Tag: machine learning

Feed: Microsoft Azure Blog. Author: Takuto Higuchi. Robustness is the ability of a closed-loop system to tolerate perturbations or anomalies while system parameters are varied over a wide range. There are three essential tests to ensure that the machine learning system is robust in the production environments: unit testing, data and model testing, and integration testing. Unit testing Tests are performed on individual components that each have a single function within the bigger system (for example, a function that creates a new feature, a column in a DataFrame, or a function that adds two numbers). We can perform unit tests ... Read More

Feed: Microsoft Azure Blog. Author: Takuto Higuchi. Testing is an important exercise in the life cycle of developing a machine learning system to ensure high-quality operations. We use tests to confirm that something functions as it should. Once tests are created, we can run them automatically whenever we make a change to our system and continue to improve them over time. It is a good practice to reward the implementation of tests and identify sources of mistakes as early as possible in the development cycle to prevent rising downstream expenses and lost time. In this blog, we will look at ... Read More

Feed: AWS Public Sector Blog. Author: Sanjeev Pulapaka. Many government agencies employ rules-based systems to identify improper payments. Improper payments are those that either should not be made, or are made in the incorrect amount, due to fraud or other errors. Rules-based techniques involve manually researching, understanding, and identifying patterns and heuristics that are then applied as business rules to flag potential issues. However, this approach increases the amount of time taken to identify improper payments due to the heavy dependence on continuously adding and updating rules based on ever changing and newly emerging patterns. In addition, traditional techniques fail ... Read More

Feed: Recent Announcements. You can now run graph analytics and machine learning tasks on graph data stored in Amazon Neptune using an open-source Python integration that simplifies data science and ML workflows. With this integration, you can read and write graph data stored in Neptune using Pandas DataFrames in any Python environment, such as a local Jupyter notebook instance, Amazon SageMaker Studio, AWS Lambda, or other compute resources. From there, you can run graph algorithms, such as PageRank and Connected Components, using open-source libraries like iGraph, Network, and cuGraph. Today’s launch helps customers to build and innovate faster by simplifying ... Read More

Feed: O'Reilly Media, Inc. Upcoming Titles. Author: O'Reilly Media, Inc.. Whether you are part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guests show you how to run an ... Read More

Feed: The Internet of Things on AWS – Official Blog. Author: Bryan Neff. Introduction Amazon Kinesis Video Streams (KVS) makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. KVS automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs. KVS enables you to play back video for live and on-demand viewing, and quickly build applications that take advantage of ... Read More

Feed: Matillion. Author: Julie Polito; What is machine learning used for?Machine learning has been around for decades, but in the era of Big Data, this type of artificial intelligence is in greater demand than ever before. Why? Simply put, organizations need help sifting through and working with the extraordinary amount of data that our systems are now continuously generating. With machine learning technology, businesses can build automated models that process massive volumes of data quickly and “learn” how to use it to solve problems. Let’s look at some of the uses of machine learning across the business. Machine learning use casesThe ... Read More

Feed: SAS Blogs. Author: Conor Hogan. Just getting started with this series? Make sure to explore Part 1 and Part 2. There are different ways you can use these two tools to accelerate model building, deployment and monitoring. Figure 1 summarizes best practices for conducting ModelOps using SAS Model Manager and Azure Machine Learning. Figure 1: Five best practices to optimize the ModelOps life cycle on the Microsoft Cloud. Best practice 1: Create models with repeatable machine learning pipelines using SAS® Model Manager When data scientists create models in SAS Viya and register in SAS Model Manager, they can innovate ... Read More

Feed: Redis. Author: Isaac Sacolick. The Data Economy is a video podcast series about leaders who use data to make positive impacts on their business, customers, and the world. To see all current episodes, explore the podcast episodes library below. You can’t just put your machine learning (ML) models in the cloud when your algorithms must process thousands to tens of thousands of transactions per second within 10 and 20 milliseconds. And you can’t sacrifice accuracy or explainability when your ML models impact roughly 80% of all credit card transactions. These are some of the requirements Scott Zoldi, FICO’s Chief ... Read More

Feed: R-bloggers. Author: R Views. This is a guest post from Gary Hutson, lead of Machine Learning at Crisp Thinking, a company that provides AI solutions to moderate and detect offensive and abusive content online. His website is available at https://hutsons-hacks.info/ and he can be reached through Twitter, @StatsGary. MLDataR package motivation I love all things Machine Learning. The MLDataR package was driven by the need to have example datasets across the healthcare system for machine learning problems. I have been a machine learning practitioner for over nine years; however, I still find it interesting to explore new examples and ... Read More