Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
MongoDB.local Austin 2018: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
Building Intelligent Apps with MongoDB and Google Cloud - Jane FineMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
MongoDB.local Sydney 2019: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
MongoDB.local DC 2018: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives.
Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you'll learn
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
MongoDB.local Austin 2018: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
Building Intelligent Apps with MongoDB and Google Cloud - Jane FineMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
MongoDB.local Sydney 2019: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
MongoDB.local DC 2018: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives.
Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you'll learn
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
Building a Marketing Data Warehouse from Scratch - SMX Advanced 202Christopher Gutknecht
This deck covers the journey of starting with BigQuery, adding more data sources and building a process around your data warehouse. It covers the three phases greenfield, dashboards and operational analytics and the necessary data components.
The code for uploading your product feed can be found here:
https://gist.github.com/ChrisGutknecht/fde93092e21039299ab76715596eac01
If you have any questions, reach out to me on Linkedin!
Google Analytics Konferenz 2019_Google Marketing Platform - Enterprise_Oleg P...e-dialog GmbH
The GMP as part of enterprise level decision making platform
See how Sixt is using various tools of the GMP stack from basic reporting via Data Studio up to advanced data science with Raw Data to make datadriven decisions and how GTM is a main tool to ensure data consistency.
Dynamics Day 2016: Microsoft Dynamics 365 first lookIntergen
James Page and Steven Foster give the first Australasian public viewing of Microsoft’s new business platform as a service, Dynamics 365, and explore the foundations underpinning future Digital Transformation success.
Customer Insights, Customer Service Insights, Supply Chain Insights, ... Microsoft is adding lots of new features in Dynamics 365 using AI in many ways. Let's take some time to review the most significant ones and explore what is already or will be available soon that will improve the way you are working personally or as a team in your day-to-day activities. We will together introduce each of these opportunities to improve our work in some real scenarios and business cases.
Microsoft Dynamics 365 - Intelligent Business Applications
Referat von Silvia Gönner (Microsoft) an der CRM Community Schweiz vom 26.10.2016 in Zug: http://www.crm-community.ch/event/crm-community-26oct2016/
Data Driven Attribution in BigQuery with Shapley Values and Markov ChainsChristopher Gutknecht
This talk covers the journey of implementing two data driven attribution models in BigQuery and the findings so far. The packages Fractribution (Google) and Channel Attribution were used to model Shapley values and markov chains respectively.
Evolution von MSE - wie geht es mit Social Listening Tool weiter?IOZ AG
Keynote von Raphael El-Saheli zum Thema Social Media Monitoring mit "Microsoft Social Engagement" an der CRM Community Schweiz vom 26.10.2016: http://www.crm-community.ch/event/crm-community-26oct2016/
Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019GoDataDriven
Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
Building a Marketing Data Warehouse from Scratch - SMX Advanced 202Christopher Gutknecht
This deck covers the journey of starting with BigQuery, adding more data sources and building a process around your data warehouse. It covers the three phases greenfield, dashboards and operational analytics and the necessary data components.
The code for uploading your product feed can be found here:
https://gist.github.com/ChrisGutknecht/fde93092e21039299ab76715596eac01
If you have any questions, reach out to me on Linkedin!
Google Analytics Konferenz 2019_Google Marketing Platform - Enterprise_Oleg P...e-dialog GmbH
The GMP as part of enterprise level decision making platform
See how Sixt is using various tools of the GMP stack from basic reporting via Data Studio up to advanced data science with Raw Data to make datadriven decisions and how GTM is a main tool to ensure data consistency.
Dynamics Day 2016: Microsoft Dynamics 365 first lookIntergen
James Page and Steven Foster give the first Australasian public viewing of Microsoft’s new business platform as a service, Dynamics 365, and explore the foundations underpinning future Digital Transformation success.
Customer Insights, Customer Service Insights, Supply Chain Insights, ... Microsoft is adding lots of new features in Dynamics 365 using AI in many ways. Let's take some time to review the most significant ones and explore what is already or will be available soon that will improve the way you are working personally or as a team in your day-to-day activities. We will together introduce each of these opportunities to improve our work in some real scenarios and business cases.
Microsoft Dynamics 365 - Intelligent Business Applications
Referat von Silvia Gönner (Microsoft) an der CRM Community Schweiz vom 26.10.2016 in Zug: http://www.crm-community.ch/event/crm-community-26oct2016/
Data Driven Attribution in BigQuery with Shapley Values and Markov ChainsChristopher Gutknecht
This talk covers the journey of implementing two data driven attribution models in BigQuery and the findings so far. The packages Fractribution (Google) and Channel Attribution were used to model Shapley values and markov chains respectively.
Evolution von MSE - wie geht es mit Social Listening Tool weiter?IOZ AG
Keynote von Raphael El-Saheli zum Thema Social Media Monitoring mit "Microsoft Social Engagement" an der CRM Community Schweiz vom 26.10.2016: http://www.crm-community.ch/event/crm-community-26oct2016/
Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019GoDataDriven
Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.
Modern Thinking: Cómo el Big Data y Cognitive están cambiando la estrategia de Marketing
Por: Ismael Yuste, Strategic Cloud Engineer Google Cloud
Presentación: Introducción a las soluciones Big Data de Google
Applying BigQuery ML on e-commerce data analyticsMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. We are going to demonstrate common marketing Machine Learning use cases we do at REEA.net to build, train, eval and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases:
Customer Segmentation
Customer Lifetime Value (LTV) prediction
Conversion/Purchase prediction
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor
How to leverage artificial intelligence in power apps with ai builder Concetto Labs
Microsoft introduced Artificial Intelligence in PowerApps with an AI builder. You will need PowerApp developers well-versed with the AI builder functions.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Advanced Analytics and Artificial Intelligence - Transforming Your Business T...David J Rosenthal
Recent advances in AI have incredible potential and they are already fundamentally changing our lives in ways we couldn’t have imagined even five years ago. And yet, AI is also probably one of the least understood technological breakthroughs in modern times. Come to this event to learn about breakthrough advances in AI and the power of the cloud, and how Microsoft provides a flexible platform for you to infuse intelligence into your own products and services. Microsoft empowers you to transform your business, uniquely combining AI innovation with a proven Enterprise platform, deriving intelligence from a wide range of data relevant to your business no matter where it lives.
Harnessing the power of AI to supercharge the Customer Experience. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By George Aspiotis
Empowering you - Power BI, Power Platform & AI BuilderRui Quintino
Slides for the "Microsoft Empowering You" webinar about Power BI, Power Apps, Power Automate & AI Builder by DevScope.
Explore how Power Platform & AI Builder can enrich your Power BI experience.
Watch the full session at https://youtu.be/IhwiESvFaxg
(English subtitles available)
Big Data Paris - A Modern Enterprise ArchitectureMongoDB
Depuis les années 1980, le volume de données produit et le risque lié à ces données ont littéralement explosé. 90% des données existantes aujourd’hui ont été créé ces 2 dernières années, dont 80% sont non structurées. Avec plus d’utilisateurs et le besoin de disponibilité permanent, les risques sont beaucoup plus élevés.
Quels sont les paramètres de bases de données qu’un décideur doit prendre en compte pour déployer ses applications innovantes?
The anlaytics industry is in the biggest state of flux at this time with Adobe SiteCatalyst 15, Google Beta and WebTrends 10 hitting the market. Analytics, as a field, is changing faster than ever and the need of integrating analytics with more and more channels is increasing. The PPT covers some aspects of the tools and technologies available for advance analytics reporting and insights.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
Linear regression
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
3. Analytics is a Constant Underachiever
2000
Business Intelligence
Data Warehouse, OLAP, Ad-
hoc, Reporting, Dashboards
Big Data
Hadoop, MapReduce,
Predictive, ML, Real-time
2010
AI
Intelligent Things, IoT,
Systems of Engagement
2017
“In 2018, 75% of AI projects will underwhelm because they fail to model operational
considerations, causing business leaders to reset the scope of AI investments.”
FORRESTER.COM/PREDICTIONS
4. $40B+ eCommerce platform
experience of millions of mobile
gamers
metadata for every single item
for sale on eBay.com
world’s leading design
collaboration platform
20M+ users
$150B+ traded
reinvent travel for millions of
customers
lab and clinical analysis for
innovative medicines
personal and business finance
management worldwide
Applications Change Our World
5. Operational
AI
ML
What We Set Out To Do
shop for
products
online
Intelligent App
get
personalized
product
recommendatio
ns
shop over
MMS/Whats
App on
mobile
device
ML
6. What’s an Intelligent App?
Applications are increasingly combining real-time analytics, machine learning
and AI to provide understand the customer, automate their tasks and provide
knowledge and decision support
7. Why Is This Hard?
Intelligent App
Developer
s
Data Scientists
uses: live data
guided by: user stories
produces: functionality
uses: prepared data
guided by: question
produces: insight
RELEASE DEFINE
BUILD
AGILE
DATA
PREP
BUILD
MODEL
GET
INSIGHT
VIZ
DEPLOY
TRAIN/EVAL
9. eCommerce App: SwagStore
Get notified when a
sold-out item is
restocked
Browse for your favorite
MongoDB swag
Put items in cart and
checkout
View your orders
10. Stitch: MongoDB Serverless Platform
Streamlines app development with
simple, secure access to data and
services from the client with
thousands of lines less code to write
and no infrastructure to manage.
Getting your apps to market faster
while reducing operational costs.
11. SwagStore: How We Built It
UI components
Routes
Application Flow Control
Google Authentication
Twilio Notifications
Functions
Rules
Triggers
Service Integrations
Flexible Document Model
Easy to Work With Data
14. Intelligent eCommerce App: SwagStore +
Receive personalized
product
recommendations based
on ML algorithm
Recommendation
Engine
15. Intelligent SwagStore: How We Built It
SwagStore
Google Cloud ML
trains and tunes model using
TensorFlow WALS Algorithm
Google Cloud Endpoints
serve recommendations
Stitch initiates
recommendations
developer data scientist developer
data engineer
16. Production Recommendation Solution on
GCP
Google Analytics
BigQuery
Google Analytics
360
Customer Web
Application
Web
Server
Application
Server
Database
Server
Rec API
App Engine
Cloud
Endpoints
Model Training
Cloud Machine Learning
Orchestration
Cloud Composer
ML Data
Training
Model files
Browser
Client
Mobile /
Tablet Client
17. The Recommendation API
Google Analytics
BigQuery
Rec API
App Engine
Cloud
Endpoints
Model Training
Cloud Machine Learning
ML Data
Training
Model files
UserID
User factor x
Item factors
Item ID &
User ID
Maps
Sort & Filter
Article
Index
List
Item ID &
User ID
Maps
Article
ID
List
ML Model
18. Training Recommendation Model
1. install the model code
2. place data into your Cloud Storage Bucket
3. run training script
When the training is finished, the model data is saved in a subdirectory named model under
the job directory of the training task. This data consists of several arrays, all saved in numpy
format
./mltrain.sh train
gs://recserve_jfmlrecengine/swag_pageviews.cs
v --data-type web_views
19. Tuning Recommendation Model
Hyperparameter tuning optimizes your machine learning model for most accuracy
Typically data scientists experimenting with various values, test the model, and then pick a combination
of parameters with the best performance.
But you can test every possible combination of parameters…. it would take a very very long time
● Each hyperparameter is passed to
the hyperparameter tuning job on
Cloud ML Engine.
● The model writes a TensorFlow
summary with a special tag that's set
to the metric that evaluates the
quality of the model.
● This summary metric enables the
search process of the Cloud ML
Engine hyperparameter tuning
./mltrain.sh tune
gs://recserve_jfmlrecengine/
swag_pageviews.csv --data-
type web_views
20. AI does AI
Systematic exploration
of the model space, using
the techniques finessed in
AlphaGo, yields super-human
performance in AI network design
21. Generate Recommendations
model.py : generate_recommendations
input
user: row index of the user in the rating matrix
items: list of indexes for items that the user has rated / viewed
latent factors: row and column factors generated by training / tuning the
model
number of desired recommendations
https://jfmlrecengine.appspot.com/recommendation?us
erId=5448543647176335931&numRecs=6
{"articles":
["299824032
","29993528
7","2998657
57","299959
410","29815
7062","2998
16215"]}
22. Serving Recommendations with Stitch
MongoDB Stitch send a HTTP GET
request to Google Cloud Endpoint
to obtain recommendations
For user who is logged in
Get list of product
recommendations
Update user profile with that
list
Service Integrations make it
simple for your app to use
leading third party services
Functions: build complex logic and orchestrate data
between clients, and services
Stitch scales precisely to meet your usage.
26. Intelligent SwagStore: How We Built It
SwagStore
Google DialogFlow:
Intent, Entities, Webhooks
Stitch - Intent Fulfilment Slack - Front End
developer
data scientist
developer data engineer
27. DialogFlow + Stitch Architecture
Stitch HTTP
Service
Webhook
Stitch Functions to
retrieve products
from MongoDB
28. Me: “Can you help me find a jacket?”
ChatBot: “What color would you like?”
Me: “White, please”
ChatBot: “I found you a white Egmont Jacket”
DialogFlow: Rich and Natural
Conversational Experiences
Stitch Service
Integration
30. Enabling DialogFlow Fulfillment With Stitch
return item from SwagStore Catalog to
DialogFlow
{
"fulfillmentText": "I found you a white
Egmont Packable Jacket. Check it out
here: https://mdb-swag-
store.netlify.com/products/299824032"
}
perform $find given
parameters requested
by DialogFlow: product
type and product color