Machine Learning & AI Foundations: A Guide to Predictive Modeling

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Machine Learning
AI Foundations A Guide to Predictive Modeling
NetCom Learning
Tom Goodheart NetCom Learning
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Agenda

• Evaluating the Proper Amount of Data
• Assessing the Data Quality and Quantity
• Data Preparation and Modeling Challenges
• Scoring Machine Learning Models
• Deploying Models and adjusting data prep /
  scoring
• Monitor and Maintenance

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Evaluating the Proper Amountof Data

• The Proper Amount of Data
• It Depends, No One Can Really tell You
• Complexity of the Problem
• Complexity of the Algorithms chosen
• Reason by Analogy Learning Curves
• Study how your problem changes as data scales as
  well as your algorithm. Averaging over many
  iterations of size can give you a good enough
  idea
• Domain Expertise
• Delphi Technique
• Statistical Heuristics
• Factor the Number of classes
• Factor the number of input features
• Factor the number of model parameters

• As data sets used in the field of Machine
  Learning grow bigger and more complex we enter
  the realm of big data massive amounts of data,
  flowing from varieties of sources, that grow
  exponentially.
• Information needs to be stored efficiently and
  rapidly in order to provide business value.
• Just because we have data doesnt mean its
  quality data or that it will provide the types
  of answers we need.

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Evaluating the Proper Amountof Data
Learning Curves
These are statistical tools built off of
heuristics and domain expertise. As the model
training examples increase you can see the
scores of the model reach an asymptotic point At
this point no matter how much more data you
throw at your model, performance will not
improve.
http//acl.ldc.upenn.edu/P/P01/P01-1005.pdf
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Evaluating the Proper Amountof Data
Domain Expertise
We reach out to people we know with
more information than us, We Google search, We
continue our growth towards Domain Expertise by
exposing ourselves to experts and information in
the field.
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Evaluating the Proper Amountof Data
Statistical Heuristics The Ten Times Rule A
rule of thumb, along with many others, that can
help set the tone for your exploratory analysis.
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Assessing Quantity and Quality of Data

• The Data Quality Cheat Sheet
• Clarify your objectives and collected data
• Is my question a categorical or numerical
  prediction?
• Have I collected the right data source?
• Take the time to execute proper collection
  methods
• Machine Learning is the best example of Garbage
  In,
• Garbage Out you can compute expensively.
• Maintain a rigorous audit trail
• git blame
• Scoring Engines/Scoring Files Logging
• Proper CI/CD Pipelines
• Designate a Data Quality Team
• Their responsibility is the maintenance, testing,
  and
• assurance of the data.
• Independent Quality Assurance
• Can someone else reproduce your results?
• https//hbr.org/2018/04/if-your-data-is-bad-your-m
  achine-learning-tools-are-useless

• One of the major setbacks in the availability of
  data present today is that you have to slice
  your way through copious amounts of questionable
  data sets.

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Data Preparation and Modeling Challenges
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Data Preparation and Modeling Challenges
To create a model, then, we make choices about
whats important enough to include, simplifying
the world into a toy version that can be easily
understood and from which we can infer important
facts and actions. We expect it to handle only
one job and accept that it will occasionally act
like a clueless machine, one with enormous blind
spots. ? Cathy O'Neil
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Scoring Machine Learning Models
Scoring is widely used in machine learning to
mean the process of generating new values, given
a model and some new input. The generic term
"score" is used, rather than "prediction,"
because the scoring process can generate so many
different types of values A list of recommended
items and a similarity score. Numeric values, for
time series models and regression models. A
probability value, indicating the likelihood that
a new input belongs to some existing category.
The name of a category or cluster to which a new
item is most similar. A predicted class or
outcome, for classification models.
Scoring is also called prediction, and is the
process of generating values based on a trained
machine learning model, given some new input
data.
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Deploying Machine Learning Models
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Monitor and Maintenance
Microsoft AzureML Studio has a built in tool
that allows you to glean insights from your
running models, like how they are scoring over
time, how fast they respond to customer inputs,
etc. Remember If It Isnt Logged It Doesnt
Exist
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Recorded WebinarVideo
To watch the recorded webinar video for live
demos, please access the link http//bit.ly/2GIpZ
Yc
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About NetCom Learning
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