Remove Data Collection Remove Deep Learning Remove Metrics
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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

The Edge-to-Cloud architectures are responding to the growth of IoT sensors and devices everywhere, whose deployments are boosted by 5G capabilities that are now helping to significantly reduce data-to-action latency. 7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all.

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What you need to know about product management for AI

O'Reilly on Data

That foundation means that you have already shifted the culture and data infrastructure of your company. If you’re just learning to walk, there are ways to speed up your progress. Without large amounts of good raw and labeled training data, solving most AI problems is not possible. If you can’t walk, you’re unlikely to run.

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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Libraries used for NLP are: NLTK, gensim, SpaCy , glove, and Scikit-Learn. Every library has its own purpose and benefits.

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Responsible AI Relies on Data Literacy

DataRobot

The flow of data through an organization: Mapping how data flows through an organization helps organizations get and stay aligned on potential bias risks with data collection and data degradation. rule-based AI , machine learning , deep learning , etc.)

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How to accelerate your data monetization strategy with data products and AI

IBM Big Data Hub

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 103
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Product Management for AI

Domino Data Lab

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

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Of Muffins and Machine Learning Models

Cloudera

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.