- Guide To Finding The Right Predictive Maintenance Machine Learning Techniques - Oct 25, 2021.
What happens to a life so dependent on machines, when that particular machine breaks down? This is precisely why there’s a dire need for predictive maintenance with machine learning.
Machine Learning, Maintenance, Monitoring
- Overview of MLOps - Mar 26, 2021.
Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.
Data Science, Deployment, Machine Learning, MLOps, Monitoring
A Machine Learning Model Monitoring Checklist: 7 Things to Track - Mar 11, 2021.
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
Checklist, Data Science, Deployment, Machine Learning, MLOps, Monitoring
- ebook: Fundamentals for Efficient ML Monitoring - Dec 17, 2020.
We've gathered best practices for data science and engineering teams to create an efficient framework to monitor ML models. This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere.
ebook, Machine Learning, Monitoring
- Here’s what you need to look for in a model server to build ML-powered services - Sep 15, 2020.
More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.
Deployment, Life Cycle, MLOps, Model Drift, Model Performance, Monitoring, Production, Scalability
- Monitoring Apache Spark – We’re building a better Spark UI - Jul 23, 2020.
Data Mechanics is developing a free monitoring UI tool for Apache Spark to replace the Spark UI with a better UX, new metrics, and automated performance recommendations. Preview these high-level feedback features, and consider trying it out to support its first release.
Apache Spark, Monitoring, UI/UX
- Observability for Data Engineering - Feb 10, 2020.
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
Data Engineering, DevOps, Explainability, KPI, Monitoring, Time Series
- The Ultimate Guide to Model Retraining - Dec 16, 2019.
Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.
Deployment, Machine Learning, Model Drift, Model Performance, Monitoring, Production, Training Data
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
Anomaly Detection, Deployment, Machine Learning, MapR, Monitoring, Real-time
- Brain Monitoring with Kafka, OpenTSDB, and Grafana - Aug 5, 2016.
Interested in using open source software to monitor brain activity, and control your devices? Sure you are! Read this fantastic post for some insight and direction.
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Brain, Internet of Things, IoT, Kafka, Monitoring
