TMLS Machine Learning Virtual Micro-Summit - Government Sector

TMLS events bring together business leaders, researchers and applied ML practitioners.TMLS is a community of over 5,000 practitioners, researchers, entrepreneurs and executives. We work to highlight global opportunities and foster growth in local ecosystems.

Toronto Machine Learning Society (TMLS)

Tickets still available at https://bit.ly/MicrosoftGovSummit

<p><strong>NOTE: Workshops are hosted on the 12th and talks on the 13th of April.</strong></p><p>Speakers will explore applications of Machine Learning from both the business and technical areas of expertise.</p>

<h3>Thank you to our Conference Co-chairs;</h3>

<h2>Azure Landing Zones for Canadian Public Sector</h2><h3>Senthuran Sivananthan, Principal Cloud Solution Architect and Hubert Duan, Data Scientist, Microsoft</h3><p></p><h3>Abstract: </h3><p>Azure Landing Zones for Canadian Public Sector is a prescriptive reference implementation for Canadian Public Sector using Protected B Medium/Medium &amp; NIST 800-53 Rev 4 compliance policies. The implementation is based on guidance from Cloud Adoption Framework &amp; Azure Landing Zone reference architecture. The goal of the project is to support organizations with achieving Authority to Operate faster while maintaining ongoing compliance, governance and security monitoring through Azure-native services.</p><h3></h3><h3>What you will learn: </h3><p>In this presentation, you will learn the core design principles, the Azure Landing Zones architecture and the automation developed through Azure DevOps to deploy, configure and manage an Azure environment. We will demo a Machine Learning pattern that is used to create and run models using Databricks, Azure Machine Learning, Azure Kubernetes Service and Azure App Service.</p><p></p><p></p><h2>Ingestion, Processing, and Analysis of GIS Data Sources on Azure</h2><h3>Hubert Duan, Data Scientist, Microsoft</h3><p></p><h3>Abstract: </h3><p>(Longer form to come) I will discuss in this presentation a reference architecture with hands-on demos to support large-scale ingestion, processing, and analysis of GIS data sources on Azure</p><p></p><h3>What you will learn: </h3><p>How to ingest, process, and analyze GIS data sources on Azure</p><p></p><p></p><h2>Operationalizing ML for Next Bus Departure Time Predictions</h2><h3>Winston Zhou, Solution Architect and Ron Mok, Software Development Manager, TransLink</h3><p></p><h3>Abstract: </h3><p>TransLink uses over 18000 ML models to provide accurate bus departure time predictions in real-time. In this session, we will share our challenges and how we overcame them. This includes meeting performance requirements for making predictions in real-time, handling cases where models are not available, training/versioning models at scale, drift detection, accuracy reporting and more. Content will be presented at the architectural level.</p><p></p><h3>What you will learn: </h3><ul><li>Meeting performance requirements for making multiple model predictions in real-time </li><li>Handling cases where models are not available. This can be due to insufficient training data or the fact that the models are no longer relevant e.g. models were trained under normal road traffic conditions but during COVID, the streets were empty </li><li>Planning the grain of our models so we can integrate various business rules. </li><li>Training/versioning models at scale (18000 models) – ML OPS </li><li>Isolating models from application code so updating models do not require redeployment of the application and vice versa </li><li>Use of drift detection to selectively retrain models</li><li> Reporting – how we monitor the accuracy of our models.</li></ul><p></p><p></p><p></p><h2>Building a Culture of Responsible AI</h2><h3>Ellie Sakhaee, Responsible AI, Microsoft</h3><p></p><h3>Abstract: </h3><p>At Microsoft, responsible AI governance is crucial to guiding our AI innovation and creating AI that warrants people’s trust. We are putting our AI principles into practice by taking a people-centered approach to the research, development, and deployment of AI. In this talk, we will talk about our journey, Microsoft's AI principles, the building blocks of operationalizing these principles across the company and creating a culture of responsible AI.</p><p></p><h3>What you will learn: </h3><p>Learn more about Microsoft's AI principles and our approach to putting these principles into practice.</p><p></p><p></p><h2>Machine Learning Using Weather Data</h2><h3>Rachel Mintz, Acting Chief Data Officer, Environment and Climate Change Canada</h3><p></p><h3>Abstract: </h3><p>Environment and Climate Change Canada produces an abundance of weather data, including monitored observations and predicted weather elements using Numerical Weather Prediction Models. This presentation will give a general overview of the types of weather data available and provide some concepts and examples as to how the weather data can be used in a machine learning context.</p><p></p><h3>What you will learn: </h3><ul><li>What is weather observation data? </li></ul><ul><li>What is Numerical Weather Prediction data? </li></ul><ul><li>How can the data be used for machine learning? </li></ul><ul><li>What are some examples? </li></ul><ul><li>How does a person access the data?</li></ul><p></p><p></p><h2>The Data Science Engine as an Agent of Institutional Change</h2><h3> Jason Solomon, Chief Data Science Solution Architecture, Statistics Canada</h3><p></p><h3>Abstract: </h3><p>Data Science in isolation of agency activities limits the capabilities and impact of this powerful tool on services delivered to citizens. By creating a Data Science Engine within your organisation you can embed the ideals of a data first strategy that can see your organization adopt the most modern concepts of entrepreneurial thinking, agile and proof-of-concepts over traditional project managent and long delivery times. Key is culture, but does culture drive innovation, or does innovation drive the culture?.</p><p></p><h3>What you will learn: </h3><ul><li>Example of a sustainable Data Science Engine;</li><li>How to institutionalize data science in your organization</li><li>What does this look like in practice?</li></ul><p></p><p></p><p></p><h2>Deep Learning Utilization at the CFIA,</h2><h3>Noureddine Meraihi, AI Manager, Canadian Food Inspection Agency; Amir Kalantari and Geetika Sharma, Sr. Data Scientist | ML Engineer, Government of Canada</h3><p></p><h3>Abstract: </h3><p>In recent years, with the availability of compute resources and the plethora of data available, Deep Learning has moved from an academic interest to a powerful tool for businesses and governments to use. With the right resources, Deep Learning methods can yield a higher accuracy and lower cost with respect to the methods they succeed. </p><p></p><h3>What you will learn: </h3><p>Some of the applications of Deep Learning within the CFIA\</p><p></p><p></p><h2>From Pickup to Delivery: Demand Forecasting at Canada Post</h2><h3>David De Vito and  Ben Polasek, Data Scientists, Canada Post</h3><h3></h3><h3>Abstract: </h3><p>In 2020, COVID-19 had a major impact on many aspects of our lives; this lead to an increase in e-commerce shopping, and thereby a massive increase in parcel volumes. Canada Post had to rapidly react to this new reality by adapting its typical operating strategies to accommodate the sudden and sustained increase in volume. To efficiently and successfully operate, all areas of the business need to coordinate their planning and decision-making under a single source of data. </p><p>In this talk we will discuss our approach to consolidating various information and data sources in building demand forecasts. These forecasts can be leveraged for planning and decision-making across diverse groups within Canada Post. We will discuss the time-series forecasting approach that we are currently leveraging, as well as the plan for future forecasting at Canada Post.</p><h3></h3><h3>What you will learn: </h3><p>A brief background on Canada Post and our current/future approach to parcel demand forecasting.</p><p></p><p></p><p></p><h2>Responsible Machine Learning Operations in Action</h2><h3>Mohammad Ghodratigohar, Data Scientist, Microsoft</h3><h3></h3><h3>Abstract: </h3><p>Throughout the development and use of machine learning models, the end-to-end MlOps process should addresses specific risks of AI with a focus on risk-adjusted value delivery and efficiency in AI scaling in alignment with regulations. Responsible machine learning contains values and principles which at the core encompasses understanding explainability and fairness of model. This is critical for data scientists, auditors, and business decision-makers alike to ensure compliance with company policies, industry standards, and government regulations in their MLOps process. </p><p>This session will talk about how, where and when responsible AI principles can be applied in different MlOps steps through showcasing hands-on demo that uses the open-source techniques for assessing explainability and fairness of trained model.</p><h3></h3><h3>What you will learn: </h3><ul><li>What is Responsible AI in actio</li><li>How each component of Responsible AI can be applied and considered in MLOps</li><li>How some of Responsible AI toolkits fit into MLOps process</li><li>Actual example of applying Responsible AI toolkits in Azure Machine learning</li></ul><p></p><p></p><h2>City of Laval Speeds Up Community Response Systems(311) With An Azure AI Solution</h2><h3>Patrick Taillon, IT Analyst and Innovation Adviser, City of Laval</h3><h3></h3><h3>Abstract: </h3><p>City of Laval - Microsoft Customer Stories One of the largest cities in Quebec, Ville de Laval, is focused on developing smart city initiatives. One such project is the infusion of AI and machine learning into its 311 non-emergency hotline. The system, which fields nearly 200,000 inquiries a year, has begun using a virtual agent to both speed citizen–agent interactions. https://customers.microsoft.com/en-us/story/1409585222396616040-laval-gov-azure-canada</p><h3></h3><h3>What you will learn: </h3><ul><li>Why we choose Voice to text over chat bot in our solutions</li><li>The result of our experimentation with AI Tools and why we choose Azure AI for our solutions </li><li>Where did we implement the use of AI in our business process to generate added value for the citizen </li><li>What are the main obticles we face during the adoption of AI in our organisation </li><li>How did we engage employees to participate in our project.</li></ul>

Reshaping the Future: Managing Different Expectations on Women in AI

Northeastern University, Toronto Campus

Toronto Machine Learning Summit (TMLS) 10th Annual Conference & Expo 2026

CIBC SQUARE

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TMLS Machine Learning Virtual Micro-Summit - Government Sector

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