Organizations need a consolidated view of their applications, but typically application health status is siloed: end-users complain on social media platforms, operational data coming from application logs is stored on complex monitoring tools, formal ticketing systems track reported issues, and synthetic monitoring data is only available for the tool administrators.
Fully automated smart factories use artificial intelligence (AI) and machine learning (ML) to analyze data, run systems, and improve processes over time. In manufacturing, computer vision on the edge is an increasingly popular Internet of Things (IoT) application used in safety and quality assurance applications.
A recurring business problem is achieving the ability to capture data in near-real time to act upon any significant event close to the moment it happens. For example, you may want to tap into a data stream and monitor any anomalies that need to be addressed immediately rather than during a nightly batch.
Data transfer charges are often overlooked while architecting a solution in AWS. Considering data transfer charges while making architectural decisions can help save costs. This blog post will help identify potential data transfer charges you may encounter while operating your workload on AWS.
This walkthrough shows how to use Azure Data Lake to do data exploration and binary classification tasks on a sample of the NYC taxi trip and fare dataset to predict whether or not a tip is paid by a fare.
The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning by suggesting how team roles work best together.
In our constantly evolving digital world, the companies that come out on top are those that can access the data they need, when they need it. Accessing data in real time enables your company to analyze, plan and improve continuously, so you can make better strategic decisions and reduce your overall risk.
Starting this week, we’re adding new content to the BigQuery Spotlight Youtube series. Throughout the summer we’ll be adding new videos and blog posts focused on helping new BigQuery architects and administrators master the fundamentals.
Many organizations are moving their data from silos and aggregating it in one location. Collecting this data in a data lake enables you to perform analytics and machine learning on that data. You can store your data in purpose-built data stores, like a data warehouse, to get quick results for complex queries on structured data.
Data scientists continuously look for ways to accelerate time to value for analytics projects. RStudio Server is a popular Integrated Development Environment (IDE) for R, which is used to render analytics visualizations for faster decision making.
For organizations moving towards a serverless microservice approach, Amazon DynamoDB has become a preferred backend database due to its fully managed, multi-Region, multi-active durability with built-in security controls, backup and restore, and in-memory caching for internet-scale application. , which you can then use to derive near-real-time business insights.
Customers create business continuity plans and disaster recovery (DR) strategies to maximize resiliency for their applications, because downtime or data loss can result in losing revenue or halting operations. Ultimately, DR planning is all about enabling the business to continue running despite a Regional outage.
This post was co-written with Marcus Rosen, a Principal – Machine Learning Operations with Rio Tinto, a global mining company. Data pre-processing is an important step in setting up Machine Learning (ML) projects for success. Many AWS customers use Apache Spark on AWS Glue or Amazon EMR to run data pre-processing scripts while using Amazon SageMaker to build ML models.
Password-based access control alone is not considered secure enough, and many organizations are adopting multi-factor authentication (MFA) and single sign-on (SSO) as a de facto standard to prevent unauthorized access to systems and data.
