En la actualidad, los modelos analíticos son herramientas esenciales para tomar decisiones basadas en datos. Desde prever tendencias hasta optimizar operaciones, los modelos analíticos dependen en gran medida de la calidad de los datos de entrada. La precisión, integridad y relevancia de estos datos son cruciales para obtener resultados confiables
The ongoing impact of inflation on the economy is a persistent news headline. Organizations around the world are exploring how data and AI can help lower costs and improve efficiency. Georgia-Pacific, one of the world’s largest manufacturers of pulp and paper products, is ahead of the curve. They are poised
Making informed decisions quickly is more critical than ever. As markets shift and customer expectations evolve, companies need tools to process vast data and turn insights into actionable strategies. That’s where SAS Decision Builder comes in, now available through Microsoft Fabric's public preview announced at Microsoft Ignite. With SAS Decision
As we move into 2025, AI continues to transform industries in unprecedented ways, driving efficiency, innovation, and productivity. But with this rapid advancement come critical ethical questions. How can we ensure that AI systems protect the rights and well-being of individuals? Manufacturing and agriculture are two essential industries where answering
Cada vez más los datos se han convertido en el corazón de todas las operaciones empresariales. Sin embargo, gestionarlos de manera eficiente y convertirlos en información valiosa sigue siendo un desafío. Una buena gestión de datos es esencial para garantizar resultados confiables, éticos y libres de sesgos; que a su
AI is at its best when it is used to enhance productivity and improve the lives of those it affects. When used correctly, AI can also save lives. That’s the vision driving a new project at SAS, where applied AI models and cameras create a simulated work environment focused on
Una encuesta mundial de profesionales antifraude realizada por la ACFE y SAS revela un increíble entusiasmo por la IA generativa, pero estudios de referencia anteriores sugieren una realidad más difícil. La IA generativa ha cautivado la imaginación del público, y su poder y promesa parecen estar a punto de afectar
En los últimos años, la ciencia de datos ha experimentado un crecimiento exponencial y se ha convertido en un pilar fundamental para las estrategias de las organizaciones en todas las industrias. Sin embargo, para los data scientist experimentados, el panorama del dato se encuentra en un proceso de cambio constante.
In the digital age, the adage "knowledge is power" has evolved into "data is power." It reflects the immense value of high-quality data and a strategic approach to data management. At the heart of any successful modern enterprise lies a robust data strategy coupled with stringent data quality standards. For
Organizations continuously search for innovative ways to optimize their operations and elevate efficiency. One promising frontier is the integration of digital twins for predictive maintenance. However, the true potential of this technology often remains untapped, with many organizations settling for what can be described as “digital shadows.” In this exploration,
No ano passado, assistimos a uma revolução notável no desenvolvimento de modelos generativos de IA e na sua adoção generalizada por indivíduos e empresas. Dois exemplos claros foram o ChatGPT e o DALL-E da OpenAI que, em apenas alguns meses, conseguiram conquistar milhões de utilizadores em todo o mundo, garantindo
1.背景 データ管理と分析の世界では、効率的かつ迅速なデータの転送と書き込みは極めて重要です。特に大規模なデータウェアハウスサービスを利用する際には、このプロセスの最適化が不可欠です。Azure Synapse Analyticsは、そのようなサービスの一つとして注目を集めており、SAS Viyaを使用する多くの企業やデータアナリストも、より効率的なデータハンドリングを追求しています。 SAS ViyaのユーザーはSAS/ACCESS to Microsoft SQL Serverを使用してAzure Synapseにデータを転送および書き込む際に、より高いデータ書き込み効率と転送速度を求めるのは当然です。データ処理能力をさらに強化し、書き込み効率を高めるために、SAS Access to SynapseのBulkLoad機能は非常に優れた選択肢です。BulkLoad機能はデータの書き込み速度を大幅に向上させるだけでなく、Azure Data Lake Storage Gen 2（以下、ADLS2と称する）を利用して、安定かつ安全なデータストレージおよび転送環境を提供します。 ただし、BulkLoad機能を使用する際にはADLS2の設定と構成が関わってくるため、構成および使用のプロセスが複雑に感じられたり、疑問が生じたりすることがあります。このブログの目的は、管理者およびユーザーに対して、明確なステップバイステップの設定プロセスを提供し、構成の過程で見落とされがちなキーポイントを強調することで、設定時の参考になるようにすることです。 以下は本記事内容の一覧です。読者は以下のリンクをで興味のあるセクションに直接ジャンプすることができます。 2.Bulkload機能について 3.BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 3-1.Azure Data Lake Storage (ADLS) Gen2のストレージアカウントの作成 3-2.ストレージアカウントのデータストレージコンテナの作成 3-3.ストレージアカウントの利用ユーザー権限の設定 3-4.データ書き込み用のSASコードの実行 3-5.Azureアプリの設定 4.SAS Viya側の設定とAzure Synapseへの接続 4-1.SAS Studioでの設定 4-2.Azure SynapseのSQLデータベースをSASライブラリとして定義 4-3.Azure Synapseへデータの書き込み 2.Bulkload機能について なぜSAS ViyaがBulkload機能を使用してAzure Synapseに効率的にデータを書き込む際にADLS2サービスが必要なのか、そしてそのプロセスがどのように行われるのかを説明します。 Azure Synapse Analyticsは、柔軟性が高く、高いスループットのデータ転送を可能にするために、COPY
If the last few years have taught us anything it’s this: business disruptions are not rare events. They are the norm. Today’s business leaders are grappling with logistics nightmares, economic upheaval, evolving consumer preferences, rapid technological advancements, regulatory changes, and armed conflicts. While it’s not possible to plan for every
Manufacturing remains a transformative process at its core, converting raw materials into valuable products. While the fundamental essence of manufacturing has endured for centuries, the methods and technologies employed have undergone significant evolution, driven by innovation and the ever-shifting demands of consumers. As we enter 2024, the manufacturing industry is
As 2023 ends, it's important to reflect on the predictions that SAS leaders made at the beginning of the year. Let’s look at some of these predictions and see how accurate they were. We'll explore forecasts related to health care, human resources, AI, data, renewable energy and more. Let's dive
Today’s consumers don’t want to be talked to; they want to have a conversation. They want to be marketed to as individuals, not as faceless members of the masses. Consumer packaged goods (CPG) organizations, in particular, recognize the value of these conversations. This dialogue – via loyalty programs, promotions, social
지난 포스팅(컴퓨터가 사물을 보는 방법 1편, 2편)에서는 CNN(Convolution Neural Network)에서 사용될 수 있는 다양한 기법들과 함께 CNN의 학습이 어떤 방식으로 이루어지는지 알아보았습니다. CNN은 데이터(특히 이미지 데이터)에 합성곱, 풀링, 패딩 등의 작업을 거쳐 특징을 추출한 후 회귀 또는 분류를 수행하는 딥러닝 모델입니다. 오늘 포스팅에서는 지금까지 배운 기법들을 SAS Viya에서 구현하는 실습을
En el último año se ha observado la llegada de nuevos inversores extranjeros a México, sobre todo por la parte de la industria manufacturera, que ha traído consigo crecimiento económico para el país. De acuerdo con datos de la Asociación para la Tecnología de Manufactura (AMT, por sus siglas en
Steam engines sparked the first Industrial Revolution, electricity energized the second, and early automation and the assembly line powered the third. Now, the fourth (often called smart manufacturing) is being shaped by artificial intelligence, advanced analytics, the internet and real-time data. Smart technologies are transforming manufacturing. It starts on the
지난 포스팅에서 컴퓨터 비전의 과거와 CNN(Convolution Neural Network)의 구성 요소, 퍼셉트론, 합성곱층에 대해 알아보았습니다. 합성곱층과 함께 풀링, 활성화 함수, 드랍아웃 등 다양한 요소를 활용한다면 모델의 성능을 더욱 향상시킬 수 있습니다. 오늘 포스팅에서는 CNN에서 사용될 수 있는 다양한 기법들과 함께 CNN의 학습이 어떤 방식으로 이루어지는지 살펴보도록 하겠습니다. 1. CNN의 구성요소 <그림
Las organizaciones saben que modernizarse es una tarea continua y una condición para seguir compitiendo y creciendo en sus respectivos mercados. Cómo abordar el proceso de modernización, sin embargo, requiere una planeación minuciosa, y no está exento de desafíos. Más aún cuando involucra la adopción de tecnologías y procesos de
컴퓨터가 인간보다 잘 하는 몇 가지 분야가 있는데, 그 중 하나가 바로 이미지 인식입니다. 2012년 알렉스넷이 개발된 이후 컴퓨터 비전 분야는 급속도로 성장하여 우리 일상에 자연스럽게 스며들었습니다. 오늘 포스팅에서는 컴퓨터가 이미지를 어떻게 인식할 수 있는지 이론을 중심으로 살펴보도록 하겠습니다. 1. 컴퓨터 비전의 과거 우리가 모니터를 통해 바라보는 이미지의 구조부터 알아보겠습니다.
Debido a la complejidad y cambios en el mercado, las organizaciones de todo el mundo están aprovechando las oportunidades para hacer mejores predicciones, identificar soluciones y dar pasos estratégicos y proactivos, lo que significa que dependen cada vez más de los big data. Sin embargo, en su búsqueda de resistencia
Computer vision is a field of artificial intelligence that teaches computers to understand visuals. Using digital images from cameras and videos and deep learning models, machines can learn to recognize and categorize objects and respond to their surroundings based on what they “see.” Computer vision's accuracy has skyrocketed in the
O nadchodzącej rewolucji związanej z internetem rzeczy (IoT) pisze się dużo od wielu lat. Wiele już słyszeliśmy o zmianach w codziennym życiu i korzyściach dla nas wszystkich, które ta rewolucja ma ze sobą przynieść, ale wciąż nie widać zastosowań IoT na szeroką skalę. Największy rozgłos zyskały dotychczas inteligentne liczniki prądu/gazu,
No mundo hiperconectado em que vivemos, os consumidores podem ter múltiplas jornadas de compras, com interações por sites, aplicativos, redes sociais ou até mesmo por uma ligação telefônica. Apesar das muitas opções de canais de contato disponíveis, o mais relevante para o cliente é a qualidade e o nível de
The need to use less energy is becoming critical to manufacturers worldwide. The transition to a clean energy economy drives new developments in the energy sector. Manufacturers must find ways to reduce energy use to stave off growing internal production costs and remain competitive. Let’s discuss why the global energy
La ciencia ficción ha utilizado la premisa de que existen universos paralelos en los que se viven realidades distintas, detonadas por decisiones o eventos excepcionales. En uno de ellos, los personajes estarían experimentando cosas únicas y distintas a las de otra dimensión que corre a la par. Los protagonistas de
Consumer goods manufacturers have faced significant challenges over the past few years due to rapidly changing demand and supply disruptions in their end-to-end supply chain. As a result, manufacturers have realized the need to strengthen their resilience and have prioritized assessing their manufacturing capacity to maximize output and automation. To
Het tegengaan van verspilling in Lean Manufacturing biedt veel potentieel om efficiënter, kostenbesparend en duurzamer te werken. Data-analyse kan hierbij helpen. De term ‘Lean Manufacturing’ verwijst naar een filosofie die ooit bij Toyota is ontstaan. Het aanpakken van de verschillende vormen van verspilling in de industrie speelt bij deze manier
