How to manage machine learning models
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InfoWorld Daily

January 09, 2019

How to manage machine learning models

Monitoring and management not only keep models in tune, but can help streamline the machine learning process Read more â¶

Your Must Read Stories

â¢	How to manage machine learning models

â¢	What is Kubernetes? Container orchestration explained

â¢	Whatâs new in Angular: Version 7.2 is here

â¢	GitHub offers free private repos for small projects

â¢	Get started with natural language processing

â¢	How to work with Azure API Management

â¢	Pythonâs popularity surges as a mainstay language

â¢	Cloud migration checklist: The 3 key areas to focus on

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What is Kubernetes? Container orchestration explained

How the Kubernetes open source project from Google makes containerized applications astonishingly easy to deploy, scale, and manage Read more â¶

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Whatâs new in Angular: Version 7.2 is here

GitHub offers free private repos for small projects

The code-sharing site also linked its separate cloud and on-premises products into one enterprise service Read more â¶

Get started with natural language processing

Software is going beyond storing and retrieving unstructured information by using NLP to improve user experiences, manage complex information, enable chatbot dialogs, and perform text analytics Read more â¶

How to work with Azure API Management

Add Azureâs API Management tools to secure and manage access to your code for serverless and microservices applications Read more â¶

Pythonâs popularity surges as a mainstay language

Java, C, C++, VisualBasic.Net, Kotlin, Matlab, Rust, and TypeScript also gain while Ruby and F# slip Read more â¶

Cloud migration checklist: The 3 key areas to focus on

80 percent of your risk comes from these areas. Address them before you migrate to get the results you want in sane, cost-effective way Read more â¶

Miscellaneous: DataStax

How Active Everywhere Database Architecture Accelerates Hybrid Cloud Delpoyments

With data stored in on-premises data centers, public clouds, and private clouds, companies spend considerable time and effort coordinating and creating data parity for insights and analytics between these various cloud states. Read more â¶

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