At HIVERY, we combine Design Thinking, Learn Start-Up thinking with Machine Learning techniques to develop and release "new to the world" solutions that are intuitive yet power by applying deep science to help solve complex business problems. HIVERY applies artificial intelligence to complex business problems. We do this through our methodology of DISCOVERY, EXPERIMENT and DEPLOYMENT.

1. Imagine you wish to predict the quality of any bananas at your will? With Machine Learning this is
possible. The first step is to acquire a large sample of bananas, assess their characteristics, and use them
to create a large dataset. From this dataset, you determine which features (eg. colour, size, weight,
shape, area grown etc) are the most important ones for predicting the quality of each banana. This
process, called Feature Engineering, provides a set of input variables. Secondly, you may decide to that
the measures of quality you are wish to predict are sweetness, softness, and storage life. These are called
output variables. The task of the machine learning algorithm is to predict the output variables, based on
the input variables.
To develop the machine learning model, we split the dataset into two groups: a Learning set (around 90%
containing both input and output variables) and a smaller Validation dataset (around 10% also containing
both input and output variables).
Using the larger Learning dataset only, we start to “train” the machine learning algorithm by feeding it
both the input and output variables. The algorithm uses internal rules (or parameters) to predict the
output based on the input, and adjusts them each time it makes a mistake (predicts the wrong output
value). This allows the algorithm to start to experience the data and learn how the input variables impact
the output variables. It begins to create its own framework of how it views bananas. This framework
models the link between a typical banana's physical characteristics (input), and its quality (output).
After training, we must test the models accuracy. To do this, we use the remaining Validation dataset and
hide the answers (output) from the algorithm. This way we can assess the algorithm’s accuracy on data in
which we know the answers, but the algorithm does not. Hence, we ask the model to predict the output
and compare its answers (output) to the true ones.
What's more, the algorithm’s prediction accuracy improves as more data becomes available; it continues
to modify itself and gets better. The machine learns!
Case study on Machine Learning. Lets talk Bananas…
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2. STEPWHAT
Dataset
CCA/CCSP
promotional
effectiveness
“historical” dataset
is received
Data is split into
“Training set”
(90%) &
“Validation set”
(10%)
Learnt Model
The models
“parameters” or
demand signals
get adjusted so it
progressively gets
better at
predicting.
We also "Feature
engineering” the
model to help it
understand the
most important
“features” of
“promotional
effectiveness” data
it needs to learn.
Training set
Using the
training data
set, we show
the model the
‘answers’ within
the data so it
learns
E.g. When we
ran a promotion
Y, during time
Z, the result
was X
Validation set
We now test the
model using
validation set but
hide the “answers”
by asking the
model to predict
the “answers”. We
compare model’s
predictions with the
hidden answers to
determine
accuracy
E.g. If we ran a
promotion Y,
during time Z, what
will be the result?
Idea Model
Once the
model is
predicting
with high
degree of
accuracy,
we are
ready live
‘market’
data
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Machine Learning , a subset of Artificial Intelligence, is the science that involves developing self-learning algorithms. The "learning" part of machine
learning is an algorithm that optimizes predictive accuracy through “training” and “validation”
Step by step flow of machine learning
Complete dataset Complete dataset
Split into two dataset to
train model
High degree predicting
model

3. DeploymentExperiment
STEP
MVP
Once the
experiment has
validated ROI,
we proceed to
develop a MVP
tool (i.e. Idea
Model + UX
interface) to
allow end-users
to interact with
the model.
Experiment
We apply our freshly-
developed model to
the real-world data
and assess the
results/business
impact. We continue
to refine the
parameters.
Product
The the
product, often
called “Beta
Product”
because it’s
the first
version is
constantly
refined and
improved
based on user
and business
(i.e. security)
feedback and
needs
WHAT
Dataset
Source dataset
Data is split into
“Training set”
(90%) &
“Validation set”
(10%)
Learnt Model
The models
“parameters” or
demand signals
get adjusted so it
progressively gets
better at
predicting.
We also "Feature
engineering” the
model to help it
understand the
most important
“features” of
“emailing” data it
needs to learn.
Training set
Using the
training data
set, we show
the model the
‘answers’ within
the data so it
learns
E.g. This is
spam email
looks like, this is
not spam email
Validation set
We now test the
model using
validation set but
hide the “answers”
by asking the
model to predict
the “answers”. We
compare model’s
predictions with the
hidden answers to
determine
accuracy
E.g. Is this email
spam or not?
Idea Model
Once the
model is
predicting
with high
degree of
accuracy,
we are
ready live
‘market’
data
HIVERY
Using HIVERY’s Discovery, Experiment and Deployment methodology, a product development cycle is added once the model has been validated
(step 5), where we experiment (test the model) and build an MVP that will allows business users ongoing use of the model in simple yet powerful
interface
From Machine Learning to custom Product Solutions
Discovery
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