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Introduction
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Starting an AI project
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Workflow of projects
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Selecting AI projects
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Organizing data and team for the projects
Workflow of a machine learning project
How do you build, say a speech recognition engine
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Key Steps:
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Collect Data: people saying “Alexa”, and other words
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Train model: learns A to B mapping… audio clip to “word”
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many iterations
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Deploy the model: implement in to a smart speaker
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Will collect new data (get data back), to maintain /update the model
How do you build, say a self driving car
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Key steps:
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Collect Data: images – > positions of other cars, draw rectangles around cars
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Train model: need to iterate and precisely identify cars
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Deploy model: may learn that golf carts are identified and positions well. keep iterating.
Workflow of a data science project
output: actionable insights
Optimize a sales funnel
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Key steps:
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Collect Data: where are people coming from, time of day, machines type, etsc…
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Analyze the data: Iterate many time to get good insights insights from the data collected.
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Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.
Optimizing a manufacturing line
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Key steps:
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Collect Data: clay supplier, mixing time, ingredients, lead times, relative humidity, temperature, kiln duration, etc…
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Analyze the data: Iterate many time to get good insights insights from the data collected.
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Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.
Every job function needs to learn how to use data
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Use data to optimize workflows through data science based analysis, and to take on tasks with machine learning (remember less than a second), Inputs (A) to Output (B).
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From Sales, recruiting, marketing, to agriculture, and beyond DS and ML are having huge impacts
How to choose an AI project
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Bring together a cross-functional team knowledgeable in AI, plus domain experts.
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Brainstorming framework:
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Think about automating “tasks,” vs automating “jobs.”
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what are the main drivers of business values?
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What are the main pain point in your business?
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Note: you can make progress without big data
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Having more data almost never hurts
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Data makes some business [Google, Facebook, Netflix, Amazon] defensible.
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With small datasets, you can still make progress. The amount of data you need is problem dependant.
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Due diligence on project
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What AI can do + Valuable for your business
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Technical diligence
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Can AI system meet desired performance (e.g. accuracy, speed, etc)
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How much data is need to meet performance goals
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Engineering timline
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Business diligence
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Current business: Lower costs
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Current business: Increase revenue ( getting more people to check out)
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New business: New product or business
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*Ethical diligence*
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money vs impact on society
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Build vs. buy
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ML projects can be in-house or outsourced
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DS projects are more commonly in-house
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Some things will be industry standard, avoid building those.
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“Don’t sprint in front of a train.”
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some times it makes sense to adopt another’s platform or approach than to build your own. resource constraints, capability constraints…
Working with an AI team
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Specify your acceptance criteria
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Goal: defects with 95% accuracy…How do you measure accuracy
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Test Set (n1000): labelled training dataset to measure performance.
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Training Set: Pictures with labels
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Learn mapping from A to B
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Test Set: Another data set to test the mappings. Often more than 1 test set will be requested.
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Pitfall of expecting 100% accuracy. Discuss with AI engineers what’s reasonable.
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Limitations of ML
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Insufficient data
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Mislabeled data
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Ambiguous labels
Technical Tools for AI teams
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CPU vs. GPU [Great for deep Learning/Neural Networks] Nvidia
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Cloud vs. On-prem, ….Edge [Processor, where data is collected.]
