Machine learning modeling
There are a number of ways to tackle machine learning modeling. Find content on supervised and unsupervised machine learning algorithm training and modeling processes, including expert tips to help you evaluate, choose and use the right machine learning model to deliver accurate predictive outputs to solve business problems.
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Machine learning modeling News
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June 20, 2019
20
Jun'19
DataRobot acquisition of ParallelM to enhance AI management
DataRobot machine learning tools should see a boost following the company's acquisition of ParallelM, a startup with tools for deploying, managing and monitoring AI.
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June 04, 2019
04
Jun'19
New Nuxeo AI platform gives enterprises custom models
Nuxeo Insight Cloud is an AI platform that enables an enterprise to use its own data with machine learning models to improve accuracy by being enterprise-specific.
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May 07, 2019
07
May'19
Google's Cloud TPU v2, v3 Pods accelerate ML training
For the first time, Google Cloud TPU Pods are available in public beta, enabling machine learning developers and engineers to more quickly deploy and train models.
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April 25, 2019
25
Apr'19
Databricks and Microsoft collaboration boosts MLflow tool
Databricks' MLflow gets support from Microsoft and new features, including easier model tracking, as the open source tool prepares for its 1.0 release next month.
Machine learning modeling Get Started
Bring yourself up to speed with our introductory content
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Reinforcement learning applications provide focused models
Goal-driven AI uses trial-and-error learning methods to find optimal solutions to enterprise problems, while distancing themselves from requiring human maintenance. Continue Reading
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Enterprise AI collaboration tools take tips from dating apps
Enterprise AI collaboration is turning to an unlikely source for inspiration: dating apps that have long used machine-learning based personalization and communication. Continue Reading
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Data science and machine learning platforms advance analytics
Data science platforms include a variety of technologies for machine learning and other advanced analytics uses. This handbook examines them and how they can be used. Continue Reading
Evaluate Machine learning modeling Vendors & Products
Weigh the pros and cons of technologies, products and projects you are considering.
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Multimodal machine learning in healthcare aids patient consults
In this Q&A, Louis-Philippe Morency talks about how he's building algorithms that capture and analyze the three V's of communication -- voice, verbal and visual. Continue Reading
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Federated deep learning offers new approach to model training
Training deep learning models puts a massive strain on enterprise infrastructure, but federated learning, which trains models on endpoint devices, could lessen some of the demand. Continue Reading
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Data preparation for machine learning still requires humans
Looking to AI to automate more of your processes? Don't overlook the labor that's still needed to prepare data for training machine learning and AI algorithms. Continue Reading
Manage Machine learning modeling
Learn to apply best practices and optimize your operations.
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How to improve candidate matching with AI in HR recruitment
AI in HR recruitment can source talent externally but can also be used to improve internal projects. AI platforms can match qualified existing employees to potential open projects. Continue Reading
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Better together: Predictive analytics and AI boost each other
Enterprises have long seen the value of predictive analytics, but now that AI is starting to influence forecasting tools, the benefits may start to go even deeper. Continue Reading
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3 ways to make machine learning in business more effective
Dun & Bradstreet analytics exec Nipa Basu offers three tips on how to integrate machine learning tools into business processes to help drive better decision-making. Continue Reading
Problem Solve Machine learning modeling Issues
We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.
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How to solve the black box AI problem through transparency
Ethical AI black box problems complicate user trust in the decision making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading
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March Madness analytics, AI help data scientist fill bracket
To create his March Madness bracket predictions, the head of data science at DataRobot uses a host of machine learning algorithms and some predictive analytics. Continue Reading
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GDPR regulations put premium on transparent AI
As the EU's GDPR regulations go into effect, enterprises must focus on building transparency in AI applications so that algorithms' decisions can be explained. Continue Reading