How to Prepare for an Automated Future: 7 Steps to Machine Learning
The increasingly digital economy requires boards and executives to have a solid understanding of the rapidly changing digital landscape. Naturally, artificial intelligence (AI) is an important stakeholder. Those organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an... Read more
What Do Managers and Decision Makers Need to Know About AutoML?
You want to be working in machine learning and artificial intelligence, but you don’t have the talent yet. You’re telling your board members that you’re using AI when you’re really just doing some basic data analysis. You feel like everyone is working in ML and you’re... Read more
Adapting Machine Learning Algorithms to Novel Use Cases
If there was a metric for success in the data science profession, it would require a multi-dimensional scoring model. This metric would cover a data scientist’s technical skills and talents, analytic literacies and ways of thinking, and soft skills and aptitudes.  Soft skills include a collection... Read more
Making Fairness an Intrinsic Part of Machine Learning
The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc are considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science... Read more
Optuna: An Automatic Hyperparameter Optimization Framework
Note: Please go here to see a high-resolution version of the title image) Preferred Networks has released a beta version of an open-source, automatic hyperparameter optimization framework called Optuna. In this blog, we will introduce the motivation behind the development of Optuna as well as its... Read more
What is MLPerf?
AI might be a buzzword, but the hype is outpacing tools to ensure benchmarks. Up to this point, assessing the performance of ML software was difficult. You couldn’t just measure it objectively against other types of frameworks. Now, a collection of tech companies have released MLPerf,... Read more
RAPIDS 0.8: Same Community New Freedoms
RAPIDS released 0.8 a few weeks back. And afterwards, like most Americans, we took off for the 4th of July holiday. Over that break, I reflected on the purpose of RAPIDS. Speed is great, building a strong community is awesome, but the true power of RAPIDS is... Read more
Cracking the Box: Interpreting Black Box Machine Learning Models
Intro To kick off this article, I’d like to explain the interpretability of a machine learning (ML) model. According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretability provides us with an understandable explanation of how a... Read more
Watch: No Black Boxes: Understandability, Transparency, and Governance in Machine Learning
In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an... Read more
NVIDIA GPUs and Apache Spark, One Step Closer
While RAPIDS started with a Python API focus, there are many who want to enjoy the same NVIDIA GPU acceleration in Apache Spark; in fact, we have many at NVIDIA. When RAPIDS first launched, we had a plan to accelerate Apache Spark as well as Dask, and we want to share some... Read more