You’ve heard good data matters in Machine Learning, but does it matter for Generative AI applications? Corporate data often differs significantly from the general Internet data used to train most foundation models. Join me for a demo on building an open source RAG (Retrieval Augmented Generation) stack using Milvus vector database for Retrieval, LangChain, Llama 3 with Ollama, Ragas RAG Eval, and optional Zilliz cloud, OpenAI.
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
An introduction to Unstructured Data and the world of Vector Databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture.
Get full visibility and find hidden security issuesElasticsearch
When even basic threats can be multi-staged and complex, limited visibility into your security data just doesn’t cut it. Whether you’re performing investigations or hunting for threats, you need all security-relevant context. Learn key practices in data collection and normalisation and see how you can use Elastic Security to quickly and accurately triage, verify, and scope issues.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Managing your black friday logs - Code EuropeDavid Pilato
The document discusses optimally configuring Elasticsearch clusters for ingesting time-based data like logs. It recommends using time-based indices with a new index created each day. It also discusses techniques for scaling clusters by adding more shards as data volumes increase and distributing the data across nodes to avoid bottlenecks. The optimal bulk size for indexing may vary depending on factors like document size and should be tested.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
This document discusses how Netflix uses Spark and GraphX to power its recommender system at scale. It describes two machine learning problems - generating item rankings using graph diffusion algorithms like Topic Sensitive PageRank, and finding item clusters using LDA. It shows how these algorithms can be implemented iteratively in GraphX by representing the data as graphs and propagating vertex attributes. Performance comparisons show GraphX can outperform alternative implementations for large datasets due to its parallelism. Lessons learned include the importance of regular checkpointing and that multicore implementations are efficient for smaller datasets that fit in memory.
Managing your Black Friday Logs NDC OsloDavid Pilato
Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.
Topics include:
* monitoring architectures
* optimal bulk size
* distributing the load
* index and shard size
* optimizing disk IO
Takeaway: best practices when building a monitoring system with the Elastic Stack, advanced tuning to optimize and increase event ingestion performance.
The document discusses MySQL InnoDB Cluster, which provides high availability and scaling features for MySQL. It uses Group Replication under the hood, which allows data to be written simultaneously across cluster nodes while maintaining consistency. By default, MySQL InnoDB Cluster runs in Single Primary Mode, where one node acts as the primary/writable node and others act as hot standbys through an automated leader election process.
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
An introduction to Unstructured Data and the world of Vector Databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture.
Get full visibility and find hidden security issuesElasticsearch
When even basic threats can be multi-staged and complex, limited visibility into your security data just doesn’t cut it. Whether you’re performing investigations or hunting for threats, you need all security-relevant context. Learn key practices in data collection and normalisation and see how you can use Elastic Security to quickly and accurately triage, verify, and scope issues.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Managing your black friday logs - Code EuropeDavid Pilato
The document discusses optimally configuring Elasticsearch clusters for ingesting time-based data like logs. It recommends using time-based indices with a new index created each day. It also discusses techniques for scaling clusters by adding more shards as data volumes increase and distributing the data across nodes to avoid bottlenecks. The optimal bulk size for indexing may vary depending on factors like document size and should be tested.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
This document discusses how Netflix uses Spark and GraphX to power its recommender system at scale. It describes two machine learning problems - generating item rankings using graph diffusion algorithms like Topic Sensitive PageRank, and finding item clusters using LDA. It shows how these algorithms can be implemented iteratively in GraphX by representing the data as graphs and propagating vertex attributes. Performance comparisons show GraphX can outperform alternative implementations for large datasets due to its parallelism. Lessons learned include the importance of regular checkpointing and that multicore implementations are efficient for smaller datasets that fit in memory.
Managing your Black Friday Logs NDC OsloDavid Pilato
Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.
Topics include:
* monitoring architectures
* optimal bulk size
* distributing the load
* index and shard size
* optimizing disk IO
Takeaway: best practices when building a monitoring system with the Elastic Stack, advanced tuning to optimize and increase event ingestion performance.
The document discusses MySQL InnoDB Cluster, which provides high availability and scaling features for MySQL. It uses Group Replication under the hood, which allows data to be written simultaneously across cluster nodes while maintaining consistency. By default, MySQL InnoDB Cluster runs in Single Primary Mode, where one node acts as the primary/writable node and others act as hot standbys through an automated leader election process.
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...Neo4j
This document discusses using graph databases, graph data science, and generative AI to unlock insights from connected data. It highlights how relationships in data are valuable, and how graph databases provide an intuitive way to represent and query relationship data. The document introduces Neo4j's graph database capabilities, including graph algorithms for analytics, machine learning on graphs, and integration with other data systems. It also discusses using Neo4j to ground language models for more accurate generative AI applications.
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...Neo4j
This document discusses how Neo4j 5's Clustering and Fabric features can help organizations operate Neo4j databases at large scale. Clustering allows elastic horizontal scaling of resources across multiple servers to support more and larger databases. Fabric enables querying across databases, including those sharded across clusters. Two financial use cases will be presented to illustrate how Clustering and Fabric can support real-time decision making across business graphs and make multi-terabyte datasets more manageable through sharding.
Mysql NDB Cluster's Asynchronous Parallel Design for High PerformanceBernd Ocklin
MySQL's NDB Cluster is a partitioned distributed database engine that is entirely build around a parallel virtual machine with an event driven asynchronous design. Using this design NDB can execute even single queries in parallel and scales linearly handling terabytes of sharded data in a real-time fashion.
[Heap con19] designing data intensive applications in serverless architectureNikolay Matvienko
https://heapcon.io/speakers/nikolay-matvienko/designing-data-intensive-applications-in-serverless-architecture/
I’ll talk about how to design serverless architecture for a data intensive application in order to process a Data Lake or data streams. I’ll show you how we did it using AWS cloud functions on Node.js running on thousands functions in parallel that process terabytes of data in ETL pipeline. Step by step we will build a serverless architecture for processing pipeline, consider the choice of services, queues, streams, databases and dive into tuning of cloud functions to build a reliable massively scalable cloud computing platform. We will talk about the advantages of such an architecture and platform, its possible limitations and how to get around them.
Active Record 4.0 includes all sorts of exciting support for PostgreSQL! In this presentation, I show many of these improvements, and discuss why these are important for Web developers. If you haven't yet adopted PostgreSQL, now might be a great time and chance to do so.
Kfir Bloch discusses considerations for decomposing monolithic applications into microservices at Wix. He outlines four main reasons for decomposing: 1) to isolate resources for high availability, 2) to support different release cycles, 3) to reuse and share logic, and 4) to have single team responsibility over services. Bloch also discusses mitigations for challenges like partial deployments and increased failure points. He emphasizes starting the decomposition gradually and monitoring services from the beginning.
MySQL Document Store - when SQL & NoSQL live together... in peace!Frederic Descamps
Frédéric Descamps gave a demonstration of MySQL Document Store, showing how it allows both SQL and NoSQL functionality. He migrated sample data from MongoDB to MySQL Document Store and performed queries and CRUD operations. The conclusion is that MySQL Document Store provides the best of both worlds by combining schemaless and flexible data with ACID compliance, SQL capabilities, and data integrity.
IRJET- Efficient Geometric Range Search on RTREE Occupying Encrypted Spatial ...IRJET Journal
This document proposes a system to securely store and search spatial data in the cloud using an R-tree index. The system aims to protect data confidentiality and the privacy of data owners. Specifically:
1) A data owner encrypts their spatial data using DES encryption and uploads it to an R-tree index in the cloud.
2) A trusted third party is used to improve trust and detect any malicious modifications to the data.
3) When a search user performs a geometric range search, the third party retrieves the encrypted results and returns them to the user. The user must then request the decryption key from the data owner to decrypt and view the actual data.
This system aims to provide secure
Designing scalable application: from umbrella project to distributed system -...Elixir Club
This document discusses designing scalable applications from an umbrella project to a distributed system. It presents a machine learning tools demo project with four applications organized under an umbrella project. It describes using interfaces modules to encapsulate applications and enable inter-application communication as the system scales to multiple nodes. Example communication methods covered include RPC for synchronous calls, distributed tasks for asynchronous work, and HTTP for a third-party API. The document also discusses limiting concurrency using a poolboy worker pool.
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...MLconf
Spark and GraphX in the Netflix Recommender System: We at Netflix strive to deliver maximum enjoyment and entertainment to our millions of members across the world. We do so by having great content and by constantly innovating on our product. A key strategy to optimize both is to follow a data-driven method. Data allows us to find optimal approaches to applications such as content buying or our renowned personalization algorithms. But, in order to learn from this data, we need to be smart about the algorithms we use, how we apply them, and how we can scale them to our volume of data (over 50 million members and 5 billion hours streamed over three months). In this talk we describe how Spark and GraphX can be leveraged to address some of our scale challenges. In particular, we share insights and lessons learned on how to run large probabilistic clustering and graph diffusion algorithms on top of GraphX, making it possible to apply them at Netflix scale.
The document discusses managing logs for Black Friday in Elasticsearch. It covers the Elastic Stack components including Beats, Logstash, Elasticsearch and Kibana. It then discusses monitoring architectures, techniques for optimally sizing Elasticsearch clusters and shards, optimizing bulk indexing size, and distributing load across nodes. The presentation aims to provide guidance on log management strategies for handling high volume traffic periods like Black Friday.
Evolution from EDA to Data Mesh: Data in Motionconfluent
Thoughtworks Zhamak Dehghani observations on these traditional approaches’s failure modes, inspired her to develop an alternative big data management architecture that she aptly named the Data Mesh. This represents a paradigm shift that draws from modern distributed architecture and is founded on the principles of domain-driven design, self-serve platform, and product thinking with Data. In the last decade Apache Kafka has established a new category of data management infrastructure for data in motion that has been leveraged in modern distributed data architectures.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
If you understand the rule engine, especially how works RETE algorithm, You may use this for Machine Learning. This slide used at Red Hat Forum Tokyo 2018 session.
The document discusses various techniques for optimizing and scaling MongoDB deployments. It covers topics like schema design, indexing, monitoring workload, vertical scaling using resources like RAM and SSDs, and horizontal scaling using sharding. The key recommendations are to optimize the schema and indexes first before scaling, understand the workload, and ensure proper indexing when using sharding for horizontal scaling.
VectorDB Schema Design 101 - Considerations for Building a Scalable and Perfo...Zilliz
People often say that vector search is easy, but that's not entirely true. Vector search is more than just vector indexing and a Python wrapper. If you want to build a high-performance, scalable, and production-ready vector search service, you need to consider many factors.
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
Rob Skillington gave a presentation on observability and M3, Uber's open source time series database. Some key points:
- M3 was created at Uber to handle high dimensionality metrics at massive scale, storing over 11 billion unique time series.
- It uses techniques like Roaring Bitmaps to efficiently store and query metrics with many dimensions or tag values.
- M3 can ingest metrics from Prometheus and Graphite, storing over 33 million metrics per second while powering dashboards and 150,000 alerts.
- The open source M3DB component can run standalone or on Kubernetes, providing a scalable time series storage solution for complex monitoring needs.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
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This document discusses using graph databases, graph data science, and generative AI to unlock insights from connected data. It highlights how relationships in data are valuable, and how graph databases provide an intuitive way to represent and query relationship data. The document introduces Neo4j's graph database capabilities, including graph algorithms for analytics, machine learning on graphs, and integration with other data systems. It also discusses using Neo4j to ground language models for more accurate generative AI applications.
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Active Record 4.0 includes all sorts of exciting support for PostgreSQL! In this presentation, I show many of these improvements, and discuss why these are important for Web developers. If you haven't yet adopted PostgreSQL, now might be a great time and chance to do so.
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Frédéric Descamps gave a demonstration of MySQL Document Store, showing how it allows both SQL and NoSQL functionality. He migrated sample data from MongoDB to MySQL Document Store and performed queries and CRUD operations. The conclusion is that MySQL Document Store provides the best of both worlds by combining schemaless and flexible data with ACID compliance, SQL capabilities, and data integrity.
IRJET- Efficient Geometric Range Search on RTREE Occupying Encrypted Spatial ...IRJET Journal
This document proposes a system to securely store and search spatial data in the cloud using an R-tree index. The system aims to protect data confidentiality and the privacy of data owners. Specifically:
1) A data owner encrypts their spatial data using DES encryption and uploads it to an R-tree index in the cloud.
2) A trusted third party is used to improve trust and detect any malicious modifications to the data.
3) When a search user performs a geometric range search, the third party retrieves the encrypted results and returns them to the user. The user must then request the decryption key from the data owner to decrypt and view the actual data.
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This document discusses designing scalable applications from an umbrella project to a distributed system. It presents a machine learning tools demo project with four applications organized under an umbrella project. It describes using interfaces modules to encapsulate applications and enable inter-application communication as the system scales to multiple nodes. Example communication methods covered include RPC for synchronous calls, distributed tasks for asynchronous work, and HTTP for a third-party API. The document also discusses limiting concurrency using a poolboy worker pool.
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The document discusses managing logs for Black Friday in Elasticsearch. It covers the Elastic Stack components including Beats, Logstash, Elasticsearch and Kibana. It then discusses monitoring architectures, techniques for optimally sizing Elasticsearch clusters and shards, optimizing bulk indexing size, and distributing load across nodes. The presentation aims to provide guidance on log management strategies for handling high volume traffic periods like Black Friday.
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Thoughtworks Zhamak Dehghani observations on these traditional approaches’s failure modes, inspired her to develop an alternative big data management architecture that she aptly named the Data Mesh. This represents a paradigm shift that draws from modern distributed architecture and is founded on the principles of domain-driven design, self-serve platform, and product thinking with Data. In the last decade Apache Kafka has established a new category of data management infrastructure for data in motion that has been leveraged in modern distributed data architectures.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
If you understand the rule engine, especially how works RETE algorithm, You may use this for Machine Learning. This slide used at Red Hat Forum Tokyo 2018 session.
The document discusses various techniques for optimizing and scaling MongoDB deployments. It covers topics like schema design, indexing, monitoring workload, vertical scaling using resources like RAM and SSDs, and horizontal scaling using sharding. The key recommendations are to optimize the schema and indexes first before scaling, understand the workload, and ensure proper indexing when using sharding for horizontal scaling.
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What will you learn?
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Topics Covered
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- Getting started with Haystack
- Ingesting data into Milvus
While achieving a basic Retrieval Augmented Generation (RAG) is relatively straightforward, attaining superior results requires tuning and optimizing various factors, such as a careful selection of embedding models. Additionally, applying advanced techniques, such as multi-stage retrieval with rerankers, is essential. A methodology for quality evaluation is also critical to success in crafting the best strategy for your specific use case. This talk will introduce the landscape of available optimization techniques and provide advice on best practices.
Emergent Methods: Multilingual narrative tracking in the news - real-time exp...Zilliz
We present an architecture of embedding models, vector databases, LLMs, and narrow ML for tracking global news narratives across a variety of countries/languages/news sources in https://asknews.app/. As an example, we explore the real-time application of this architecture for tracking the news narrative surrounding the death of Russian opposition leader Alexei Navalny coming from Russian, French, and English sources
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning.
One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
In this talk, we are going to cover the use-case of food image generation at Delivery Hero, its impact and the challenges.
In particular, we will present our image scoring solution for filtering out inappropriate images and elaborate on the models we are using.
Explore how multimodal embeddings work with Milvus. We will see how you can explore a popular multimodal model - CLIP - on a popular dataset - CIFAR 10. You use CLIP to create the embeddings of the input data, Milvus to store the embeddings of the multimodal data (sometimes termed “multimodal embeddings”), and we will then explore the embeddings.
A Beginners Guide to Building a RAG App Using Open Source MilvusZilliz
We will showcase how you can build a RAG using Milvus. Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.
Zilliz - Overview of Generative models in MLZilliz
Learn the following topics:
What is Generative AI?
Use cases of generative AI
Large Language Models, Neural Networks, and Parameters
GAN (Generative Adversarial Network)
Diffusion models
Multimodal models
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
10. Why do models hallucinate?
• The reason LLMs
hallucinate is because
…
• They are trained on
sequences of words
(tokens)
Sample Data
The hamster cabinet …
!!@#%# …
Monkey eats shark …
trees in the moons…