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![words = []
for word in title.split():
words.append(stripNonAlphaNumeric(
word.strip()
))
val words = title
.trim
.split(" ")
.map(w => w.trim)
.map(w => stripNonAlphaNumeric(w))
.map(w => stem(w))
Training
Inference](https://image.slidesharecdn.com/aiinproductionslides-2017-10-180131213332/85/AI-in-Production-37-320.jpg)
AI in Production
- 1. AI in Production Crunch,Budapest October 20, 2017
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- 6. I am afool
- 7. We’re here totalk about reality.
- 8. Reality Bytes Everything Else Models Whatyou see What you get
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- 17. Is this itema fanny pack? Is this item relevant to the query “fanny pack”? On a scale of 1-10 how “fanny pack” is this thing? Which of these is the most “fanny pack”? Crowd Sourcing
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- 20. Crowd Sourcing So...umm...Is thisdata any good?
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- 22. Search results for“fanny pack”
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- 27. Transfer Learning Does itwork for our data?
- 28. Transfer Learning When itworks 1. Embeddings 2. Super General Tasks
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- 33. Serving 1. RPC a. ServiceWrapper? b. Monitoring/Deployment? c. New Models? d. Expertise?
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- 35. Serving 1. RPC 2. RewriteSearch Engine Model
- 36. Modern ML applicationsare comprised of an increasingly diverse mix of libraries and systems...Even if each of these libraries is optimized in isolation, real pipelines combine multiple libraries, so production use at scale usually requires a software engineering team to rewrite the whole application in low-level code”
- 37. words = [] forword in title.split(): words.append(stripNonAlphaNumeric( word.strip() )) val words = title .trim .split(" ") .map(w => w.trim) .map(w => stripNonAlphaNumeric(w)) .map(w => stem(w)) Training Inference
- 38. Serving 1. RPC 2. Rewrite 3.Cache Search Engine Cache Model
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- 41. Top 3 ReasonsAI Products Fail Product
- 42. Product Top 3 ReasonsAI Products Fail 1. Measuring stuff is hard.
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- 45. Day 1 Top Resultfor “shorts” Control Experiment
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- 47. Top Result for“shorts” Day 1 Day 20 ExperimentControl
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- 49. 1. Think aboutyour objective. 2. Isolate. 3. Change one thing: the model. 4. Check your instrumentation. Measurement is hard
- 50. Product Top 3 ReasonsAI Products Fail 1. Measuring stuff is hard. 2. Wrong Venue/Treatment/Approach.
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- 52. Choose an itemyou like
- 53. Items matching yourtaste
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- 58. Product Top 3 ReasonsAI Products Fail 1. Measuring stuff is hard. 2. Wrong Venue/Treatment/Approach. 3. Models First. Users Last.
- 59. Models First Cool Model:word2vec! bluenavy
- 60. Models First Cool Model→ Query Expansion! (navy or blue) totenavy tote
- 61. Search Results for“indian sea”
- 62. Precision Problems Garbage results RecallProblems Not enough results
- 63. f you wantedto make this one statement as well. Or another one. Search results for “wedding dress”
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- 66. Search results for“jewellery”
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- 68. Everything Else Models What yousee What you get
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- 71. Denoument Things you shouldcare about 1. Humans 2. Measurement 3. Training Data 4. Serving Inferences 5. Models
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