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# RAG
![Markus Stoll](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/me2-removebg-preview-9ee0250f-a803-445b-aee3-0aad921430b5-1725381808850.png?fit=scale-down&width=52)
Markus Stoll · Sep 3rd, 2024
This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models.
# Data Visualization
# RAG
# Renumics
![Sean Morgan](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Sean-Morgan-d66af4bb-e86d-4289-b5bd-51238707bdb6-1725032467627.jpeg?fit=scale-down&width=52)
![Demetrios Brinkmann](https://cdn.gradual.com/images/https://d92co48ro6fll.cloudfront.net/gradual/mlops/speakers-v1/Demetrios-Brinkmann.jpg?fit=scale-down&width=52)
Sean Morgan & Demetrios Brinkmann · Aug 30th, 2024
MLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly how you can improve that security posture through the use of MLSecOps.
# MLSecOps
# AISPM
# Protect AI
![Jonathan Bennion](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Screenshot-2024-08-27-at-11-47-59-AM-f5799229-5cb2-4a37-89a7-1293cb1e6951-1724939347575.png?fit=scale-down&width=52)
Jonathan Bennion · Aug 29th, 2024
GraphRAG (by way of Neo4j in this case) enhances faithfulness (a RAGAS metric most similar to precision) when compared to vector-based RAG, but does not significantly lift other RAGAS metrics related to retrieval; may not offer enough ROI to justify the hype of the accuracy benefits given the performance overhead.
# GraphRAG
# Retrieval Database
# Vector Database
# The Objective AI
![Harcharan Kabbay](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/1659302575272-f26a415b-2c68-447c-97cf-43dc3e8008d6-1724780504728.jpeg?fit=scale-down&width=52)
![Demetrios Brinkmann](https://cdn.gradual.com/images/https://d92co48ro6fll.cloudfront.net/gradual/mlops/speakers-v1/Demetrios-Brinkmann.jpg?fit=scale-down&width=52)
Harcharan Kabbay & Demetrios Brinkmann · Aug 27th, 2024
The discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models.
The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications.
A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures.
Let's talk about the power of Kubernetes and related tools to aid a good application design.
The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be re-used, also templates for pull requests, thereby minimizing human errors and streamlining the development process.
# GenAI Applications
# RAG
# CI/CD Pipeline
![Korri Jones](https://cdn.gradual.com/images/https://d92co48ro6fll.cloudfront.net/gradual/mlops/speakers-v1/Korri-Jones.jpg?fit=scale-down&width=52)
![Sonam Gupta](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Sonam-Gupta-bfc71ed1-cb22-4082-b563-440463a641ee-1717078998050.jpeg?fit=scale-down&width=52)
![Nehil Jain](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Nehil-Jain-a13d9c4b-fe75-4117-8043-2909800207e1-1720692660220.jpeg?fit=scale-down&width=52)
+1
Korri Jones, Sonam Gupta, Nehil Jain & 1 more speaker · Aug 26th, 2024
# Long Context Language Models
# RAG
# SQL
![Nicolas Mauti](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Reportlinker-29-cbed637c-d180-4a7d-9d83-0583bd88a9c2-1724427813447.png?fit=scale-down&width=52)
![Demetrios Brinkmann](https://cdn.gradual.com/images/https://d92co48ro6fll.cloudfront.net/gradual/mlops/speakers-v1/Demetrios-Brinkmann.jpg?fit=scale-down&width=52)
Nicolas Mauti & Demetrios Brinkmann · Aug 23rd, 2024
Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system.
We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all!
# BigQuery
# Feature Store
# Malt
![Andy McMahon](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Andy-McMahon-a7f75a76-f85b-473b-a6e9-a51185fa1cd2-1704800660089.jpeg?fit=scale-down&width=52)
![Demetrios Brinkmann](https://cdn.gradual.com/images/https://d92co48ro6fll.cloudfront.net/gradual/mlops/speakers-v1/Demetrios-Brinkmann.jpg?fit=scale-down&width=52)
Andy McMahon & Demetrios Brinkmann · Aug 20th, 2024
As we move from MLOps to LLMOps we need to double down on some fundamental software engineering practices, as well as augment and add to these with some new techniques. In this case, let's talk about this!
# MLOps
# LLMOps
# Barclays
![Ian Eisenberg](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/Ian-Eisenberg-3ddeefb3-020c-4472-a51a-4011732f2057-1717766018340.jpeg?fit=scale-down&width=52)
Ian Eisenberg · Aug 16th, 2024
This talk presents a comprehensive overview of enterprise AI governance, highlighting its importance, key components, and practical implementation stages. As AI systems become increasingly prevalent in business operations, organizations must establish robust governance frameworks to mitigate risks, ensure compliance, and foster responsible AI innovation. I define AI governance, and articulates its relationship to AI risks and a common set of emerging regulatory requirements. I then outline a three-stage approach to enterprise AI governance: organization-level governance, intake, and ongoing governance. At each stage I give examples of actions that support effective oversight, and articulate how they are actually operationalized in practice.
![Paco Nathan](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/paco-6df15419-242c-44a5-a6c2-70c891517ffa-1704380173675.png?fit=scale-down&width=52)
Paco Nathan · Aug 16th, 2024
Knowledge graphs have spiked recently in popular use, for example in _retrieval augmented generation_ (RAG) methods used to mitigate hallucination in LLMs. Graphs emphasize _relationships_ in data, adding _semantics_ — more so than with SQL or vector databases. However, data quality issues can degrade linking during KG construction and updating, which makes downstream use cases inaccurate and defeats the point of using a graph. When you have join keys (unique identifiers) building relationships in a graph may be straightforward, although false positives (duplicate nodes) can result from: typos or minor differences in attributes like name, address, phone, etc.; family members sharing email; duplicate customer entries, and so on. This talk describes what an _Entity Resolved Knowledge Graph_ is, why it's important, plus patterns for deploying _entity resolution_ (ER) which are proven to work. We'll cover how to make graphs more meaningful in data-centric architectures by repairing connected data: Unify connected data from across multiple data sources. Consolidate duplicate nodes and reveal hidden connections. Create more accurate, intuitive graphs which provide greater downstream utility for AI applications.
![Mona Rakibe](https://cdn.gradual.com/images/https://d2xo500swnpgl1.cloudfront.net/uploads/mlops/IMG-4626-1--8b8ac93a-accb-4cb6-b66a-213ba1317e3f-1693318834860.jpeg?fit=scale-down&width=52)
Mona Rakibe · Aug 16th, 2024
Often, the data stored for AI workloads is in its raw formats and stored in data lakes with open formats. This talk will focus on designing a data quality strategy for these raw formats.
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