Remove Data Processing Remove Deep Learning Remove Experimentation Remove Testing
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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. This personalized approach might lead to more effective therapies with fewer side effects.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Managing Machine Learning Projects” (AWS).

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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability. Production Monitoring and Development Testing.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Machine learning model interpretability. At CMU I joined a panel hosted by Zachary Lipton where someone in the audience asked a question about machine learning model interpretation. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Here’s a sampler of related papers and articles if you’d like to dig in further: “ Synthesizing Programs with Deep Learning ” – Nishant Sinha (2017-03-25). “ Software writes Software?

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Make Better Data-Driven Decisions with DataRobot AI Platform Single-Tenant SaaS on Microsoft Azure

DataRobot Blog

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

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Digital Analytics + Marketing Career Advice: Your Now, Next, Long Plan

Occam's Razor

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. Intro to Machine Learning.

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