How Cloud is Affecting Data Scientists

This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/CgoxjmdyMiU This session will discuss how to get up and running quickly with containerized H2O environments (H2O Flow, Sparkling Water, and Driverless AI) at scale, in a multi-tenant architecture with a shared pool of resources using CPUs and/or GPUs. See how how you can spin up (and tear down) your H2O environments on-demand, with just a few mouse clicks. Find out how to enable quota management of GPU resources for greater efficiency, and easily connect your compute to your datasets for large-scale distributed machine learning. Learn how to operationalize your machine learning pipelines and deliver faster time-to-value for your AI initiative — while ensuring enterprise-grade security and high performance. Bio: Nanda Vijaydev is senior director of solutions at BlueData (now HPE) - where she leverages technologies like Hadoop, Spark, and TensorFlow to build solutions for enterprise analytics and machine learning use cases. Nanda has 10 years of experience in data management and data science. Previously, she worked on data science and big data projects in multiple industries, including healthcare and media; was a principal solutions architect at Silicon Valley Data Science; and served as director of solutions engineering at Karmasphere. Nanda has an in-depth understanding of the data analytics and data management space, particularly in the areas of data integration, ETL, warehousing, reporting, and machine learning.

The document discusses machine learning concepts and approaches for practical implementation in enterprises. It defines key terms like business analytics, predictive analytics, and machine learning. Business analytics answer questions about past data through queries, while predictive analytics uses algorithms to predict future probabilities and outcomes. The document also outlines challenges to enterprise adoption of machine learning and how vendors are helping to address skills gaps through cloud-based tools and services.

2018 Women in Analytics Conference https://www.womeninanalytics.org/ Over the last year I’ve become obsessed with learning how to be a better "cloud computing evangelist to data scientists" - specifically to the R community. I’ve learned that this isn’t often an easy undertaking. Most people (data scientists or not) are skeptical of changing up the tools and workflows they’ve come to rely on when those systems seem to be working. Resistance to change increases even further with barriers to quick adoption, such as having to teach yourself a completely new technology or framework. I’d like to give a talk about how working in the cloud changes data science and how exploring these tools can lead to a world of new possibilities within the intersection of DevOps and Data Analytics. Topics to discuss: - Working through functionality/engineering challenges with R in a cloud environment - Opportunities to customize and craft your ideal version of R/RStudio - Making and embracing a decision on what is “real" about your analysis or daily work (Chapter 6 in R for Data Science) - Running multiple R instances in the cloud (why would you want to do this?) - Becoming an R/Data Science Collaboration wizard: Building APIs with Plumber in the Cloud

Big Data analytics is well known to uncover hidden insights that gives an organization an edge over the competition. But data does not need to be big in order to be useful. Smaller companies and startups may lack the volume of data that qualifies as big data, yet the variety of data can still yield a trove of insights that helps in driving the business strategies of a company. Startups may also lack the resources to fund an additional, seemingly expensive development project. The key is in simplicity, start small, simple and architect for scalability and performance. But how do you start? In this presentation, we share our experience in building a cost effective, AWS serverless data analytics platform that became an invaluable tool for sales, marketing and operational efficiencies.Serverless architectures simplify development work where servers and software are managed by a third party cloud provider. Developers can focus on just building the data wrangling and data analysis logic where critical aspects like scalability and high availability are guaranteed by the cloud provider. Besides, serverless services offer the pay as you go model, where you pay only based on the amount of resources you use. This turns out to be another attractive aspect where costs can be managed based on the usage. In this presentation we will focus on techniques and best practices to build a big data analytics platform using AWS serverless services like Lambda, DynamoDB, S3, Kinesis, Athena, QuickSight and Amazon ML. We will highlight the strengths of each of these services and what role each plays in the data analytics pipeline. We compare and contrast these services with some of the other popularly used big data technologies like Hadoop, Spark and Kafka. We also demonstrate the usage of these services to build intelligent components that detect anomalies, yield recommendations, simulate chat bots and generate predictive analytics.

Ayush Gaur has extensive experience and skills in big data analytics, cloud computing, and data science. He holds an M.S. in Computer Science with a concentration in data science from UT Dallas and a B.E. in Computer Science from Chitkara University in India. He has professional experience as an instructor for big data and analytics and as a senior associate focusing on big data, analytics, and cloud computing at Infosys. He has strong technical skills in Apache Spark, Hadoop, Python, and cloud platforms like AWS.

This document discusses democratizing AI using Apache Spark on Databricks. It first discusses how AI is changing the world through advances like AlphaGo but that AI hasn't been fully democratized. It then discusses how Databricks uses Apache Spark to close gaps in managing big data infrastructure, establishing production-ready applications, and empowering teams. Specifically, Databricks provides an integrated workspace, just-in-time data platform, and automated Spark management to accelerate developing and deploying AI applications. The document concludes by discussing how Databricks enables faster and easier deep learning through features like TensorFlow, TensorFrames, GPU support, and a full stack for data ingestion, model training, and productionization.

¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen. ¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático? ¿Se pregunta sobre los diferentes sabores de AutoML? H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses. Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia. H2O Driverless AI hace: * Visualización automática de datos * Ingeniería automática de funciones a nivel de Grandmaster * Selección automática del modelo * Ajuste y capacitación automáticos del modelo * Paralelización automática utilizando múltiples CPU o GPU * Ensamblaje automático del modelo *automática del Interpretaciónaprendizaje automático (MLI) * Generación automática de código de puntuación ¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial. Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics. ¡Te veo pronto! Acerca de H2O.ai H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.

The document discusses building real-time targeting capabilities at Capital One. It introduces two speakers, Ryan Zotti and Subbu Thiruppathy, and describes challenges around striving for speed in everything. It then covers how to achieve fast model data, training, deployment, and scoring through techniques like using the most up-to-date data, distributed computing in the cloud, automatic model refitting, and response times under 100 milliseconds.

The rapid expansion of mobile phone usage in low-income and middle-income countries has created unprecedented opportunities for applying AI to improve individual and population health. In benshi.ai, a non-profit funded by the Bill and Melinda Gates Foundation, the goal is to transform health outcomes in resource-poor countries through advanced AI applications. We aim to do so by providing personalized predictions and recommendations to support diagnosis to medical care teams and frontline workers, as well as to nudge patients through personalized incentives towards an improvement in disease treatment management and general wellness. To this end, we have built an operational machine learning platform that provides personalized content and interventions real-time. Multiple engineering and machine learning decisions have been made to overcome different challenges and to build an experimentation engine and a centralized data and model management system for global health. Databricks served as a cornerstone upon which all our data/ML services were built. In particular, MLflow and dbx (an opensource tool from Databricks) have been crucial for the training, tracking and management of our end-to-end model pipelines. From the data science perspective, our challenges involved causal inference analysis, behavioral time series forecasting, micro-randomized trials, and contextual bandits-based experimentation at the individual level. This talk will focus on how we overcome the technical challenges to build a state-of-the-art machine learning platform that serves to improve global health outcomes.

A plethora of data processing tools, most of them open source, is available to us. But who actually runs data pipelines? What about dynamically allocating resources to data pipeline components? In this talk we will discuss options to operate elastic data pipelines with modern, cloud native platforms such as DC/OS with Apache Mesos, Kubernetes and Docker Swarm. We will review good practices, from containerizing workloads to making things resilient and show elastic data pipelines in action.

Machine learning projects often fail to make it from development to production. Looking at the full machine learning lifecycle is essential for success. The lifecycle includes development, deployment, infrastructure, monitoring, automation, standardization, lineage and reproducibility. A machine learning operations (MLOps) platform can provide an end-to-end system view for increased efficiency, collaboration, and trust across the lifecycle. Key takeaways are to focus on what is important, avoid doing nothing which fails to scale, and doing everything which stifles progress.

Successfully deploying a working machine learning prototype to a production application is a challenging task, frought with difficulties not experienced in traditional software deployments. In this talk, you will learn techniques to successfully deploy ML applications in a scalable, maintainable, and automated way.

Sharon Dashet (Sr. Data Analytics Solution Lead) @ Google Cloud: The worlds of traditional RDBMS and Data Lake Hadoop systems are converging and moving to public cloud and SaaS offerings. In this session, Sharon will share her personal journey as a data professional since the 90s weaved into the history of data management systems. The session will also cover the differences between on-premise and cloud Data Lakes.

This document discusses Pivotal's real time business platform for maximizing the value of data investments. It recommends identifying business problems with high ROI potential, then focusing data solutions on high-speed ingestion, consolidation, real-time queries, and analytics to drive real-time insights. The platform combines Gemfire for fast transactions with Greenplum for analytics. Use cases discussed include predictive maintenance, fraud detection, and recommendation engines. The platform provides a complete solution from data capture and analytics to application integration.

This document summarizes the challenges faced by SocGen, a large French bank, in implementing machine learning at scale using Spark and MLflow. Some key challenges included: 1) Keeping data and models local for regulatory reasons while performing training and prediction, 2) Ensuring reliability when moving models between prototyping and production phases, 3) Managing different Python package dependencies, 4) Tracking and managing many models, and 5) Ensuring high availability of the tracking server. The presentation provided a concrete example of using Spark, MLflow, and Kafka to periodically retrain a model for scoring news articles and handling user feedback in a scalable and reliable way.

15 mars 2017 Groupe Excel et Power BI Sujet: Analyse prédictive dans Excel Conférencier: Robert Luong Ensuite, nous recevrons Robert Luong, qui viendra nous parler de l’analyse prédictive avec Azure ML et l’intégration avec Excel. Azure ML est un service d’analyse prédictive sur l’infonuagique qui permet de créer et de déployer rapidement des modèles prédictifs sous forme de solutions d’analyse. Azure ML fournit non seulement des outils pour modéliser des analyses prédictives, mais également un service entièrement pris en charge, que vous pouvez utiliser pour déployer vos modèles prédictifs sous la forme de services web.

Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.

Learn how you can leverage the elastic, on-demand processing power of Microsoft Azure to create faster, more applicable analytics by viewing this informative webinar. Data Scientist and Author, Ahmed Sherif, demonstrates key analytic use cases that can be spun up quickly with minimal effort and maximum return on investment. To watch the full recording of this webinar, visit http://ccgbi.com/resources/webinars/driving-customer-loyalty-with-AML