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Automated machine learning or AutoML explained
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
How to do real-time analytics across historical and live data
5 in-memory computing platform capabilities that support analytical processing of both data lake data and operational streams
![Do More With R [video teaser/video series] - R Programming Guide - Tips & Tricks](https://images.idgesg.net/images/article/2018/05/do_more_with_r_teaser-100758232-small.3x2.jpg)
“Do More with R” video tutorials
Search for R video tutorials by task, topic, or package. Most videos are shorter than 10 minutes
HPE plus MapR: Too much Hadoop, not enough cloud
MapR gives HPE superior big data analytics technology and expertise, but not what HPE needs most
Stop searching for that data scientist unicorn
Instead, focus on hiring the technical skills needed to build the team, and the soft skills needed to work on the team
The best NoSQL databases
Highly flexible and hugely scalable, NoSQL databases offer a range of data models and consistency options to suit your application
Unsupervised learning explained
Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow
Get started with AI using ML.Net and Model Builder
Microsoft’s .Net machine learning tooling makes it easy to add AI to your code
The best graph databases
These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data
5 machine learning tools to ease software development
AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation
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How to make your own ggplot2 functions
See how easy it is to make your own functions using ggplot2, dplyr, and other tidyverse package functions – without worrying about quoted and unquoted column names! Thanks to the latest version of the rlang package, tidy evaluation is...
Applying devops in data science and machine learning
Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key
How to use .SD in the R data.table package
See how to use data.table's special .SD symbol to perform calculations and other tasks by group
video
How to use .SD in the data.table package
See how to use the special .SD symbol in the R data.table package.
The best machine learning and deep learning libraries
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models
Julia vs. Python: Which is best for data science?
Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job
TensorFlow 2 review: Easier machine learning
Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment
The data lake is becoming the new data warehouse
Platforms like AWS Lake Formation and Delta Lake point toward a central hub for decision support and AI-driven decision automation
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