Building a Distributed Data Pipeline

1. BUILDING A DISTRIBUTED MACHINE LEARNING AT SCALE

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 data continuously ▸Apply several processing steps COLLECT MODEL DEPLOY INTEGRA TE

4. SOLUTION ANALYSE THE STOCK MARKET YAHOO.C OM YAHOO.C OM (PREFETCHED) COLLECTO R MESSAGE BROKER STREAMIN G STORAGE MODEL MACHINE LEARNING MLlibWEBSERVI CE USER / CLIENTS

5. DEMO DEMO (FINGERS CROSSED)

6. DONE QUESTIONS? ▸?

Building a Distributed Data Pipeline

Building a Distributed Data Pipeline

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Building a Distributed Data Pipeline