Operationalization of Machine Learning Models
Lots of businesses want to use machine learning, but few are ready to integrate machine learning into a real-life context of operations. Dr. Mufajjul Ali, Data Solutions Architect for Microsoft, outlines how Microsoft is addressing these needs and offers some advice for businesses looking to operationalize ML models and... Read more
Ensemble Models Demystified
Ensemble models give us excellent performance and work in a wide variety of problems. They’re easier to train than other types of techniques, requiring less data with better results. In machine learning, ensemble models are the norm. Even if you aren’t using them, your competitors are. Kevin Lemagnen is... Read more
The Complete Guide to Decision Trees (part 1)
In the beginning, learning Machine Learning (ML) can be intimidating. Terms like “Gradient Descent”, “Latent Dirichlet Allocation” or “Convolutional Layer” can scare lots of people. But there are friendly ways of getting into the discipline, and I think starting with this guide to decision trees is a wise decision.... Read more
What are MLOps and Why Does it Matter?
During the industrial revolution, the rise of physical machines required organizations to systematize, forming factories, assembly lines, and everything we know about automated manufacturing. During the first tech boom, Agile systems helped organizations operationalize the product lifecycle, paving the way for continuous innovation by clearing waste and automating processes... Read more
How to Choose Machine Learning or Deep Learning for Your Business
AI is the future, or so you’re hearing. Every day, news of another organization leveraging AI to produce business outcomes that outstrip competition hit your inbox, but your company either hasn’t started at all or is mired in the discussion. AI, machine learning, and deep learning are sometimes used... Read more
25 Excellent Machine Learning Open Datasets
Your machine learning program is only as good as your training sets. Data sets are an integral part of the quality of your machine learning, but you may not always have access to data behind closed walls or the budget to purchase (or rent) the key. Don’t despair. There... Read more
5 Roadblocks to Getting an ML System in Production
We typically meet an organization’s data science team after they’ve carried out a successful proof of concept. The algorithm they built or acquired produced results that were promising enough to greenlight development of a production ML system. It’s at this point that the immaturity of ML project management often... Read more
Properly Setting the Random Seed in ML Experiments. Not as Simple as You Might Imagine
Join Comet at Booth 406 in the ODSC East Expo Hall. We will also be speaking at ODSC: – April 30, 9 am — A Deeper Stack for Deep Learning: Adding Visualizations + Data Abstractions to your Workflow (Douglas Blank, Head of Research) – May 2, 2:15 pm... Read more
4 Steps to Start Machine Learning with Computer Vision
In 2012, AlexNet took first place at the ImageNet Large Scale Visual Recognition Challenge, marking the first time a convolutional neural network had won the image classification competition. One more factor that made this achievement much more significant is that AlexNet showed twice the accuracy than the second-place participant.... Read more
Darwin: Machine Learning Beyond Predefined Recipes
The same way a tailored suit feels and looks different from generic options because it actually fits, tailored models perform differently than pre-established boxed algorithms because they are custom-fitted to your data. To answer this need, SparkCognition has developed Darwin™, a machine learning product that automates the building and... Read more