Machine learning
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14 October 2019
4 min read
Machine learning engineers and data scientists are not the same role, although there is often the misconception that they are synonymous. While there are areas of overlap or reliance on one another, there are very distinct differences between these two roles in computer science.  What is a machine...
8 October 2019
4 min read
Customers have an abundance of options when it comes to products for purchase. This excess of options, however, increases the risk of poor customer retention. Since acquiring new customers costs much more than keeping current customers, a higher retention rate is always better. Customer retention represents...
30 September 2019
2 min read
TensorFlow 2.0 shipped today, 30 September 2019, with new features, such as faster debugging and iteration with Eager Execution, a TensorFlow-enhanced implementation of the Keras API, and simplification and compatibility improvements across its APIs. TensorFlow 2.0 is a major upgrade that should increase...
26 September 2019
6 min read
While consumer-facing applications of machine learning (ML) have gotten a lot of attention (Netflix, Uber, and Amazon) the back office deserves some recognition. Enterprise-level systems that run the business—think finance, robo-advisors, accounting, operations, human resources, and procurement—tend...
19 September 2019
3 min read
Continuous Integration and Continuous Deployment (CI/CD) are key components of any mature software development environment. During CI, newly added code is merged into the codebase, kicking off builds and automated testing. If all tests succeed, then the CD phase begins, deploying the changes automatically...
23 August 2019
6 min read
In software development, it makes sense to create reusable, portable, self-contained modules that can seamlessly plug into any application. As the old adages insist: rely on modular design, don’t repeat yourself (DRY), and write once, run anywhere. The rise of API-first design, containerization, and...
15 August 2019
3 min read
Are you an experienced fullstack developer looking to bring machine learning to your apps? Or are you an ML expert who wants to build a website to have a place to show off your models? In any event, the process of bringing AI to applications can be laborious and confusing—but it doesn’t have to...
9 August 2019
5 min read
As companies begin developing use cases for machine learning, the infrastructure to support their plans must be able to adapt as data scientists experiment with new and better processes and solutions. Concurrently, organizations must connect a variety of systems into a platform that delivers consistent...
31 July 2019
9 min read
Tableau combines the ease of drag-and-drop visual analytics with the flexibility to dynamically integrate data from analytical systems. Algorithmia lets analysts go even further, extending worksheets with machine learning (ML) and allowing for the execution of Java, Node.js, Python, R, Ruby, Rust, and...
5 July 2019
8 min read
Developing processes for integrating machine learning within an organization’s existing computational infrastructure remains a challenge for which robust industry standards do not yet exist. But companies are increasingly realizing that the development of an infrastructure that supports the seamless...