As healthcare organizations accelerate their adoption of AI and data-driven systems, the challenge lies not only in innovation but in responsibly scaling these technologies within clinical and operational workflows. This session examines the technical and governance frameworks required to translate AI research into reliable and compliant real-world applications. We will explore best practices in model lifecycle management, data quality assurance, bias detection, regulatory alignment, and human-in-the-loop validation, grounded in lessons from implementing AI solutions across complex healthcare environments. Emphasizing cross-functional collaboration among clinicians, data scientists, and business leaders, the session highlights how to balance technical rigor with clinical relevance and ethical accountability. Attendees will gain actionable insights into building trustworthy AI pipelines, integrating MLOps principles in regulated settings, and delivering measurable improvements in patient care, efficiency, and organizational learning.

Fifth Third Bank transformed its MLOps using a feature store built with dbt Cloud. The result? Improved model governance, reduced risk and faster product innovation. In this session, learn how they defined features, automated pipelines and delivered real-time and historical feature views.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack. Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer — systems thinking is.

We'll use concrete examples to walk through common failure modes in ML pipelines, highlight where analogies fall apart, and show how to build systems that tolerate failure, adapt to change, and support iteration without regressions.

Topics covered include: - Common failure modes in ML pipelines - Modular design: feature, training, inference - Built-in observability, versioning, reuse - Orchestration across batch, real-time, LLMs - Platform-agnostic patterns that scale

Key takeaways: - Resilience > diagrams - Separate concerns, embrace change - Metadata is your backbone - Infra should support iteration, not block it

In today’s financial sector, the continuous accuracy and reliability of machine learning models are crucial for operational efficiency and effective risk management. With the rise of MLOps (Machine Learning Operations), automating monitoring mechanisms has become essential to ensure model performance and compliance with regulations. This presentation introduces a method for continuous monitoring of model drift, highlighting the benefits of automation within the MLOps framework. This topic is particularly interesting because it addresses a common challenge in maintaining model performance over time and demonstrates a practical solution that has been successfully implemented in the bank.

This talk is aimed at data scientists, machine learning engineers, and MLOps practitioners who are interested in automating the monitoring of machine learning models. Attendees will be guided on how to continuous monitor model drift within the MLOps framework. They will understand the benefits of automation in this context, and gain insights into MLOps best practices. A basic understanding of MLOps principles, and statistical techniques for model evaluation will be helpful but not strictly needed.

The presentation will be an informative talk with a focus on the design and implementation. It will include some mathematical concepts but will primarily be demonstrating real-world applications and best practices. At the end we encourage you to actively monitor model drift and automate your monitoring processes to enhance model accuracy, scalability, and compliance in your organizations.

Imagine performing complex regulatory checks in minutes instead of days. We made this a reality using GenAI on the Databricks Data Intelligence Platform. Join us for a deep dive into our journey from POC to a production-ready AI audit tool. Discover how we automated thousands of legal requirement checks in annual reports with remarkable speed and accuracy. Learn our blueprint for: High-Performance AI: Building a scalable, >90% accurate AI system with an optimized RAG pipeline that auditors praise. Robust Productionization: Achieving secure, governed deployment using Unity Catalog, MLflow, LLM-based evaluation, and MLOps best practices. This session provides actionable insights for deploying impactful, compliant GenAI in the enterprise.

Ready to streamline your ML lifecycle? Join us to explore MLflow 3.0 on Databricks, where we'll show you how to manage everything from experimentation to production with less effort and better results. See how this powerful platform provides comprehensive tracking, evaluation, and deployment capabilities for traditional ML models and cutting-edge generative AI applications. Key takeaways: Track experiments automatically to compare model performance Monitor models throughout their lifecycle across environments Manage deployments with robust versioning and governance Implement proven MLOps workflows across development stages Build and deploy generative AI applications at scale Whether you're an MLOps novice or veteran, you'll walk away with practical techniques to accelerate your ML development and deployment.

Botnet attacks mobilize digital armies of compromised devices that continuously evolve, challenging traditional security frameworks with their high-speed, high-volume nature. In this session, we will reveal our advanced system — developed on the Databricks platform — that leverages cutting-edge AI/ML capabilities to detect and mitigate bot attacks in near-real time. We will dive into the system’s robust architecture, including scalable data ingestion, feature engineering, MLOps strategies & production deployment of the system. We will address the unique challenges of processing bulk HTTP traffic data, time-series anomaly detection and attack signature identification. We will demonstrate key business values through downtime minimization and threat response automation. With sectors like healthcare facing heightened risks, ensuring data integrity and service continuity is vital. Join us to uncover lessons learned while building an enterprise-grade solution that stays ahead of adversaries.

Don't miss this session where we demonstrate how the Texas Rangers baseball team is staying one step ahead of the competition by going back to the basics. After implementing a modern data strategy with Databricks and winnng the 2023 World Series the rest of the league quickly followed suit. Now more than ever, data and AI are a central pillar of every baseball team's strategy driving profound insights into player performance and game dynamics. With a 'fundamentals win games' back to the basics focus, join us as we explain our commmitment to world-class data quality, engineering, and MLOPS by taking full advantage of the Databricks Data Intelligence Platform. From system tables to federated querying, find out how the Rangers use every tool at their disposal to stay one step ahead in the hyper competitive world of baseball.

Deploying AI models efficiently and consistently is a challenge many organizations face. This session will explore how Vizient built a standardized MLOps stack using Databricks and Azure DevOps to streamline model development, deployment and monitoring. Attendees will gain insights into how Databricks Asset Bundles were leveraged to create reproducible, scalable pipelines and how Infrastructure-as-Code principles accelerated onboarding for new AI projects. The talk will cover: End-to-end MLOps stack setup, ensuring efficiency and governance CI/CD pipeline architecture, automating model versioning and deployment Standardizing AI model repositories, reducing development and deployment time Lessons learned, including challenges and best practices By the end of this session, participants will have a roadmap for implementing a scalable, reusable MLOps framework that enhances operational efficiency across AI initiatives.

DSPy is a framework for authoring GenAI applications with automatic prompt optimization, while MLflow provides powerful MLOps tooling to track, monitor, and productize machine learning workflows. In this lightning talk, we demonstrate how to integrate MLflow with DSPy to bring full observability to your DSPy development. We’ll walk through how to track DSPy module calls, evaluations, and optimizers using MLflow’s tracing and autologging capabilities. By the end, you'll see how combining these two tools makes it easier to debug, iterate, and understand your DSPy workflows, then deploy your DSPy program — end to end.

Join us as we dive into how Turnpoint Services, in collaboration with DataNimbus, built an Intelligence Platform on Databricks in just 30 days. We'll explore features like MLflow, LLMs, MLOps, Model Registry, Unity Catalog & Dashboard Alerts that powered AI applications such as Demand Forecasting, Customer 360 & Review Automation. Turnpoint’s transformation enabled data-driven decisions, ops efficiency & a better customer experience. Building a modern data foundation on Databricks optimizes resource allocation & drives engagement. We’ll also introduce innovations in DataNimbus Designer: AI Blocks: modular, prompt-driven smart transformers for text data, built visually & deployed directly within Databricks. These capabilities push the boundaries of what's possible on the Databricks platform. Attendees will gain practical insights, whether you're beginning your AI journey or looking to accelerate it.

This in-depth session explores advanced MLOps practices for implementing production-grade machine learning workflows on Databricks. We'll examine the complete MLOps journey from foundational principles to sophisticated implementation patterns, covering essential tools including MLflow, Unity Catalog, Feature Stores and version control with Git. Dive into Databricks' latest MLOps capabilities including MLflow 3.0, which enhances the entire ML lifecycle from development to deployment with particular focus on generative AI applications. Key session takeaways include: Advanced MLflow 3.0 features for LLM management and deployment Enterprise-grade governance with Unity Catalog integration Robust promotion patterns across development, staging and production CI/CD pipeline automation for continuous deployment GenAI application evaluation and streamlined deployment

Struggling to implement traditional machine learning models that deliver real business value? Join us for a hands-on exploration of classical ML techniques powered by Databricks' Mosaic AI platform. This session focuses on time-tested approaches like regression, classification and clustering — showing how these foundational methods can solve real business problems when combined with Databricks' scalable infrastructure and MLOps capabilities. Key takeaways: Building production-ready ML pipelines for common business use cases including customer segmentation, demand forecasting and anomaly detection Optimizing model performance using Databricks' distributed computing capabilities for large-scale datasets Implementing automated feature engineering and selection workflows Establishing robust MLOps practices for model monitoring, retraining and governance Integrating classical ML models with modern data processing techniques

As machine learning (ML) models scale in complexity and impact, organizations must establish a robust MLOps foundation to ensure seamless model deployment, monitoring and retraining. In this session, we’ll share how we leverage Databricks as the backbone of our MLOps ecosystem — handling everything from workflow orchestration to large-scale inference. We’ll walk through our journey of transitioning from fragmented workflows to an integrated, scalable system powered by Databricks Workflows. You’ll learn how we built an automated pipeline that streamlines model development, inference and monitoring while ensuring reliability in production. We’ll also discuss key challenges we faced, lessons learned and best practices for organizations looking to operationalize ML with Databricks.

Adopting MLOps is getting increasingly important with the rise of AI. A lot of different features are required to do MLOps in large organizations. In the past, you had to implement these features yourself. Luckily, the MLOps space is getting more mature, and end-to-end platforms like Databricks provide most of the features. In this talk, I will walk through the MLOps components and how you can simplify your processes using Databricks. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

Unlock the power of Amazon SageMaker Studio, a comprehensive IDE for streamlining the machine learning (ML) lifecycle. Explore data exploration, transformation, automated feature engineering with AutoML, and collaborative coding using integrated Jupyter Notebooks. Discover how SageMaker Studio MLOps integration simplifies model deployment, monitoring, and governance. Through live demos and best practices, learn to leverage SageMaker Studio tools for efficient feature engineering, model development, collaboration, and data security.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

Amazon SageMaker provides purpose-built tools to create a reliable path to production for both machine learning and generative AI workflows. SageMaker MLOps helps you automate and standardize processes across generative AI and ML lifecycles. Using SageMaker, you can train, test, troubleshoot, deploy, and govern models at scale to boost your productivity while maintaining model performance in production. Explore the latest and greatest capabilities such as SageMaker Experiments with MLflow, SageMaker Pipelines, and SageMaker Model Registry supporting efficiencies in your ML workflow (MLOps) and generative AI workflows (FMOps). Learn how to bring generative AI concept to production quickly and securely.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

DevOps and MLOps are both software development strategies that focus on collaboration between developers, operations, and data science teams. In this session, learn how to build modern, secure MLOps using AWS services and tools for infrastructure and network isolation, data protection, authentication and authorization, detective controls, and compliance. Discover how AWS customer PathAI, a leading digital pathology and AI company, uses seamless DevOps and MLOps strategies to run their AISight intelligent image management system and embedded AI products to support anatomic pathology labs and biopharma partners globally.

Learn more about AWS re:Inforce at https://go.aws/reinforce.

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts.

AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

reInforce2024 #CloudSecurity #AWS #AmazonWebServices #CloudComputing

Big Data Europe Onsite and online on 22-25 November in 2022 Learn more about the conference: https://bit.ly/3BlUk9q

Join our next Big Data Europe conference on 22-25 November in 2022 where you will be able to learn from global experts giving technical talks and hand-on workshops in the fields of Big Data, High Load, Data Science, Machine Learning and AI. This time, the conference will be held in a hybrid setting allowing you to attend workshops and listen to expert talks on-site or online.