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The Best Machine Learning Research of June 2019
Machine Learning and the data science industry is always changing. To keep you updated on the most recent discoveries, we’ve compiled the 5 most exciting machine learning research pieces that expand what we thought we knew about machine learning and the industries to which it relates.  [Related Article: The... Read more
A Manager’s Guide to Starting a Computer Vision Program
So you’re thinking of starting a computer vision program, but you’ve realized now that the logistics are overwhelming. What framework do you use? What infrastructure? Do you go with an out of the box solution or take the time to build your own? Cloud GPU or on-premise? What’s your... Read more
Best Practices for Deploying Machine Learning in the Enterprise
If you’re an organization worried about being left behind with deploying machine learning, it’s not just you. According to Gartner’s Hype Cycle Chart, machine (and deep) learning are the biggest hyped trends of the year. More businesses, organizations, and startups are talking about deep learning and what it means... Read more
Deep Learning for Speech Recognition
Deep learning is well known for its applicability in image recognition, but another key use of the technology is in speech recognition employed to say Amazon’s Alexa or texting with voice recognition. The advantage of deep learning for speech recognition stems from the flexibility and predicting power of deep... Read more
An Introduction to Active Learning
The current utility and accessibility of machine learning is in part due to the exponential increase in the availability of data over time. While data is abundant, labels that are required for specific supervised machine learning tasks can be difficult to obtain. At ODSC West in 2018, Dr. Jennifer... Read more
The New Life of the Travel Industry with Artificial Intelligence
The new online opportunities for travelers has a negative influence on touristic companies. A large number of their potential clients prefer to arrange their vacations on the internet instead of going to travel agencies. But there are a lot of ways new technologies, like AI, can actually help travel... Read more
Watch: Kubeflow and Beyond: Automation of Model Training, Deployment and Testing
Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to your Python application.... Read more
Using Mobile Devices for Deep Learning
A key avenue for deploying deep learning models is a mobile device. The advantage of running models in mobile apps instead of sending them to the cloud is the reduction in latency and the ability to ensure data privacy for users. Despite the variety of deep learning libraries and... Read more
Automating Data Wrangling – The Next Machine Learning Frontier
Up to 95% of a data scientist’s time is spent data wrangling. Conversely, about 99% of data-scientists hate data wrangling. That’s problematic. Data wrangling tends to be the most redundant and mind-numbing process associated with building Machine Learning (ML) models. There are four steps to building an ML model:... Read more
OS for AI: How Serverless Computing Enables the Next Gen of ML
Jon Peck is a Full Spectrum Developer & Advocate for Algorithmia, an open marketplace for algorithms. At ODSC West 2018, he delivered a talk “OS for AI” which discussed how serverless computing enables the next generation of machine learning. The slides for Peck’s presentation can be found HERE.  The... Read more