We’re excited to welcome @MosaicML to Databricks! Together, we’re excited to: Rapidly democratize model capabilities Make #generativeAI work for enterprises Unifying the #AI and #data stack Learn more
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Tidier: AI and Machine Learning Resource Link
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Tidier https://
bit.ly/3DOG5NK
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Drive Performance Through Data-Driven Collaboration and Innovation
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See real-world customer stories that show how you can drive performance with a culture of collaboration, sharing, and innovation with data. Watch #AlteryxInspire sessions with leaders like @BankofAmerica
, @WestRock
, and @RoyalCaribbean today: https://
ow.ly/G1Zc50Pux5O -
Databricks Enhances Monitoring and Observability for Production Workflows
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Enhanced monitoring and observability in #DatabricksWorkflows is here These new features will simplify your daily operations by allowing you to see across your production workflows while optimizing productivity for #data practitioners. Learn more https://
bit.ly/3KdRxGu -
Zombie Email Accounts: Managing Dormant Access After Employee Departure
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Anyone here (especially managers) have any good stories about what happens to email accounts when somebody leaves a job? heard one the other day about a zombie email account that was dormant for 8 yrs ( person got a new job at their old company all their old emails were there!)
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Legal Data Pipelines Enable AI Projects to Survive Criticism
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If there is such a plausible data pipeline that makes it legal, then it'd be easy to keep the project online in the face of criticism.
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Hybrid Cloud AI Strategy Guide for Data Executives
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Unlock hybrid cloud's potential to boost enterprise AI with our white paper on the top AI considerations every Chief Data & Analytics exec should know. Produced by @DominoDataLab
, @nvidia & @ventanaresearch. https://
domino.buzz/3Yhtexb #AI #DataScience #HybridCloud. -
The Future Of Product Management: Embracing AI’s Revolution
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The Future Of Product Management: Embracing AI's Revolution
#AI #AIio #BigData #ML #NLU #Futureofwork @chrismessina @ChristopherIsak @davidwkenny @debashis_dutta @petitegeek @fabiomoioli @GaryMarcus @asokan_telecom http://
ow.ly/Yi9p30swABy -
Evaluation: Testing Neural Networks on Unseen Data Points
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7. Evaluation: You should always keep aside some data points from your original training set for testing. Here we evaluate how the NN predicts data points it's never been trained on. 8/10
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Neural Network Training: Iteration and Data Requirements
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6. Iteration: We repeat steps 2 to 6 for all the data points in your training set multiple times (epochs). Hence, your neural network is likely to be a better fit, if you have more training data points. 7/10