Data Science
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2 cloud and AI myths you shouldn’t believe
If you think the cloud will drive data centers to extinction and that AI projects are doomed to fail, think again
Pandas 1.0 brings big breaking changes
The first major point release of the powerful Python data analysis package removes many features and deprecates many others
Netflix open sources data science management tool
Metaflow manages Python data science projects end-to-end, works with any machine learning library, and integrates with AWS cloud services
Simplifying data management in the cloud
Enterprises are bogged down in a sea of data complexity as they expand the use of cloud-native databases. Could self-identifying data be the answer?
6 best programming languages for AI development
Which programming language should you pick for your machine learning or deep learning project? These are your best options
Handicapping the AI modeling horse race
AI has become a core focus for application developers everywhere. It’s as hot in the consumer space as it is in business, industry, research, and government
Data science and cloud computing win most political campaigns
With the 2020 elections a year away, technology will play a bigger role than policy. Enterprises should learn something
Why retailers can’t get enough data scientists
As the retail apocalypse continues – with an estimated 8,600 closing their doors in 2019 – other retail survivors are upping their game with hard-to-find data scientists
Google previews site for sharing machine learning experiments
TensorBoard.dev allows you to upload and share TensorBoards and visualize model graphs and metrics
Data integration platforms every developer should understand
Knowing the new data practices and machine learning technologies is vital for software developers to create business value
Artificial intelligence today: What’s hype and what’s real?
Two decades into the AI revolution, deep learning is becoming a standard part of the analytics toolkit. Here’s what it means
Semi-supervised learning explained
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
PyTorch vs. TensorFlow: How to choose
If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options
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
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
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
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
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