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Building a Distributed Data Pipeline
Distributed machine learning allows companies to collect and process large amounts of data continuously to build models that generate value. The solution involves collecting high-volume data from various sources and applying a multi-step pipeline to process, analyze, and model the data using machine learning algorithms. The models are then deployed via web services so users can integrate the insights into applications to solve problems like analyzing stock market trends.
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Building a Distributed Data Pipeline
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- 2. BACKGROUND DATA ▸Data is everywhere ▸Data,unapplied, is useless ▸How can we turn high volume & velocity data into value?
- 3. BACKGROUND PIPELINE ▸Process the datacontinuously ▸Apply several processing steps COLLECT MODEL DEPLOY INTEGRA TE
- 4. SOLUTION ANALYSE THE STOCKMARKET YAHOO.C OM YAHOO.C OM (PREFETCHED) COLLECTO R MESSAGE BROKER STREAMIN G STORAGE MODEL MACHINE LEARNING MLlibWEBSERVI CE USER / CLIENTS
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