This talk will focus on Techniques, metrics and different tests (code, models, infra and features/data) that help the developers of machine learning systems to achieve CD.
vodQA Pune (2019) - Testing ethereum smart contractsvodQA
This document discusses testing Ethereum smart contracts. It provides an overview of Ethereum as a distributed public network running the Ethereum Virtual Machine. It describes how to set up a testing environment using Geth and Truffle, including developing smart contracts, running unit and integration tests. It also discusses non-functional testing like node, performance, and security testing.
Scaling Ride-Hailing with Machine Learning on MLflowDatabricks
"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK.
"
BDD for RIAs with JavaScript - Skills MatterCarlos Ble
This document discusses behavior-driven development (BDD) for rich internet applications (RIAs) using JavaScript. It explains that the business logic for BDD step definitions can live directly in the browser code. Outside-in test-driven development can start by testing the JavaScript application in the browser first before developing the server-side code, which would act as an adapter. The server-side features will emerge progressively as needed to support rules for security, data consistency, and a RESTful API. The document also covers testing JavaScript applications, event-oriented programming, promises, and references for further reading.
Teams often waste effort on such useless things as integration tests and maintaining multiple nonproduction environments. Moving to an only-production viewpoint would save countless engineering cycles and put effort where it matters.
In this session we will discuss why eliminating nonproduction environments is not such a crazy idea. We will review tools and practices that would help teams to deliver their services with confidence and much faster than with standard approaches.
vodQA Pune (2019) - Testing AI,ML applicationsvodQA
1) The document discusses testing machine learning and AI applications. It covers topics like what AI and ML are, how technology is shifting towards them, and where quality assurance fits in.
2) Hands-on activities are described for creating and testing a basic wine-beer classifier and an image classifier using mobile networks.
3) Challenges of testing AI and ML applications include generating training and test data, knowing thresholds, and dealing with data quality issues like overfitting and underfitting. Metrics like accuracy, precision, recall, and error rates are discussed.
This talk discusses the pros and cons of Angular and React and shows how they're similar, as well as how they're different. Deepu will defend React, Matt will defend Angular. We'll even show you the same app created with React and Angular via JHipster.
Are you a Java developer and you've seen the fluent and modern specification frameworks available in other programming languages such as spock or jasmine and wondering why we can't have the same thing?
Then Lambda Behave is made for you! It uses numerous Java 8 features in order to enable cleaner and more readable tests and specifications. This quickie will introduce the framework, explain why you would want to use it over junit and cover some of its cool features, including:
* How to write fluent and clean specifications in your domain language
* How to write data driven specifications
* How to integrate these into your build tool or IDE
* How IDE usage can influence library design for the better
http://richardwarburton.github.io/lambda-behave/
Neo4j is a graph database that is natively designed to store and query graph data. It uses a graph-native architecture that optimizes for storing and traversing relationships between nodes. This allows Neo4j to provide faster performance on workloads involving connected data compared to traditional databases.
The Effect of Microservices on API DesignLunchBadger
How has microservices and new architectural style helped to evolve API Design? If you're working with microservices and APIS, this helpful presentation will underscore the important challenges and solutions you can take advantage of right now.
Learn more about:
- Monolith versus Microservices
- How Microservices and APIs interact
- Challenges & Solutions
- How to leverage Express Gateway, an open source API gateway built entirely on Express.js
Presented by Al Tsang, CEO of LunchBadger, at the 2017 SmartBear Conference in Boston, MA.
Imagine an application that has a web and a mobile, IOS and Android, or that your API is consumed by similar frontends from totally different teams. The functionalities they provide are distinct, hence the need for distinct sets of data and functions. You might think that the solution for this is having an “as generic as possible” backend for all UI’s. From my experience, this kind of backend leads to big issues in matters of performance, entangled user experience as well as extra and unnecessary communication for the development teams in order to align and meet their needs. Fortunately, there is a promising set of approaches taking the stage as they are created with the intention to optimize how front-end applications collaborate with back-ends: BFF (Backend-For-Frontend) pattern and GraphQL. Given these two approaches, which one is the right to consider? Join me in a talk where we will discuss the two approaches, underline both their good and bad sides, and determine which you should consider as the backend technology for your frontend application.
When setting up a new project we have some tips and tricks to help you do this in the best way possible, incl. infrastructure, database, standard attributes, logging, code alignment, and service center.
Raphaël Pinson presented on implementing GitOps with the DevOps Stack. The DevOps Stack provides an opinionated Kubernetes stack that is deployed and managed using GitOps. It handles provisioning Kubernetes, integrating single sign-on, and managing observability tools through Argo CD. Argo CD syncs the cluster state with the desired manifests in Git, ensuring congruence. It also provides an interface for managing applications and templates. The DevOps Stack offers a standardized way to deploy common services and manage infrastructure as code.
Web Applications of the Future with TypeScript and GraphQLRoy Derks
Type systems like TypeScript are the next thing when developing JavaScript applications. They make sure that your props are passed in the correct form and shape throughout your application. Together with GraphQLs query language based on types, you can create the web application of the future!
GraphQL is a query language for APIs that allows flexible querying of data from a server. It was originally created by Facebook in 2012 and open sourced in 2015. Some key benefits of GraphQL include allowing apps to control the specific data received from servers instead of receiving all possible data like with REST APIs, and GraphQL queries mirroring the response structure. GraphQL schemas define query and mutation parameters as well as return data types.
Creating a GraphQL endpoint in 60 minutes or less.
Assaf Gannon, Javascript Architect, from Tikal.
In this talk, we will address 2 of the most interesting topics in recent times - serverless and GraphQL. We will go over the requirements and key tools required to get started with both. We will demonstrate how simple it is to create a GraphQL endpoint and deploy it using Serverless services.
Was originally presented at https://www.meetup.com/Fullstack-Developers-Silicon-Valley/events/250007589/
Sviluppare applicazioni nell'era dei "Big Data" con Scala e Spark - Mario Car...Codemotion
This document discusses developing applications for big data using Scala and Spark. It provides an overview of Scala and Spark, including their history, features, and modules. Scala is a functional programming language for the JVM that combines object-oriented and functional programming. Spark is an open-source cluster computing framework that provides APIs for processing large datasets in parallel. The document outlines how Spark works using its DAG execution engine and RDD abstraction to distribute tasks across a cluster. It also lists various Spark modules like SQL, MLlib, Streaming, and GraphX.
The document summarizes the implementation of an open source search solution for a company with over 12 million documents. It discusses three plans for implementing access control to filter search results based on user permissions: 1) storing permissions with each document and filtering at search time, 2) checking permissions directly from the file server at search time, and 3) iterating user permissions at index time and storing readable documents as search terms to filter as booleans at search time. The third plan provided fast access control while only causing up to one day of indexing lag.
GraphQL is a wonderful abstraction for describing and querying data. Apollo is an ambitious project to help you build apps with GraphQL. In this talk, we'll go over how all the parts—Client, Server, Dev Tools, Codegen, and more—create an end-to-end experience for building apps on top of any data.
## Detailed description
In today's development ecosystem, there are tons of options for almost every part of your application development process: UI rendering, styling, server side rendering, build systems, type checking, databases, frontend data management, and more. However, there's one part of the stack that hasn't gotten as much love in the last decade, because it usually falls in the cracks between frontend and backend developers: Data fetching.
The most common way to load data in apps today is to use a REST API on the server and manage the data manually on the client. Whether you're using Redux, MobX, or something else, you're usually doing everything yourself—deciding when to load data, how to keep it fresh, updating the store after sending updates to the server, and more. But if you're trying to develop the best user experience for your app, all of that gets in the way; you shouldn't have to become a systems engineer to create a great frontend. The Apollo project is based on the belief that data loading doesn't have to be complicated; instead, you should be able to easily get the data you want, when you want it, and it should be managed for you just like React manages updating your UI.
Because data loading touches both the frontend and backend of your app, GraphQL and Apollo have to include many parts to fulfill that promise of being able to seamlessly connect your data together. First, we need client libraries not only for React and JavaScript, but also for native iOS and Android. Then, we must bring server-side support for GraphQL queries, mutations, and most recently subscriptions to every server technology and make those servers easier to write. And finally, we want not only all of the tools that people are used to with REST APIs, but many more thanks to all of the capabilities enabled by GraphQL.
In this talk, we'll go over all of the parts of a GraphQL-oriented app architecture, and how different GraphQL and Apollo technologies come together to solve all of the parts of data loading and management for React developers.
OSGi Community Event 2016 Presentation by Milen Dyankov (Liferay)
OSGi has evolved and matured beyond recognition over the last few years. It’s now easier than ever before, to build dynamic, modular Java applications to address the challenges imposed by ever growing and constantly changing business requirements. Despite that fact, OSGi seems to be far from receiving the appreciation it deserves. And if you are OSGi developer who now wanders “why should I care?”, let me remind you Thomas Edison’s famous quote “The value of an idea lies in the using of it”!
Growing large community around given technology has proven to be an essential part of its success. In this talk I’d like to go over what OSGi community is (not) doing to attract “outsiders”. I’d also argue it can do much better than that. Based on observations and conversation from the last 2 years trying to advocate for OSGi among Java developers. I’ll try to position the technology it today’s reality of microservices, containers, clouds, DevOps, automation, Java 9, … and bring to your attention the perspective of an “outsider” together with all the presumptions, fallacies and promises it comes with. Finally I’d like to share some ideas about how to address those, promote relevant parts of OSGi and thus perhaps make it more attractive to Java developers!
This document discusses building observable infrastructure and code using the Elastic Stack. It recommends collecting all relevant system data such as logs, metrics and traces using common data schema to correlate information across sources. Specific tools mentioned include Elastic APM, Logstash, Metricbeat and Prometheus. Demos are provided showing how to visualize logs, metrics and traces together for observability. Resources for getting help from the Elastic community are also listed.
This document discusses software quality assurance and control for small development teams. It defines the differences between quality assurance, which ensures the development process is efficient, and quality control, which ensures the product meets requirements. Quality control has two components: validation, which checks the right product is built, and verification, which checks the product is built correctly. Validation involves getting complete requirements from customers and defining the problem being solved. Verification uses testing strategies like unit, integration, and system testing following a V-model approach. The document provides tips for testing, including change-related, integration, and ensuring independence between development and testing roles.
Agile Data Science on Greenplum Using Airflow - Greenplum Summit 2019VMware Tanzu
This document discusses using Apache Airflow to build agile data science pipelines on Greenplum Database. It outlines the typical data science phases of discovery and operationalization. In the discovery phase, rapid iteration and experimentation is used. The operationalization phase focuses on building automated, testable pipelines for data preparation, model training/scoring, monitoring, and APIs. It provides examples of directed acyclic graphs (DAGs) for end-to-end data processing, model training, model scoring, and model re-training. It emphasizes the importance of testing, monitoring failures, and fixing errors for responsive pipelines. Greenplum and Jupyter notebooks enable agile data science in discovery, while Greenplum, Airflow,
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
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About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
Deploying ML models to production (frequently and safely) - PYCON 2018David Tan
1. The document discusses principles and practices for reliably and repeatedly deploying machine learning models from development to production.
2. It emphasizes the importance of frequent and safe deployment to iteratively improve models and correct any biases, and addresses challenges like deploying models that impact lives.
3. The talk provides an overview of continuous delivery principles for machine learning and demonstrates an automated pipeline for testing, versioning, and deploying models.
Continuous delivery requires more that DevOps. It also requires one to think differently about product design, development & testing, and the overall structure of the organization. This presentation will help you understand what it takes and why one would want to deliver value to your customers multiple times each day. #CIC
Jeff "Cheezy" Morgan Ardita Karaj
Production ready big ml workflows from zero to hero daniel marcous @ wazeIdo Shilon
This document provides an overview of production-ready machine learning workflows. It discusses challenges of big ML including skill gaps, dimensionality, and model complexity. The solution is presented as a workflow that includes preprocessing, naive implementation, monitoring with dashboards, optimization, A/B testing, and iteration. Key steps are to measure first before optimizing, start small and grow, test infrastructure, and establish a baseline before optimizing models. The document provides examples of applying these workflows at Waze for tasks like irregular traffic event detection, dangerous place identification, and speed limit inference.
Performance Test Automation With GatlingKnoldus Inc.
Gatling is a lightweight dsl written in scala by which you can treat your performance test as a production code means you can easily write a readable code to test the performance of an application it s a framework based on Scala, Akka and Netty.
Production-Ready BIG ML Workflows - from zero to heroDaniel Marcous
Data science isn't an easy task to pull of.
You start with exploring data and experimenting with models.
Finally, you find some amazing insight!
What now?
How do you transform a little experiment to a production ready workflow? Better yet, how do you scale it from a small sample in R/Python to TBs of production data?
Building a BIG ML Workflow - from zero to hero, is about the work process you need to take in order to have a production ready workflow up and running.
Covering :
* Small - Medium experimentation (R)
* Big data implementation (Spark Mllib /+ pipeline)
* Setting Metrics and checks in place
* Ad hoc querying and exploring your results (Zeppelin)
* Pain points & Lessons learned the hard way (is there any other way?)
The document discusses moving from data science to MLOps. It defines MLOps as extending DevOps methodology to include machine learning, data science, and data engineering assets. Key concepts of MLOps include iterative development, automation, continuous integration and delivery, versioning, testing, reproducibility, monitoring, source control, and model/feature stores. MLOps helps address challenges of moving models to production like the deployment gap by establishing best practices and tools for testing, deploying, managing, and monitoring models.
A workshop to demonstrate how we can apply agile and continuous delivery principles to continuously deliver value in machine learning and data science projects.
Code: https://github.com/davified/ci-workshop-app
The quality of data-powered applications depends not only on code, but also on collected data, as well as models trained on data. This renders traditional quality assurance inadequate. We will take a look in our toolbox for more holistic tactics that bridge the gap between code and data quality assurance.
The document provides an overview of machine learning and artificial intelligence concepts. It discusses:
1. The machine learning pipeline, including data collection, preprocessing, model training and validation, and deployment. Common machine learning algorithms like decision trees, neural networks, and clustering are also introduced.
2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
3. The impact of AI on society and the workplace. While AI is predicted to help humans solve problems, some people remain wary of technologies like home health diagnostics or AI-powered education. Responsible development of explainable AI is important.
Certification Study Group - Professional ML Engineer Session 3 (Machine Learn...gdgsurrey
Dive into the essentials of ML model development, processes, and techniques to combat underfitting and overfitting, explore distributed training approaches, and understand model explainability. Enhance your skills with practical insights from a seasoned expert.
Machine Learning is transforming every industry with innovative techniques receiving deserved attention. But turning innovation into value requires integrating into practical technology products, often with the leadership of product managers. We'll talk about how to help your friendly neighborhood Product Owner: identify where ML can make a difference, develop metrics to validate and refine it, identify data to feed it, prioritize work to develop it, and structure teams to deliver it in a satisfying way.
Enroll expert level Online Testing Tools Training by Spiritsofts, Learn Testing Tools Certification Training with Course Material, Tutorial Videos, Attend Demo for free & you will find Spiritsofts is the best Online Training Institute within reasonable fee. Software Testing
Spiritsofts is the best Training Institutes to expand your skills and knowledge. We Provides the best learning Environment. Obtain all the training by our expert professionals which is having working experience from Top IT companies.
The Training in is every thing we explained based on real time scenarios, it works which we do in companies.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Design System as a Product - Maria Elena Duenias, Esther Butcher
Design systems are a great example where web development and design meet. You can find innumerable resources on the internet, books and conferences on how to build them, and how they are exactly what your organization needs. But, building one requires a lot more than following a recipe. In this talk we are going to discuss how to build a design system as an internal product, and how it evolves to become what the users need.
Designers, Developers and Dogs: Finding the magic balance between product and tech - Charlotte Vorbeck, ShareNow and Sahil Bajaj
How can an agile delivery team become a successful product team? When does collaboration between product and tech succeed and when not? Why do people in some teams inspire each other while others in the same environment don't speak the same language? In this talk we want to share our learnings and experiences from rebuilding an internal tool for customer support at ShareNow. What could have been just another boring rewrite surprisingly became one of our best experiences in collaboration. We will look at how a joint discovery phase helped us to come up with a shared vision, how a better team setup enabled us to do the necessary work, how focusing on the customer kept us aligned during our journey, and also how we built upon existing collaborative techniques to achieve this new level of cooperation and trust.
During this presentation, Ward Coessens, ThoughtWorks' Consultant will share best practice insights from the Daimler partnership, helping the automotive group on their cloud innovation journey.
How to create more business impact with flexible teams - Jan Hegewald, Zalando & Rebekka Beels, Zalando
Usually, Software Engineering teams are organized around a fixed set of components which they develop further and maintain. Such component teams gain a high level of expert knowledge about their services. However, with agile product development, it often is difficult to implement the most important initiatives with such teams. This leads to a situation where the teams do not work on the most relevant business topics but on those for the respective team. At Zalando, we introduced a new model where we shape teams flexibly around business goals to create the highest impact. How we organize these teams and which challenges especially for the software quality need to be addressed, will be explored in this talk.
Amazon’s Culture of Innovation & The Working Backwards session
Working Backwards; leading organisations achieve growth by marrying customer-obsession with a modern technology strategy. Where do you begin? By focusing on the customer.
During this webinar, Amazon will discuss key innovation principles which have been instrumental in their continued success and their Working Backwards approach.
Dual-Track Agile for Discovery & Development - Adriana Katrandzhieva
The talk will focus on one of the ways teams can ensure continuous delivery and design in their projects. The so-called ‘Dual-track’ model shows the parallel tracks of discovery and development throughout the product design and delivery process. These continually feedback into each other informing new hypothesis that can be tested in order to be proven/disproven. This model is not always easy to implement out of the box and so I will share my own experiences in applying it in practice - what worked, what didn't and how the model can be adjusted to fit different teams and organisational environments.
This document discusses developer experience (DX) and how to design for it. It begins with introductions of the presenters and defines DX as the experience developers have when using a product. It then discusses understanding developer pain points and personas, designing the developer journey, and using different interfaces like APIs, portals, and CLIs. The document outlines challenges in the design process like getting buy-in and measuring DX metrics. It argues that improving DX benefits business goals like enabling experiments, increasing flexibility, and attracting talent. It concludes that developers are also users and designers, and that DX is important to consider for business reasons.
When we design together - Sabrina Mach, Ammara Gafoor and James Emmott
From three distinct perspectives, this talk will contend that design is an activity undertaken by everyone in a software development team. It occurs throughout the process of delivery — not only at the beginning or the end — and it is a powerful instrument for learning about and adapting to the problems our work seeks to solve, which is a shared responsibility. Making the best use of our multidisciplinary expertise in the activity of design requires forms of collaboration that are too often disrupted by the role-based silos that keep us separated and weaken the valuable contribution our diverse approaches could make to our collective efforts. If you care about accelerating time to market, improving customer experience, or building happy and productive teams, you will want to know why and how it matters that we believe ‘design is in everything that we do’.
Hardware is hard(er): designing for distributed user experiences in IoT - Claire Rowland, www.clairerowland.com
Designing connected devices and hardware-enabled services is significantly more complex than pure software. There are more devices on which code can run, connectivity and data sharing patterns to consider, and often multiple and varied touchpoints for users to interact with. Pulling this all together into a coherent experience involves strong collaboration between design and engineering, and a systems thinking approach to UX. In this talk, we’ll introduce what designers need to know about the tech, what engineers need to know about UX for IoT, and how to facilitate the whole-collaboration needed to create great products.
www.clairerowland.com
Customer-centric innovation enabled by cloudThoughtworks
Working Backwards - Leading organisations achieve growth by marrying customer-obsession with a modern technology strategy. In this upcoming webinar, we’ve partnered with AWS to bring you exclusive insights from one of the world’s most innovative companies, Amazon.
The document discusses Amazon's culture of innovation. It emphasizes starting with the customer and working backwards to develop solutions. Amazon focuses on small, autonomous teams that are nimble and own their work. The company's leadership principles guide decision-making and encourage inventiveness, customer obsession, high standards, and long-term thinking to solve customer problems.
The document discusses techniques for establishing shorter feedback loops between developing features and measuring user behavior, including:
1) Shadow traffic which runs new and old features simultaneously to get early feedback without users noticing a difference.
2) Visual reports which assess the quality of a feature through a report (e.g. HTML page) of key metrics.
3) A/B testing which statistically compares user behavior between a control and test group after exposing each to a different variant of a feature. Sample size considerations and statistical significance are discussed.
As a tech leader at ThoughtWorks, a large part of my job involves recommending practices to our clients so they can build and deliver good quality software faster. In doing so repeatedly for many clients I have created a toolkit that contains practical advice from being on the ground. This is what we do, we know it works. When Julius Caesar entered Rome with his army by crossing the river Rubicon, he did something that couldn’t be undone ever again. In your journey as a leader, avoid mistakes that are difficult to correct later. Here are a set of practices that you want to adopt as soon as possible.
Handling error conditions is a core part of the software we write. However, we often treat it as a second class citizen, obscuring our intent through abuse of null values and exceptions that make our code hard to understand and maintain. In the functional programming community, it is common to use datatypes such as Option, Either or Validated to make our intentions explicit when dealing with errors. We can leverage the compiler to verify that we are handling them instead of hoping for the best at runtime. This results in code that is clearer, without hidden path flows. We’ll show how we have been doing this in Kotlin, with the help of the Arrow library.
Mutation testing in software development surfaced in academia during the 70's and has recently seen a resurgence in popularity as a legitimate tool in your testing arsenal. In this session we review the conventional testing pyramid, modern approaches to testing software and look at how mutation testing can help fill in those blind spots.
The document discusses security challenges and best practices for Docker containers. It outlines risks at different stages of the container lifecycle from image development to deployment. Key risks include lack of isolation, complex ecosystems, and known vulnerabilities. The document recommends practices like using linting and scanning during development, restricting resources and access controls at deployment, and signing images from trusted sources to improve container security.
Mainframes handle 30 billion business transactions each day and 87% of all credit card transactions*, they are not traditionally associated with flexible, fail-fast development approaches. Can we bring the practices of agile, CI/CD and fully automated deployments to applications running on a mainframe? During our talk, we'll tell you a story about test automation; redefining the smallest testable unit of a program. And we'll discuss our learnings from introducing continuous integration and agile practices to the world of insurance and mainframes.
*9 Mainframe statistics that may surprise you
ThoughtWorks' Lucy Kurian, James Lewis & Kief Morris discuss tech trends in our latest Technology Radar, covering techniques, platforms, tools, languages and frameworks.
Waze vs. Google Maps vs. Apple Maps, Who Else.pdfBen Ramedani
Let’s face it, getting lost isn’t really part of the adventure anymore (unless you’re into that sort of thing!). Nowadays, a good navigation app is like your trusty compass, guiding you through busy city streets and winding country roads. But with so many options out there—from big names like Waze, Google Maps, and Apple Maps to some lesser-known contenders—choosing the right one can feel a bit overwhelming.
Think about it: you're about to head out on a road trip, and the last thing you want is to end up in the middle of nowhere because you took a wrong turn. Or maybe you're just trying to navigate your daily commute without hitting every single red light. That's where a solid navigation app comes in handy.
Google Maps is like the old reliable friend who knows every shortcut and scenic route. It's packed with features, from real-time traffic updates to detailed directions, making it a top choice for many. But then there's Waze, the social butterfly of navigation apps. It's all about community, with drivers sharing real-time updates on traffic, accidents, and even speed traps. It’s perfect if you want to feel like you’re part of a huge driving club, all working together to get everyone to their destination faster.
And let’s not forget Apple Maps, which has come a long way since its rocky start. If you're deep into the Apple ecosystem, it's a seamless choice, integrating smoothly with all your devices and offering some pretty neat features like Flyover for 3D city views.
But wait, there are also some underdog apps worth considering! Have you heard of MapQuest? It's still around and offers some great features, especially for planning long trips with multiple stops. Then there's HERE WeGo, which is fantastic for offline navigation—a real lifesaver if you're heading somewhere with spotty cell service.
So, whether you're planning a cross-country adventure or just trying to find the quickest route to work, we’ll help you sift through these options. We’ll dive into what makes each app unique, their pros and cons, and ultimately, guide you to the perfect navigation app for your needs. Buckle up and get ready for a smooth ride!
Generative AI The Key to Smarter, Faster IT Development.pdfayushiqss
Discover how generative AI is transforming IT development in this blog. Learn how using AI software development, artificial intelligence tools, and generative AI tools can lead to smarter, faster, and more efficient software creation. Explore real-world applications and see how these technologies are driving innovation and cutting costs in IT development.
Test Polarity: Detecting Positive and Negative Tests (FSE 2024)andrehoraa
Positive tests (aka, happy path tests) cover the expected behavior of the program, while negative tests (aka, unhappy path tests) check the unexpected behavior. Ideally, test suites should have both positive and negative tests to better protect against regressions. In practice, unfortunately, we cannot easily identify whether a test is positive or negative. A better understanding of whether a test suite is more positive or negative is fundamental to assessing the overall test suite capability in testing expected and unexpected behaviors. In this paper, we propose test polarity, an automated approach to detect positive and negative tests. Our approach runs/monitors the test suite and collects runtime data about the application execution to classify the test methods as positive or negative. In a first evaluation, test polarity correctly classified 117 tests as as positive or negative. Finally, we provide a preliminary empirical study to analyze the test polarity of 2,054 test methods from 12 real-world test suites of the Python Standard Library. We find that most of the analyzed test methods are negative (88%) and a minority is positive (12%). However, there is a large variation per project: while some libraries have an equivalent number of positive and negative tests, others have mostly negative ones.
Contractor PMS Software is intended to make the work process of a group dealing with a similar Software simpler. It is proper for new companies, enormous prepared ones, and, surprisingly, various groups that should team up to execute a task. The product empowers you to dole out and reassign occupations, helpfully track cutoff times to guarantee they are met, and, obviously, control the whole interaction from an centralised management interface.
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Old Tools, New Tricks: Unleashing the Power of Time-Tested Testing ToolsBenjamin Bischoff
In the rapidly evolving landscape of software development and testing, it is tempting to chase the latest tools and technologies. However, some of the most effective solutions have been in existence for decades. In this talk, we’ll delve into the enduring value of these timeless testing tools.
We’ll explore how established tools like Selenium, GNU Make, Maven, and Bash remain vital in today’s software development and testing toolkit even though they have been around for a long time (some were even invented before I was born). I’ll share examples of how these tools have addressed our testing and automation challenges, showcasing their adaptability, versatility, and reliability in various scenarios. I aim to demonstrate that sometimes, the “old” ways can indeed be the best ways.
Fix Production Bugs Quickly - The Power of Structured Logging in Ruby on Rail...John Gallagher
Rails apps can be a black box. Have you ever tried to fix a bug where you just can’t understand what’s going on? This talk will give you practical steps to improve the observability of your Rails app, taking the time to understand and fix defects from hours or days to minutes. Rails 8 will bring an exciting new feature: built-in structured logging. This talk will delve into the transformative impact of structured logging on fixing bugs and saving engineers time. Structured logging, as a cornerstone of observability, offers a powerful way to handle logs compared to traditional text-based logs. This session will guide you through the nuances of structured logging in Rails, demonstrating how it can be used to gain better insights into your application’s behavior. This talk will be a practical, technical deep dive into how to make structured logging work with an existing Rails app.
I talk about the Steps to Observable Software - a practical five step process for improving the observability of your Rails app.
Alluxio Webinar | What’s new in Alluxio Enterprise AI 3.2: Leverage GPU Anywh...Alluxio, Inc.
Alluxio Webinar
July.23, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Shouwei Chen (core maintainer and product manager, Alluxio)
In today's AI-driven world, organizations face unprecedented demands for powerful AI infrastructure to fuel their model training and serving workloads. Performance bottlenecks, cost inefficiencies, and management complexities pose significant challenges for AI platform teams supporting large-scale model training and serving. On July 9, 2024, we introduced Alluxio Enterprise AI 3.2, a groundbreaking solution designed to address these critical issues in the ever-evolving AI landscape.
In this webinar, Shouwei Chen will introduce exciting new features of Alluxio Enterprise AI 3.2:
- Leveraging GPU resources anywhere accessing remote data with the same local performance
- Enhanced I/O performance with 97%+ GPU utilization for popular language model training benchmarks
- Achieving the same performance as HPC storage on existing data lake without additional HPC storage infrastructure
- New Python FileSystem API to seamlessly integrate with Python applications like Ray
- Other new features, include advanced cache management, rolling upgrades, and CSI failover
Bring Strategic Portfolio Management to Monday.com using OnePlan - Webinar 18...OnePlan Solutions
Unlock the full potential of your projects with OnePlan’s seamless integration with monday.com. Join us to discover how OnePlan enhances monday.com by aligning your portfolio of projects with your organization’s strategic goals, optimizing resource allocation, and streamlining performance tracking. Learn how this powerful combination can drive efficiency, cost savings, and strategic success within your organization.
CrushFTP 10.4.0.29 PC Software - WhizNewsEman Nisar
Introduction:
In this never-ending digital world, the essence of a smooth and safe file transfer solution is vital. CrushFTP 10.4.0.29 is a kind of full-featured, robust, and easy-to-use PC software designed for a smooth file transfer process without compromising security. In this review, we will dig in deep regarding the CrushFTP features, functions, and system requirements to have a 360-degree view of its capabilities and possible applications.
Description:
CrushFTP, LLC develop the software, and it comes in a bundle of new features and improvements, which are set to deliver a great experience to the user.With CrushFTP, from the smallest to the most extensive scale of businesses, all kinds of file transfer operations can be centrally managed on a single platform.
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Abstract:
At its heart, CrushFTP is a powerful server that allows users to exchange files over the networks safely. Many features of the FTP servers have been extended in CrushFTP. It supports protocols like FTPS, SFTP, SCP, HTTP, and HTTPS for maximum flexibility with client applications and devices.
The intuitive web interface enables users to use file management tools simply without installing complex client software.
Software Characteristics:
Security:
CrushFTP ensures security through the use of protocols for encryption, such as SSL/TLS, to secure transmitted data. It also offers user authentication mechanisms using LDAP, Active Directory, and OAuth for proper secure access control.
Automation:
The automation capability of CrushFTP allows automating the everyday routine tasks through schedule-based transfer, event-based triggers, and custom flow. This ensures that the batch processing is effective with minimum manual interruption, improving productivity.
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Remote Administration:
CrushFTP supports remote administration through the web interface. This allows an administrator to manage server settings, user permissions, and file operations from any part of the world that is connected to the Internet. In this regard, it gives a very nice distributed team and remote work environment.
Integration:
The software easily integrates with third-party applications and services through a very extensive API, as well as through support for plenty of plugins. This way, it becomes straightforward for organizations to fit CrushFTP into their already existing infrastructure to promote interoperability and ensure scalability.
Monitoring and Logging:
CrushFTP provides very detailed tracking and logging where an administrator can trace all user activities, monitor the performance of the server, and analyze network traffic. It also offers real-time alerts and notifications for proactive management and troubleshooting.
Customization:
Make CrushFTP work with any possible parameters in mind through configurable settings, themes, and extensions
Unified customer experience management tools integrate various customer interaction channels, data, and analytics into a single platform. They streamline customer service, enhance personalization, and provide insights for improving customer satisfaction and loyalty across all touchpoints.
BitLocker Data Recovery | BLR Tools Data Recovery SolutionsAlina Tait
BLR Tools provides an advanced BitLocker Data Recovery Tool specifically engineered to recover lost or inaccessible data from BitLocker-encrypted drives. Whether you're dealing with accidental deletion, encryption key problems, or system crashes, our cutting-edge software guarantees a secure and efficient recovery process. Rely on BLR Tools for dependable BitLocker data recovery and effortlessly restore access to your essential files.
What is Micro Frontends and Why Use it.pdflead93317
🚀 Let's Deep Dive into 𝐖𝐡𝐲 𝐌𝐢𝐜𝐫𝐨 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 🚀
In today's fast-paced tech landscape, agility, scalability, and maintainability are more crucial than ever. Traditional monolithic frontend architectures often struggle to keep up with these demands. Enter Micro Frontends: a revolutionary approach that's transforming the way we build web applications.
Unlocking the Future of Artificial IntelligencedorinIonescu
Unlock the Future: Dive into AI Today! Videnda AI specializes in developing advanced artificial intelligence solutions, including visual dictionaries and language learning tools that leverage immersive virtual travel experiences. Stay Ahead of the Curve: Master AI Now! Our AI technology integrates machine learning and neural networks to enhance education and business applications. AI: The Next Frontier. Are You Ready to Explore? With a focus on real-time AI solutions and deep learning models, Videnda AI provides innovative tools for multilingual communication and immersive learning.
In this course, you'll find a series of engaging videos packed with vibrant animations that break down complex AI concepts into digestible pieces. Our curriculum covers AI models such as Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Generative Adversarial Networks (GAN), and Transformers, providing a solid understanding of these models and their real-world applications. We also offer hands-on experience with Generative AI tools like ChatGPT and Midjourney, and Python programming tutorials to help you implement AI algorithms and build your own AI applications.
We are proud participants in the Nvidia Inception Program, driving AI innovation across various industries. By the end of our course, you'll have a strong understanding of AI principles, enhanced Python programming skills, and practical experience with state-of-the-art Generative AI tools. Whether you're looking to kickstart a career in AI or simply curious about this revolutionary technology, Videnda AI is your partner in mastering the future of artificial intelligence.
pgroll - Zero-downtime, reversible, schema migrations for PostgresTudor Golubenco
pgroll is an open source command-line tool that offers safe and reversible schema migrations for PostgreSQL by serving multiple schema versions simultaneously. It takes care of the complex migration operations to ensure that client applications continue working while the database schema is being updated. This includes ensuring changes are applied without locking the database, and that both old and new schema versions work simultaneously (even when breaking changes are being made!). This removes risks related to schema migrations, and greatly simplifies client application rollout, also allowing for instant rollbacks.
5. Continuous deployment
What it is and why everybody wants it
Idea Develop
Deploy in
prod
● New features on the fly.
● Quality goes up (smaller changes).
6. Continuous deployment
What it is and why everybody wants it
Idea Develop
Deploy in
prod
● New features on the fly.
● Quality goes up (smaller changes).
● Faster development.
7. Continuous deployment
What it is and why everybody wants it
Idea Develop
Deploy in
prod
● New features on the fly.
● Quality goes up (smaller changes).
● Faster development.
● Experimentation.
8. Continuous deployment
Idea Develop
Deploy in
prod
What it is and why everybody wants it
● New features on the fly.
● Quality goes up (smaller changes).
● Faster development.
● Experimentation.
● Innovation.
9. So… we want to reduce the gap between
a new idea and when this idea is in
production.
14. Machine learning
What is it?
● Subset of artificial intelligence.
● Statistical models that systems use to
effectively perform a specific task.
15. Machine learning
What is it?
● Subset of artificial intelligence.
● Statistical models that systems use to
effectively perform a specific task.
● It doesn´t use explicit instructions,
relying on patterns and inference
instead.
16. So… we want to reduce the gap between
a new idea and when this idea is in
production.
18. 2017 The ML Test Score:
A Rubric for ML Production Readiness and Technical Debt Reduction
Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley Google, Inc.
How do we achieve CD?
25. Code
Apply the best practices for writing
your code. Code is always code
● Not only model. Complex systems.
26. Code
Apply the best practices for writing
your code. Code is always code
● Not only model. Complex systems.
● Extreme programming.
27. Code
Apply the best practices for writing
your code. Code is always code
● Not only model. Complex systems.
● Extreme programming.
● Quality gates.
28. Code
Apply the best practices for writing
your code. Code is always code
● Not only model. Complex systems.
● Extreme programming.
● Quality gates.
● Feature toggles.
29. Code
Apply the best practices for writing
your code. Code is always code
● Not only model. Complex systems.
● Extreme programming.
● Quality gates.
● Feature toggles.
● Test Pyramid.
Manual session
based testing
Automated
GUI tests
Automated unit tests
Automated integration tests
Automated API tests
Automated component tests
* Vishal Naik
(Thoughtworks insights)
31. Unlike in traditional software systems,
the ¨behavior of ML systems is not specified
directly in code but is learned from data¨.
32. Unlike in traditional software systems,
the ¨behavior of ML systems is not specified
directly in code but is learned from data¨.
So our tests depend on the sets
of data for training models.
43. Data wrangling/mungling
● Datamart (not data warehouse).
● Be careful with data cooking:
if your features are bad, everything
is bad.
● Data cleaning
46. Get training data
● data scientist. Make their life easier.
● Big data. Importance-weight sampled.
47. Get training data
● data scientist. Make their life easier.
● Big data. Importance-weight sampled.
● Data security.
48. Get training data
● data scientist. Make their life easier.
● Big data. Importance-weight sampled.
● Data security.
● Versioning data.
49. ● data scientist. Make their life easier.
● Big data. Importance-weight sampled.
● Data security.
● Versioning data.
● Training/Serving Skew.
Get training data
51. “All models are wrong”. Common aphorism in Statistics.
”All models are wrong, some are useful”. George Box.
52. “All models are wrong”. Common aphorism in Statistics.
”All models are wrong, some are useful”. George Box.
”All models are wrong, some are useful for a short
period of time”. Tensorflow´s team.
56. First of all
● Design & evaluate the reward function.
● Define errors & failure.
57. First of all
● Design & evaluate the reward function.
● Define errors & failure.
● Ensure mechanisms for user feedback.
58. First of all
● Design & evaluate the reward function.
● Define errors & failure.
● Ensure mechanisms for user feedback.
● Try to tie model changes to a clear metric of the subjective user experience.
59. ● Design & evaluate the reward function.
● Define errors & failure.
● Ensure mechanisms for user feedback.
● Try to tie model changes to a clear metric of the subjective user experience.
● Objective vs many metrics.
First of all
67. Training model
● Feature engineering. (Unbalancing data,
unknown unknowns, etc).
● Be critical with your features: data dependencies
cost more than code dependencies.
68. Training model
● Feature engineering. (Unbalancing data,
unknown unknowns, etc).
● Be critical with your features: data dependencies
cost more than code dependencies.
● Training/serving Skew.
69. Training model
● Feature engineering. (Unbalancing data,
unknown unknowns, etc).
● Be critical with your features: data dependencies
cost more than code dependencies.
● Training/serving Skew.
● Deterministic training dramatically simplifies.
70. Training model
● Feature engineering. (Unbalancing data,
unknown unknowns, etc).
● Be critical with your features: data dependencies
cost more than code dependencies.
● Training/serving Skew.
● Deterministic training dramatically simplifies.
● Tune hyperparameters.
74. Model performance
● Test performance with production data.
● Check your reward functions and failures. E.g: ROC curve.
75. Model performance
● Test performance with production data.
● Check your reward functions and failures. E.g: ROC curve.
● Be careful. Satisfy a baseline of quality in all data slices.
76. Model performance
● Test performance with production data.
● Check your reward functions and failures. E.g: ROC curve.
● Be careful. Satisfy a baseline of quality in all data slices.
● Baseline of accuracy.
77. Model performance
● Test performance with production data.
● Check your reward functions and failures. E.g: ROC curve.
● Be careful. Satisfy a baseline of quality in all data slices.
● Baseline of accuracy.
● Feedback loop.
