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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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.
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April 17, 2019
17
Apr'19
Real-time machine learning with Splice Machine's ML Manager
Splice Machine's AI management software ML Manager, shown at Spark + AI Summit 2019, enables users to update their machine learning models with up-to-date data from varied sources.
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April 10, 2019
10
Apr'19
Cloud AutoML, BigQuery ML upgrades expand Google stake in ML
Google introduced a raft of updates to its cloud-based machine learning and AI products, including expanded capabilities for its AutoML and BigQuery ML tools.
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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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
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What-if business planning simulation at its predictive best
Simulating timely and accurate business scenarios can be an essential competitive weapon for predicting the performance, pitfalls and benefits of strategic initiatives. 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