Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value across all domains. More and more businesses are investing in ML capabilities, putting together data science teams to develop innovative, predictive models that provide the enterprise with a competitive edge — be it providing better customer service or optimizing logistics and maintenance of systems or machinery.

One particular feature requested by our customers is 3D body mapping, and whether exporting 3D models and utilizing its visualization and filtering can be applied easily. This technical walkthrough shows you how to leverage Yellowfin to integrate 3D models within Yellowfin and then use them to create a fully interactive display in your dashboard.

Data is produced and consumed at volumes and speeds which were unimaginable just a decade ago.Top players have taken advantage of this growth. Tapping into data resources for actionable insights - aptly called the new oil - makes data-driven companies dominate their competition. But the proliferation of data can lead to growing pains. Companies find themselves increasingly incapacitated by the vast and messy nature of their in-house data.

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

Most blogs in my history are very focused on Industry 4.0’s digital transformation of the manufacturing industry, which in itself is pretty remarkable. By 2025, Industry 4.0 is expected to generate greater than $11 trillion in economic value as connected manufacturing processes, operations and their supply chains become more streamlined, efficient, agile and realize improved productivity, improved uptime and product quality.

Since becoming Databox customers, Knowmad is able to set big picture goals for their clients and have more time to focus on building long-term client relationships.

There is an undeniable truth that nobody can unsee: 2020 accelerated the digitalization of the world like no other time in the past. Individuals shifted en masse to interact, shop, play, learn, and even go to the doctor online. On the same note, organizations migrated internal and customer-facing operations to a digital realm, regardless of their size, location, or goals.

We created the Terraform API gateway module to help you follow DevOps best practices while implementing Kong using infrastructure as code (IaC). Terraform is an open source tool that allows you to implement IaC using a declarative declaration definition. This Terraform module is the reference platform maintained by Kong for potential and existing customers to quickly set up both Kong Gateway and Kong Enterprise for demo and PoC environments.

Rate limiting protects your APIs from inadvertent or malicious overuse by limiting how often each user can call the API. Without rate limiting, each user may make a request as often as they like, leading to “spikes” of requests that starve other consumers. Once enabled, rate limiting can only perform a fixed number of requests per second. A rate limiting algorithm helps automate the process. In the example chart, you can see how rate limiting blocks requests over time.

“We’ve seen two years’ worth of digital transformation in two months” said Microsoft’s Satya Nadella. Due to COVID-19, digital transformation roadmaps have been deleted, redrafted, doubled down and accelerated by up to a decade. Traditional companies are moving by osmosis towards streaming technologies such as Apache Kafka to kick off new digital services. But how much should it cost to experience 2030 in 2021?