TCP1P.net Meetup Vision, Objectives and RoadmapStefan Ianta
Toronto Code Pile 1 Programming Meetup - the social innovation network - How to build a platform for reactive microservices on a Nobel Prize Reverse Game Algorithm - 2017 Roadmap
Data Science at Netflix - Principles for Speed & Scale [Rev 2019 keynote]Michelle Ufford
Data science powers Netflix. It informs our decisions and challenges our assumptions. It fuels experimentation and innovation at an unprecedented scale. It helps us discover fantastic content and deliver personalized experiences for millions of people around the world. In short, data science has become critical to Netflix, touching nearly every aspect of the business today.
In this talk, Michelle Ufford will discuss the philosophies and innovations that have helped Netflix scale its data science practice. She’ll walk through what the process looks like today and how Netflix is tackling some of the biggest challenges facing data scientists. Through it all, she’ll share core principles that have contributed to their success, which you can put to immediate use at your own company.
MLSEV Virtual. Optimization of Passengers Waiting Time in ElevatorsBigML, Inc
Optimization of Passengers Waiting Time in Elevators using Machine Learning, by Delio Tolivia, Technical Manager of Research, Development and Innovation Projects at Talento Transformación Digital.
*MLSEV 2020: Virtual Conference.
TCP1P.net Meetup Vision, Objectives and RoadmapStefan Ianta
Toronto Code Pile 1 Programming Meetup - the social innovation network - How to build a platform for reactive microservices on a Nobel Prize Reverse Game Algorithm - 2017 Roadmap
Data Science at Netflix - Principles for Speed & Scale [Rev 2019 keynote]Michelle Ufford
Data science powers Netflix. It informs our decisions and challenges our assumptions. It fuels experimentation and innovation at an unprecedented scale. It helps us discover fantastic content and deliver personalized experiences for millions of people around the world. In short, data science has become critical to Netflix, touching nearly every aspect of the business today.
In this talk, Michelle Ufford will discuss the philosophies and innovations that have helped Netflix scale its data science practice. She’ll walk through what the process looks like today and how Netflix is tackling some of the biggest challenges facing data scientists. Through it all, she’ll share core principles that have contributed to their success, which you can put to immediate use at your own company.
MLSEV Virtual. Optimization of Passengers Waiting Time in ElevatorsBigML, Inc
Optimization of Passengers Waiting Time in Elevators using Machine Learning, by Delio Tolivia, Technical Manager of Research, Development and Innovation Projects at Talento Transformación Digital.
*MLSEV 2020: Virtual Conference.
From Data Analytics to Fast Data IntelligenceTrieu Nguyen
1) How to understand users with Data Analytics ?
2) How to build Real-time Music Recommender System from Data Stream ?
3) How to boost profit with Cross Sale in Real-time ?
Key Ideas to build Fast Data Intelligence Platform from Open Source Tools:
+ Apache Kafka
+ Apache Spark
+ RFX framework
Monitoring with Elastic Machine Learning at SkyElasticsearch
Learn how Sky leveraged the power of Elastic’s machine learning feature to process over seven billion documents and help discover trends, learn from real-time data, and generate alerts when anomalies occur.
See the video: https://www.elastic.co/elasticon/tour/2019/london/monitoring-with-elastic-machine-learning-at-sky
Notebooks @ Netflix: From analytics to engineering with Jupyter notebooksMichelle Ufford
Slides from JupyterCon 2018 in NYC on 8/23/2018.
Notebooks have moved beyond a niche solution at Netflix; they are now the critical path for how everyone runs jobs against the company’s data platform. From creating original content to delivering bufferless streaming, Netflix relies on notebooks to inform decisions and fuel experiments across the company. Netflix also uses notebooks to power its machine learning infrastructure and run over 150,000 jobs against its 100 PB cloud-based data warehouse every day. The goal is to deliver a compelling notebooks experience that simplifies end-to-end workflows for every type of user. To enable this, Netflix is investing deeply in notebook infrastructure and open source projects such as nteract.
In this talk, Michelle Ufford and Kyle Kelley share interesting ways Netflix uses data and some of the big bets the company is making on notebooks. Topics will include architecture, kernels, UIs, and Netflix’s open source collaborations with projects such as Jupyter, nteract, pandas, and Spark.
Learn how to build advanced GraphQL queries, how to work with filters and patches and how to embed GraphQL in languages like Python and Java. These slides are the second set in our webinar series on GraphQL.
Oracle Bare Metal Cloud: IBM Cognitive Direct to Consumer SolutionIBM
IBM Watson cognitive solutions accelerate clients’ direct-to-consumer growth. This presentation provides an overview of IBM's cognitive direct-to-consumer solution for Oracle Bare Metal Cloud, complete with an overview of the solution and architecture.
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...Big Data Spain
Geotab is a leader in the expanding world of Internet of Things (IoT) and telematics industry with Big Data.
https://www.bigdataspain.org/2017/talk/vehicle-big-data-that-drives-smart-city-advancement
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Image Caption Generation: Intro to Distributed Tensorflow and Distributed Sco...ICTeam S.p.A.
Tech talk by Luca Grazioli (https://www.linkedin.com/in/luca-grazioli-a74927bb/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
The Elastic Stack has been integral to the growth of E*Trade’s real-time operational intelligence pipeline. Hear their journey to adopting Elastic machine learning features and see firsthand how they’re using them to identify anomalies using full-text aggregations and performance data.
Predictive Analytics for IoT Network Capacity Planning: Spark Summit East tal...Spark Summit
The Internet of Things (IoT) is a growing network, supporting a wide variety of service types with specific network requirements that differ from traditional human type communications. This has led to emergence of dedicated IoT network standards. To optimize investments for dedicated network infrastructures, we’re investigating a dynamic approach in network capacity planning to accommodate multiple IoT traffic types over a cellular network, while maintaining their specific requirements.
We studied models of IoT traffic and used machine learning in prediction and scheduling of future workload under heterogeneous and variable traffic conditions when human-type and machine-type communications are mixed.
An integrated analytics framework including Hadoop and Spark were deployed for experimentation and a number of capacity planning use cases were implemented to verify the accuracy of the method.
Stop observing data ! - Turn them into business with OpenDataSoftOpenDataSoft
They've turned their organisation into an innnovation platform. Find out why our Data Driven Software approach has been core for open innovation & adressing web challenges.
Digital Transformation Mindset - More Than Just Technologyconfluent
Many enterprises faced with silo’ed, batch-oriented, legacy systems struggle to compete in this new digital-first world. Adhering to the ‘If it’s not broken don’t fix it’ mentality leaves the door wide open for native digital challengers to grow and succeed. To stay competitive, your organization must respond in real time to every customer experience transaction, sale, and market movement. But how do you get there? First, you must change your mindset.
As streaming platforms become central to data strategies, companies both small and large are re-thinking their enterprise architecture with real-time context at the forefront. Monoliths are evolving into microservices. Datacenters are moving to the cloud. What was once a ‘batch’ mindset is quickly being replaced with stream processing as the demands of the business impose real-time requirements on technology leaders.
Join Argyle, in partnership with Confluent, in our 2018 CIO Virtual Event: The Digital Transformation Mindset – More Than Just Technology. During the webinar we’ll learn how leading companies across industries rely on a streaming platform to make event-driven architectures central to:
• How data strategies and IT initiatives are improving the digital customer experiences
• How executives are reducing risk with real time monitoring and anomaly detection
• Increasing operational agility with microservices and IoT architectures within organizations
A New Data Architecture for the App Economy - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, Anant Jhingran, VP of Products at Apigee, discusses A New Data Architecture for the App Economy. It has been clear for quite some time that traditional warehouses do not cut it for unstructured and semistructured data, and therefore new systems such as NoSQL and Hadoop have emerged. But these systems throw the baby out with the bathwater. Traditional warehouses were built on the premise that applications can be simpler because the databases did a lot. Of course, the penalty for this was that the application’s world view had to fit the relational, database world view. In the new Big Data system, the primitives have been lowered so much (simple key value pair, or completely unstructured tuple structure), that the applications now have to do a lot more. We argue that there is a happy medium. We have studied the kinds of data that sits in the app economy, and the data structures that need to be built on top of NoSQL and Hadoop that considerably speed up Insights in the app economy without requiring every problem to be coded from scratch.
Smart Markets of Services / ATG meetup TorontoStefan Ianta
Evolutionary machine intelligence in a smart services market. Presentation at Analytics and Technology Group meetup / Ivey Tangerine Centre of Leadership, Toronto, Aug 18, 2016
From Data Analytics to Fast Data IntelligenceTrieu Nguyen
1) How to understand users with Data Analytics ?
2) How to build Real-time Music Recommender System from Data Stream ?
3) How to boost profit with Cross Sale in Real-time ?
Key Ideas to build Fast Data Intelligence Platform from Open Source Tools:
+ Apache Kafka
+ Apache Spark
+ RFX framework
Monitoring with Elastic Machine Learning at SkyElasticsearch
Learn how Sky leveraged the power of Elastic’s machine learning feature to process over seven billion documents and help discover trends, learn from real-time data, and generate alerts when anomalies occur.
See the video: https://www.elastic.co/elasticon/tour/2019/london/monitoring-with-elastic-machine-learning-at-sky
Notebooks @ Netflix: From analytics to engineering with Jupyter notebooksMichelle Ufford
Slides from JupyterCon 2018 in NYC on 8/23/2018.
Notebooks have moved beyond a niche solution at Netflix; they are now the critical path for how everyone runs jobs against the company’s data platform. From creating original content to delivering bufferless streaming, Netflix relies on notebooks to inform decisions and fuel experiments across the company. Netflix also uses notebooks to power its machine learning infrastructure and run over 150,000 jobs against its 100 PB cloud-based data warehouse every day. The goal is to deliver a compelling notebooks experience that simplifies end-to-end workflows for every type of user. To enable this, Netflix is investing deeply in notebook infrastructure and open source projects such as nteract.
In this talk, Michelle Ufford and Kyle Kelley share interesting ways Netflix uses data and some of the big bets the company is making on notebooks. Topics will include architecture, kernels, UIs, and Netflix’s open source collaborations with projects such as Jupyter, nteract, pandas, and Spark.
Learn how to build advanced GraphQL queries, how to work with filters and patches and how to embed GraphQL in languages like Python and Java. These slides are the second set in our webinar series on GraphQL.
Oracle Bare Metal Cloud: IBM Cognitive Direct to Consumer SolutionIBM
IBM Watson cognitive solutions accelerate clients’ direct-to-consumer growth. This presentation provides an overview of IBM's cognitive direct-to-consumer solution for Oracle Bare Metal Cloud, complete with an overview of the solution and architecture.
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...Big Data Spain
Geotab is a leader in the expanding world of Internet of Things (IoT) and telematics industry with Big Data.
https://www.bigdataspain.org/2017/talk/vehicle-big-data-that-drives-smart-city-advancement
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Image Caption Generation: Intro to Distributed Tensorflow and Distributed Sco...ICTeam S.p.A.
Tech talk by Luca Grazioli (https://www.linkedin.com/in/luca-grazioli-a74927bb/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
The Elastic Stack has been integral to the growth of E*Trade’s real-time operational intelligence pipeline. Hear their journey to adopting Elastic machine learning features and see firsthand how they’re using them to identify anomalies using full-text aggregations and performance data.
Predictive Analytics for IoT Network Capacity Planning: Spark Summit East tal...Spark Summit
The Internet of Things (IoT) is a growing network, supporting a wide variety of service types with specific network requirements that differ from traditional human type communications. This has led to emergence of dedicated IoT network standards. To optimize investments for dedicated network infrastructures, we’re investigating a dynamic approach in network capacity planning to accommodate multiple IoT traffic types over a cellular network, while maintaining their specific requirements.
We studied models of IoT traffic and used machine learning in prediction and scheduling of future workload under heterogeneous and variable traffic conditions when human-type and machine-type communications are mixed.
An integrated analytics framework including Hadoop and Spark were deployed for experimentation and a number of capacity planning use cases were implemented to verify the accuracy of the method.
Stop observing data ! - Turn them into business with OpenDataSoftOpenDataSoft
They've turned their organisation into an innnovation platform. Find out why our Data Driven Software approach has been core for open innovation & adressing web challenges.
Digital Transformation Mindset - More Than Just Technologyconfluent
Many enterprises faced with silo’ed, batch-oriented, legacy systems struggle to compete in this new digital-first world. Adhering to the ‘If it’s not broken don’t fix it’ mentality leaves the door wide open for native digital challengers to grow and succeed. To stay competitive, your organization must respond in real time to every customer experience transaction, sale, and market movement. But how do you get there? First, you must change your mindset.
As streaming platforms become central to data strategies, companies both small and large are re-thinking their enterprise architecture with real-time context at the forefront. Monoliths are evolving into microservices. Datacenters are moving to the cloud. What was once a ‘batch’ mindset is quickly being replaced with stream processing as the demands of the business impose real-time requirements on technology leaders.
Join Argyle, in partnership with Confluent, in our 2018 CIO Virtual Event: The Digital Transformation Mindset – More Than Just Technology. During the webinar we’ll learn how leading companies across industries rely on a streaming platform to make event-driven architectures central to:
• How data strategies and IT initiatives are improving the digital customer experiences
• How executives are reducing risk with real time monitoring and anomaly detection
• Increasing operational agility with microservices and IoT architectures within organizations
A New Data Architecture for the App Economy - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, Anant Jhingran, VP of Products at Apigee, discusses A New Data Architecture for the App Economy. It has been clear for quite some time that traditional warehouses do not cut it for unstructured and semistructured data, and therefore new systems such as NoSQL and Hadoop have emerged. But these systems throw the baby out with the bathwater. Traditional warehouses were built on the premise that applications can be simpler because the databases did a lot. Of course, the penalty for this was that the application’s world view had to fit the relational, database world view. In the new Big Data system, the primitives have been lowered so much (simple key value pair, or completely unstructured tuple structure), that the applications now have to do a lot more. We argue that there is a happy medium. We have studied the kinds of data that sits in the app economy, and the data structures that need to be built on top of NoSQL and Hadoop that considerably speed up Insights in the app economy without requiring every problem to be coded from scratch.
Smart Markets of Services / ATG meetup TorontoStefan Ianta
Evolutionary machine intelligence in a smart services market. Presentation at Analytics and Technology Group meetup / Ivey Tangerine Centre of Leadership, Toronto, Aug 18, 2016
The success of any transformation efforts depending on the best practices followed over the transformation and beyond. Enterprise Architecture practice helps to execute the transformation efforts seamlessly. This presentation discover more details.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
What powers the AI/ML services of Switzerland's leading telecommunication company? In this talk, we will provide an overview of the different AI/ML projects at Swisscom, from Conversational AI and Recommender Systems to Anomaly Detection. Moreover, we will show how we automate, scale, and operationalise these ML pipelines in production, highlighting the MLOps techniques and open source tools that are used. Finally, we will present Swisscom's roadmap towards the cloud with AWS and discuss how we envision a common MLOps solution for the organisation.
Platform governance, gestire un ecosistema di microservizi a livello enterpriseGiulio Roggero
A livello enterprise, le moderne architetture distribuite coinvolgono molti team differenti, centinaia di sviluppatori e operations e migliaia microservizi ed API in produzione. Come si può gestire questa
e o
un'esplosione di costi e preservando il time-to-market?
Build Answer-generating Apps that Users Love: Development best practices for ...TIBCO Jaspersoft
More and more employees (not just data analysts or C-level execs) are expected to make data-driven decisions, yet only 20–25% of workers have access to business intelligence (BI) products. And when asked, end users don’t want to use a “BI tool”—another interface to learn, another login—they want easily accessible answers. Instead of offering a standalone dashboard, the new trend is to embed analytics into applications that are already used every day.
This workshop series features a brand new demo application—created by TIBCO Jaspersoft and projekt202—that illustrates and teaches you how to create answer-generating applications. Over four webinars, we will show you to the what and why of data-as-a-feature applications and how you can build your own.
Data-as-a-Feature webinar sessions:
- Build answer-generating apps that users love: Learn best practices for making data valuable to your application users
- Getting started: How to set up your data-as-a-feature project
- Your data in your app: Best practices for embedding interactive reports and visualizations in your app
- Managing the experience + ad hoc reporting: Handling security, multi-tenancy, and self-service reporting for your data-as-a-feature app
Mindtree leverages its performance engineering services to develop software products and applications that perform optimally in normal as well as extreme load conditions. This reduces the number of failures related to performance and availability. We offer performance engineering services across a wide range of verticals and applications based on client server, Web technologies, Web services and ERP.
Similar to Evolutionary Design Patterns for Software Development (20)
Manhattan Project 2017 - Building the Socially Controlled AI NetworkStefan Ianta
Dec 2, 2017 - Presentation at Manhattan Algorithmic Innovations Reactor Meetup at Columbia University, Pupin Physics Laboratory where Einstein, Szilard and Fermi launched 77 years ago the Manhattan Project to exploit massive chain reactions
From Copycat Codelets to an AI Market Internet ProtocolStefan Ianta
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Introduction to the concepts and benefits of the Universal Innovation Language and how to implement it as the Semantic AI Internet / Digital Democracy.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
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Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
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Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
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Evolutionary Design Patterns for Software Development
1. Evolutionary Design Patterns
for Software Development
Growing Abstract Syntax Trees
from
Business Context Graphs
Stefan Ianta
Cofounder Servi.ca & Ianta Labs
@v_ianta
Self-assembling wires
Stanford Complexity Group
www.youtube.com/watch?v=PeHWqr9dz3c
2. Summary
Service Compilers
Growing software from Business Models
Solving a Simple Workflow Problem
Q & A
servi.ca
5. Service Compilers
Business Models
Contexts
Change
Service
Microservices
Solutions
Innovations
Business Transformation
Google Model
Smart Market of Services
Software generation
Smart Markets of Service
servi.ca
6. Graph Reactor / Ianta Labs
Initial Business Context – Data & Code
ClientsRules
Rivals
Code
Meta
Data
Plans
Data Team
Sales
Bank
Skills
CONTEXT
7. Graph Reactor / Ianta Labs
Final Business Context – Data & Code
ClientsRules
Rivals
Codes
Meta
-Data
Plan
Data
Team
Sales
Money
Skills
CONTEXT
15. Graph Reactor / Ianta Labs15
Solution
=
Path
Target Context
Metadata
Data
Software
Initial Context
Metadata
Data
Software
Software Development as a Solution Search Process
Initial Context / Code Target Context / Code
Changes
Transformation
16. Graph Reactor / Ianta Labs16
Search Engine Business Model
AdWords
Advertisers
Pay Per ClickAdSense
Web Masters
Pay Per Click
Internet Pages Search
Question Words
One question/search
Search Engines Do NOT Build Solutions
Internet Pages are Contributed by Millions of People
17. Graph Reactor / Ianta Labs17
Expanding Search Engine Model into an Solution Search Engine
AdWords
Advertisers
Pay Per Click
AdSense
Web Masters
Pay Per Click
Internet Pages Search
Internet Users
One question/search
Microservices
Programmers
Pay Per Use
Cypher Annotations
Business Analysts
Pay Per Use
Budgeted Projects
Product Managers
Pay Per Use
22. Graph Reactor / Ianta Labs22
Implementation Steps
• Translate existing APIs into Microservices / Coders + DevOps
• Annotate microservices with Cypher queries / BAs
• Define projects including test queries / Product Managers
• Build a Service compiler or join a Service Smart Market – Servi.ca
23. “
”
Civilization advances by extending
the number of important
operations which we can perform
without thinking of them.
Alfred North Whitehead
Interested to join building a smart market of services?
Subscribe at Servi.ca!
24. Q & A
Stefan Ianta
@v_ianta
servi.ca
https://ca.linkedin.com/in/stefanianta