The VP of Engineer's complex role as a translator between the development team and the executive team makes it possibly the most executive role.
Forecast Error Measures: Scaled, Relative, and other Errors. Following through from my previous blog about the standard Absolute, Squared and Percent Errors, let’s take a look at the alternatives — Scaled, Relative and other Error measures for Time Series Forecasting.
How to evaluate Machine Learning models? In this part i.e. part 1 we are going to cover varoius metrics. So without any further delay lets start.
Goodhart’s Law for Data Science and what happens when a measure becomes a target? When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law.
Different types of distance metrics used in Machine Learning. In this article, we will go through 5 of the most commonly used distance metrics.
The Definitive Guide to Designing Product Metrics. Discussing Precision and Recall metrics, metric design interviews, and the metric lifecycle
Once a Data Scientist, there are certain skills you will apply each and every day of your career. Some of these might be common techniques you learned during your education, while others may develop fully only after you become more established in your organization. Continuing to hone these skills will provide you with valuable professional benefits. In this tutorial, you'll see 5 Concepts Every Data Scientist Should Know
We start the problems with metric selection as to know the baseline score of a particular model. In this blog, we look into the best and most common metrics for Multi-Label Classification and how they differ from the usual metrics.
Forecast Error Measures: Understanding them through experiments. In this blog post, let’s explore the different Forecast Error measures through experiments and understand the drawbacks and advantages of each of them.
Mean Average Precision for Clients. Non-technical explanation of Mean Average Precision metric
Mean Average Precision for Clients.Non-technical explanation of Mean Average Precision metric
A brief introduction to the most important metrics used in machine learning for evaluating classification, regression, ranking, statistical, vision, NLP, & deep learning models
This post was written by Dean Record, Engineer at Goji Investments. In the four years since we launched, Goji Investments’ developers have worked hard to help us expand our product offering and bring in new customers. In return, we’ve done everything to keep them happy, and that means supplying them with the right tools to help them transition from building our platform to scaling it
Journey of Apache Kafka & Zookeeper Administrator. Consumer Group Monitoring is very Important because it provides stats about consumer applications and how far aka lag the application is from actual stream of data.
Deepa Kalani and Ramiro Salas from the VMware team spoke at SpringOne 2020 Conference last week about the service mesh product and how it helps developers with Global Namespaces to implement access control and security policies, as well as visualization tools to show application-centric metrics.
In this article, I decided to share the implementation of these metrics for Deep Learning frameworks. It includes recall, precision, specificity, negative predictive value (NPV), f1-score, and Matthews' Correlation Coefficient (MCC). You can use it in both Keras or TensorFlow v1/v2.
APIs are an essential component of today's digitalization and digital transformation efforts. Take a look at how you can measure them with KPIs. KPIs for APIs: How They Are Used in The Real World [Video]
What metrics you should use to measure the performance of your hierarchical classification model. Hierarchical machine learning models are one top-notch trick. As discussed in previous posts, considering the natural taxonomy of the data when designing our models can be well worth our while. Instead of flattening out and ignoring those inner hierarchies, we’re able to use them, making our models smarter and more accurate.
Dave Longman, an account manager with UK software firm HeadForwards, recently published an article titled Working from Home and its Impact on Productivity.
With the DevOps revolution we suddenly find ourselves with autonomous teams who can take full ownership and not only develop features but also follow up on their usage, put them in production and fix them when they break. Without operations, the developers suddenly need to care about things such as logging, metrics and container orchestration. How can we get our developers spend more time on building their product and less time on all the tooling?