Introdução a Monitoramento - DEM09 - Sao Paulo Summit
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Aproveitando a infraestrutura dinâmica disponível por meio de provedores e containers da nuvem, podemos dimensionar como nunca antes. Entretanto, ao aumentar nossa escala, o monitoramento se tornou mais complicado e precisamos ajustar nossa estratégia de monitoramento de acordo. Nesta sessão, aprenda a dimensionar sua estratégia de monitoramento. Mostramos a você como enquadrar suas métricas para determinar quais dados coletar e como interpretá-los. Essa sessão é oferecida pelo parceiro AWS, Datadog.
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The DevOps movement has not only influenced the tools we use in modern development and operations engineering, but also how we work. As part of how we work, DevOps has changed how we respond when systems inevitably stop working or don't work as expected. This presentation will provide methods and techniques for gathering information and effectively using that information to avoid and mitigate failure in the future. I'll cover best practices for gathering systems-related data, including monitoring and logging. This presentation will also cover practices for gathering and recording people-related data; including methods we can adopt from police, accident investigators, and other safety management professions to learn the most from incidents. After discussing how to gather data, I'll discuss how we can use the data to formulate actionable response plans and how to adjust existing organizational practices to avoid repeating failure.
I plan to keep the technical portions of this talk at a novice level so that it's accessible to both developers/engineers and those in non-technical roles who will be involved in incident response.
Codemotion Milan 2018 - AI with a devops mindset: experimentation, sharing an...
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be in real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
8 HR Tech Trends That Impact Your HR Role
The document discusses emerging technology trends in HR, including social and collaborative technologies, gamification, mobile technologies, cloud computing, data analytics, acquisitions, and continuous releases. It notes that these trends will impact the role of HR professionals by enabling new approaches to social recruiting, learning, performance tracking, and workforce management that leverage real-time sharing and self-organization.
Luminis Session - ML, TechCrunch, Hype, AI and no ironies
The talk and demo were given at Luminis HQ (Appeldoorn) on 22/May/2018.
The audience was a group of software engineers with limited exposure to ML algorithms on their professional life. The main takeaway is how different it is developing traditional software engineering and ML development.
EVOLUTION not Revolution - Matt Harasymczuk @ Agile Management 2014 Poland
The document discusses an enterprise agile transformation at an organization. It describes adopting agile practices like Scrum and Kanban as a communication framework, reorganizing into a project-oriented structure, and emphasizing business values, quality practices and an evolutionary rather than revolutionary approach. The transformation focused on increasing flexibility, visibility and quality while decreasing costs and time to market through practices like test-driven development, continuous integration and frequent releases.
Evolution Not Revolution. A Case Study of an Enterprise Agile Transformation
Agile transformation requires understanding its rules by management. It is hard to leave status quo and change your way of thinking. It is especially hard when the previous model has been in use for many years. A frequent problem with Scrum / Kanban / Lean transformation is to go hard on deep water. Starting tommorow we're doing Scrum. Lack of understanding, communication chaos and rules distortion "just because daily is not working for us". What if take a different approach: slow and prudent? Let say iteratively. Gradually introduce another steps of chosen framework. However the question persists. How to effectively change our conservative organization into agile and constantly changing in response to business needs company?
Why Your Marketing Will Fail Without Cross Channel Data & How to Fix It
This document discusses how Nike generates revenue and the value of celebrity athlete endorsements. It notes that in 2015, 60% of Nike's revenue came from footwear, 28% from apparel, and 5% from equipment. It also discusses Lebron James' billion dollar lifetime contract with Nike and how Michael Jordan has become a global brand through shoes, apparel, and equipment.
Digital Summit DC Presentation
The document discusses how adopting agile principles and practices can help content marketers produce more impactful content in less time. It advocates using personas, user stories, and story mapping to create audience-centered content. Content should be created and released iteratively through short cycles rather than long, drawn-out processes. Methods like prioritizing a backlog, daily standups, tracking interruptions, demos, and retrospectives can help teams work collaboratively to consistently deliver content. Adapting agile approaches can potentially double a content team's output while reducing the time spent on production.
Codemotion Berlin 2018 - AI with a devops mindset: experimentation, sharing a...
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...
The document discusses applying a DevOps mindset to machine learning. It notes traditional problems in ML like data scientists not being engineers, lack of versioning and scaling issues. Applying continuous integration/deployment practices from DevOps can help address these by facilitating collaboration, versioning of code and data, continuous evaluation, packaging, deployment and model serving. This requires culture change and bridging the gap between data scientists and engineers. DevOps practices may help achieve the collaboration, automation and monitoring needed between ML development and operations.
InfluxData Webinar 12.12.18
The document discusses a presentation about incident response and being an incident commander. It provides tips for skills needed for an incident commander including process, communication, time management, and listening. It also discusses training for inclusive incident response such as defining the incident commander role, having office hours for questions, shadowing other incident commanders, being a scribe to take notes, conducting failure exercises, having experienced commanders shadow new ones, and celebrating successes. Later, the document discusses the InfluxData and PagerDuty platforms and products for ingesting, storing, and analyzing various types of data and enabling collaboration and response to incidents.
Influx data & pagerduty webinar dec 2018
The document discusses a presentation about incident response and being an incident commander. It provides skills needed for an incident commander including process, communication, time management, and listening. It also discusses training for inclusive incident response such as defining the incident commander role, having office hours for questions, shadowing other incident commanders, being a scribe during incidents, conducting failure exercises, having experienced commanders shadow new commanders, and celebrating successes. The presentation then discusses the InfluxData and PagerDuty platforms for ingesting, storing, and analyzing various types of metrics and event data and responding to incidents.
Data Visualization - CodeMash 2022
We spend much of our time collecting and analyzing data. That data is only useful if it can be displayed in a meaningful, understandable way.
Yale professor Edward Tufte presented many ideas on how to effectively present data to an audience or end user.
In this session, I will explain some of Tufte's most important guidelines about data visualization and how you can apply those guidelines to your own data. You will learn what to include, what to remove, and what to avoid in your charts, graphs, maps and other images that represent data.
Forecasting with Less Effort and More Accuracy (Agile Camp NY 2018)
This document summarizes Matt Philip's presentation on NoEstimates forecasting with less effort and more accuracy. Some key points from the presentation include: reducing sources of variation can improve forecasting more than improving estimating skills; data and probabilistic forecasts are preferable to intuition and deterministic estimates; and focusing on delivering value quickly can reduce the need for detailed estimates. The presentation provided examples of techniques to reduce variation, biases to consider, and recommendations for incorporating probabilistic forecasting into projects.
To Estimate or Not to Estimate, Is that the Question? (2017 Better Software C...
This document discusses different approaches to software project estimation, including arguments for and against estimating. It presents a spectrum of estimating approaches from never estimating to always estimating. It advocates for probabilistic and data-driven forecasting over deterministic estimates based on intuition. The key ideas are to reduce sources of variation, gather data over time to build probabilistic forecasts, focus on delivering value quickly, and establish service-level expectations rather than commitments.
The Future for Marketers - Strategy, Creativity and Technology
By Paddy Moogan at Marketing Festival 2014 in Brno. Paddy talks about the future for marketers which is in the middle of strategy, creativity and technology.
MeasureFest July 2021 - Session Segmentation with Machine Learning
1. Audience segmentation using unsupervised machine learning can analyze individual user sessions from analytics tools like Google Analytics to automatically group users into behavior segments.
2. The key steps involve extracting session data, processing it, selecting important features, clustering the data using algorithms like k-means, and manually exploring the resulting segments.
3. These segments can provide insights for hypotheses to test in conversion rate optimization experiments and personalizing the user experience.
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Postmortems orientados por dados - DEV201 - Sao Paulo Summit
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Luminis Session - ML, TechCrunch, Hype, AI and no ironies
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EVOLUTION not Revolution - Matt Harasymczuk @ Agile Management 2014 Poland
EVOLUTION not Revolution - Matt Harasymczuk @ Agile Management 2014 Poland
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Evolution Not Revolution. A Case Study of an Enterprise Agile Transformation
Why Your Marketing Will Fail Without Cross Channel Data & How to Fix It
Why Your Marketing Will Fail Without Cross Channel Data & How to Fix It
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Forecasting with Less Effort and More Accuracy (Agile Camp NY 2018)
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The Future for Marketers - Strategy, Creativity and Technology
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MeasureFest July 2021 - Session Segmentation with Machine Learning
MeasureFest July 2021 - Session Segmentation with Machine Learning
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Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
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La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
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Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
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L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
Open banking as a service
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
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AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
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Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
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Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
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Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
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Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
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Tools for building your MVP on AWS
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3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
How to Build a Winning Pitch Deck
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
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This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
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Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
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Introdução a Monitoramento - DEM09 - Sao Paulo Summit
- 1. MONITORING BASICSHOW WE THINK ABOUT MONITORING AT DATADOG
- 2. @realscottdixon Solutions Engineer @datadoghq SaaS-based monitoring Trillions of data points per day http://jobs.datadoghq.com
- 4. COLLECTING DATA IS CHEAP; NOT HAVING IT WHEN YOU NEED IT CAN BE EXPENSIVE SO INSTRUMENT ALL THE THINGS! @realscottdixon @datadoghq
- 5. COLLECTING DATA IS CHEAP; NOT HAVING IT WHEN YOU NEED IT CAN BE EXPENSIVE SO INSTRUMENT ALL THE THINGS! @realscottdixon @datadoghq
- 6. 4 QUALITIES OF GOOD METRICS NOT ALL METRICS ARE EQUAL @realscottdixon @datadoghq
- 11. TW: @gitbisect @datadoghq 2. SUFFICIENT GRANULARITY
- 12. 1 second Peak 46% 1 minute Peak 36% 5 minutes Peak 12%
- 13. 1 second Peak 46% 1 minute Peak 36% 5 minutes Peak 12%
- 14. 1 second Peak 46% 1 minute Peak 36% 5 minutes Peak 12%
- 15. 3. TAGGED & FILTERABLE @realscottdixon @datadoghq
- 19. Query Based Monitoring “What’s the average throughput of application:nginx per version ?” “How many requests per second is my role:accounting-app running application:postgresql hosted in region:us-west-1 compared to region:us- east-1?” @realscottdixon @datadoghq
- 20. WHAT FACETS/CHARACTERISTI CS ARE IMPORTANT TO YOU? MAKE THEM TAGS! @realscottdixon @datadoghq
- 24. M T W TH FM T W TH FM T W TH FM T W TH F
- 25. M T W TH FM T W TH FM T W TH FM T W TH F OUTAGE? TUESDAY HOLIDAY?
- 26. M T W TH FM T W TH FM T W TH FM T W TH F
- 27. TYPES OF METRICS A FRAMEWORK @realscottdixon @datadoghq
- 30. P.S. - June 1! Mark your calendar! @realscottdixon @datadoghq
- 32. @realscottdixon @datadoghq WHAT TO PAGE ON?
- 33. @realscottdixon @datadoghq WHAT TO PAGE ON?
- 34. RECURSE UNTIL YOU FIND THE TECHNICAL CAUSES @realscottdixon @datadoghq
- 35. -Future You
- 36. RECURSE UNTIL YOU FIND THE TECHNICAL CAUSES @realscottdixon @datadoghq
- 37. ALERT ON WORK METRICS, BUT RESOURCE METRICS BECOME WORK METRICS? ALERT ON EVERYTHING? @realscottdixon @datadoghq
- 38. WHO TO ALERT? eadership @realscottdixon @datadoghq
- 39. WHO TO ALERT? Engineers @realscottdixon @datadoghq
- 40. WHO TO ALERT? DB Team (Ops/SRE) @realscottdixon @datadoghq
- 41. WHO TO ALERT? Ops/SRE @realscottdixon @datadoghq
- 42. BE AWARE OF ROLLUPS @realscottdixon @datadoghq
- 43. 15m = 30s blocks
- 44. 1hr = 1m blocks
- 45. 4hrs = 2m blocks
- 46. 1 Day = 20m blocks