2023 Edinburgh, United Kingdom

Southampton Data Platform and Cloud user group - in-person meetup User group Sessionize Event

January 2023 Southampton, United Kingdom

Dativerse #2 Sessionize Event

October 2022

Data Relay 2022 Sessionize Event

October 2022

SQLBits 2022 Sessionize Event

March 2022 London, United Kingdom

Tori Tompkins

Senior Data Science Consultant at Advancing Analytics

London, United Kingdom


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