2. THE HEARTS ELECTRICAL FIELD
IS ABOUT 60 TIMES GREATER IN
AMPLITUDE THAN THE
ELECTRICAL ACTIVITY
GENERATED BY THE BRAIN
https://www.researchgate.net/publication/221920256_A_DSP_P
ractical_Application_Working_on_ECG_Signal
5. WHY THIS
TOPIC
A brief explanation of
DS and ML
PROJECT
SELECTION &
IMPL.
A practical approach to
narrow the scope
A CUSTOMER
CHURN
PREDICTION
MODEL DEMO
Findings
MS LEARNING
RESOURCES
Certification Prep.
Takeaways in
a nutshell
My goal with this presentation:
I want you to feel empowered to do a machine learning project
even with your current knowledge level!
6. Be warned!
EDUCATIONAL DATASETS ARE WAY
DIFFERENT FROM REAL-LIFE DATA!
DATA SCIENCE JOBS MAY REQUIRE
A MASTER OR PHD AND/OR +2
YEARS OF EXPERIENCE
DATA ENGINEERING IS THE NEW
HYPE
This is not about
carrier advice!
F O R T H AT, C O N S U LT W I T H YO U R
P R O F E S S I O N A L C A R R I E R C O A C H O R
C O N S U LTA N T.
14. Project
idea
Think like:
- a scientist
- an engineer
- a businessperson
Find an interesting
question
Create a project
analysis plan
15.
16. Project idea Tools
Finding anomalies in the
working equipment at early
stages and alerting
the manufacturing supervisor
to carry out maintenance
activity
Azure Cognitive Services
(ACS)- Anomaly
Detector/Metrics Advisor
-Azure Stream Analytics (ASA)
Resource
DATA SOURCE:
Azure Kinect DK €399
Arduino Kit €60
More advanced kits €1000-
Customer’s own data
17. Time
Can we
achieve
success
within the
given
timeframe?
Balance speed with value
PROJECT IDEA CREATION/SELECTION
CRITERIA
Data
Readiness
Are there
already
available
data?
ML Need/
Applicability
Does it really
need ML?
Chance of
success based
on team skills
Business
Impact
Does it
solve a real
problem
for the
business?
18. Project idea Tools
Optimizing in-store product
assortment to maximize sales
and/or manage inventory
distribution across
warehouses/stores
Azure Cognitive Services
(ACS)- Computer Vision/
Custom Vision / Anomaly
Detector (to detect/predict
the anomalies on indirect
factors)
-Azure Stream Analytics (ASA)
Resource
DATA SOURCE:
Web scraping for outside factors/competitors
Customer’s own data
Business case
from Belgium
19. Project idea Tools
Optimizing pricing/growing
techniques with AI-Based
Yield Prediction and Smart
Irrigation
Azure Cognitive Services
(ACS)- Computer Vision/
Custom Vision / Anomaly
Detector (to detect/predict
the anomalies on indirect
factors)
-Azure Stream Analytics (ASA)
Resource
DATA SOURCE:
Web scraping for outside factors
IoT devices like allMETEO, Smart Elements,
and Pycno
Weather API’s
Business case
example
20. Project idea Tools
Detecting anomalies and
optimizing recommended
treatment/exercise schedule
by means of patient
symptoms, clinical history,
lifestyles and realtime IoT
data
Azure Cognitive Services
(ACS)- Computer Vision/
Custom Vision / Anomaly
Detector (to detect/predict
the anomalies on indirect
factors)
-Azure Stream Analytics (ASA)
Resource
DATA SOURCE:
Mobile devices, smart watch and other wearibles
Clinical records
Azure Kinect DK
Other IoT devices
21. Project idea Tools
Detecting anomalies in the
roads at early stages and
alerting the authorities to
carry out maintenance activity
Azure Cognitive Services
(ACS)- Anomaly
Detector/Metrics Advisor
-Azure Stream Analytics (ASA)
Resource
DATA SOURCE:
vehicle sensors by using the accelerometer
and gyroscope of a smartphone
Normal traffic data (data.gov.be)
Google roads API
23. CROWD4
ROADS
SmartRoadSense
APIs
Google Roads API - No historical data
- A reliable/accurate
anomaly detection is
impossible with
current data
- No road surface
data
data.gov.be
TomTom API
arcGIS(esri) API
Waze API
Openstreetmap API
Solution:
To make a mobile APP to get data via our
own API & data storage.
* Detecting anomalies in the roads at early stages and alerting the authorities to carry out maintenance activity
25. NEW Project idea 😊 Tools
- Predicting lifetime value and
risk of churn for individual
customers
(telecommunication, banking,
energy etc),
- Advising focused customer
retention programs according
to the predicted behaviour.
- Azure Cognitive Services
(ACS)- Personalizer)
- Python, Jupyter NB on
Azure Compute Instance
and Azure ML Studio
- PowerBI, SAC
Resource
DATA SOURCE:
- Publicly available tested/reliable datasets
26. Customer Retention Model Lifecycle Blueprint
Prepare
Data
Register and
Manage Model
Train &
Test Model
…
Build model Deploy Service
Monitor Model
Prepare Experiment Deploy Presentation
Data Manipulation:
- Clean the data
- Outliers
- Missing val.
- Preprocess the data
Find the best fit model:
- Compare models
- Calculate test scores
- Model optimization
Deploy the model:
- Run with customer data
- Apply new data
- Azure Container Instance
Present the outcome:
- PowerBI
-SAP Analytics Cloud
D1 D10
31. Azure free account
http://www.azure.microsoft.com/Account/Free/
Get started with 12 months of free services!
With your Azure free account, you get all of this—and you won’t be charged until you
choose to upgrade.
What can I do with my free account?
Here are just a few ideas of all you can do with Azure
32. MS Azure Training and Certifications
Role-based
Technical skills required
to perform a job
Fundamentals
Foundational understanding
of technology
Associate
Expert
Azure Administrator
Azure Developer
Azure Security Engineer Azure Data Engineer
Azure AI Engineer
Azure Data Scientist
Azure DevOps Engineer
Azure Fundamentals (AZ-900)
Azure Solution Architect
Apps & Infra Data & AI
Main reference for all learning paths : https://learn.microsoft.com.
35. Fundamentals
Training & certification
-for everyone
Role based
Training & certification
-for a targeted sub-set
Specialty
Workload training
-based on project need
Approach: Build skills in phases
What’s in a skilling plan
Events
Free local events to learn Azure Fundamentals
Instructor led training
In person & virtual classes from MSFT Trainers &
Learning Partners
Certifications
Azure certifications to validate knowledge 1
2
3
Digital skilling
Free on-demand training
Skilling Plan Components
36. Microsoft Azure Training Day: Fundamentals
Event Overview
Free one-day technical training event appropriate for all technical roles who want to get started with Azure
and provides guidance on getting started and insight directly from the experts.
Get skilled and certified for free at Microsoft Azure Training Day: Fundamentals events
Free Azure Fundamentals training
Free Azure Fundamentals certification exam code to individuals that attend training
Take the exam following the training – either on-site or at a testing center*
Topics
General cloud computing concepts, models, and services such
Public, Private, and Hybrid cloud; IaaS, PaaS, and SaaS; security,
privacy, compliance, and trust.
This course will prepare you to take the AZ-900 exam and
received the Azure Fundamentals certification.
Targeted Roles
Developer Architect
*proctored on-site exam testing rooms not available at all events
I am so excited to be here. Before jumping into the main topic, I would like to mention some fun facts as an icebreaker.
The other day, I came across this scientific statement on the screen that says: THE HEARTS ELECTRICAL FIELD IS ABOUT 60 TIMES GREATER IN AMPLITUDE THAN THE ELECTRICAL ACTIVITY GENERATED BY THE BRAIN.
Upon this insight, I enlightened about the source of Hack Your Future’s big, asymmetrical positive impact not only on Belgium education market and job market but also on our lives…
Hack Your Future community’s core values are really amazing:
-Respect for diversity -use of positive language,
Continuous encouragement and empowerment, mostly by a team of volunteer mentor and teachers
All those values are theoretically stated on most organisations value declarations, but to see them in action requires a big heart with good intentions which is something rare.
So I am proud of being a community member of HYF and being among you.
Here, I would like to give some historical information to clarifiy why you are sitting there as a listener and I am here as a speaker!
Everything started with the announcement of Lien over the Global Diversity Call For Paper Day.
I answered this invitation positively, then Nick came also under the thread and said that, due to covid-19, this year format is a bit different, and no speech will be delivered there
And both Nick and Lien suggested instead deliver a speech on a Sunday on this platform.
That is why I am here on the stage… This was the story behind my first technical talk.
I am a software developer at Cronos group.
Nowadays I am on a reallocation traject from a front-end development position to data-science position.
For further information, you can check my linkedin profile and any connection request is welcome…
With this slide, I would like to express my goal with this presentation:
I want you to feel empowered to do a machine learning project even with your current knowledge level!
Yes, if you know how to interact with API’s, which is for sure, I know your curriculum, then you are more than ready!
I will talk about how to selecet a machine learning project idea,
Which will be followed by a project demo,
And lastly, I will try to cover some Microsoft Learning resources.
Disclaimer: This talk is not about carrier advice!
I won’t say don’t try it at home, but please be aware that
EDUCATIONAL DATASETS ARE WAY DIFFERENT FROM REAL-LIFE DATA!
DATA SCIENCE JOBS MAY REQUIRE A MASTER OR PHD AND/OR +2 YEARS OF EXPERIENCE
DATA ENGINEERING IS THE NEW HYPE
This is a slide from yesterday, from the 8th International Data Science Summit.
If something grows economically, it is not hard to guess that job market will also adapt itself accordingly.
You don’t have to read all of those stuf.
From this slide, I understand one thing clearly:
We have the data, just we can’t use it yet…
To use this data potential, other than many legal regulations, security regulations, other interoperability aspects;
Companies need data engineers and data scientist, ml engineers etc. to put this data in action…
3 Months ago, there was a great session over Machine Learning by Nick Trogh, Microsoft’s Development Engagement Lead for Belgium and Luxemburg. There Nick covered many concepts, inclusive also the history of AI, evolution of AI, use cases, bias, ethical concerns, and much more… If you couldn’t make it then, I provided the link to that session here .
I strongly encourage you to check it out. I have also added a supplementary presentation covering similar subjects, mainly Azure cognitive services and some business use-cases; but because of the time constraint, I won’t be able to go over it. That requires absolutely another session. But for those who are interested, I put the link for those slides here as well.
I put this slide just to mention that Machine learning is not necessarily required for every case.
Our main focus must be solving the problem. If classical programming manages it with a simple rule-based system, we must go for that.
This slide is also from yesterday, from DS Summit.
Instead of such a full cycle with a full-fledged agile team, I suggested to use a practical approach for project idea creation and selection.
In this approach, we must Think like a scientist (we must use advanced analytics, investigate motivations, find patterns, understand the why, we must use critical thinking, start by researching, hypothesize it, test it, confirm or disprove those hypothesis, and measure the results)
– we must think like an engineer (write code, build systems, deploy models, automate things, reproducible, testable, extensible, portable, robust, reliable, efficient systems)
–we must also think like a businessperson (this is all about making more money, and spending less of it, we must search for real world problems, that needs real world solution)
Considering all those, we must come up with an interesting question.
Which will be solved by us in complience with the Created project analysis plan
Now, let’s see those principles in action.
To narrow the scope, I started with a Mc.Kinsey report showing the AI impact potential across all business domains.
In this heatmap, it is visible that Predictive analytics has a huge impact in many fields.
Please don’t be distracted with the left side. The reason I put it is to show the method I used to narrow the scope. You can only focus on the intersection at the left side
And at the right side, I wrote down the project idea, tools and of course, the data source.
I added a link to the State Of The Art report about a similar use case. According the time and ml applicability, considering our team skills,
I categorized it in three different colors. Red for hard, blue for moderate, and green for doable…
--X--Here I want to mention my team, we were three people, one with data science background, one with data analytics background and I was the only one with a development background. Today I present you mostly the parts that I did myself without team contribution. If there is a team contribution, I will use we instead of I as a pronoun.
(for the patients in rehabilitation, recovery period, or extensive-care patients)
But while further analysing our project idea, we realized that our assumptions about the data availability were competely wrong for this case.
This was a real challenge for my team.
We had to manage the expectations.
We had to find order in the chaos
Here we applied a golden rule: When mentioning a problem, suggest a solution
Define Here is the data scientist’s inner loop of work
There will be a demo on jupyter notebook environment demonstrating how to consume the model
This is our complete portfolio of training and certification for Azure, designed to meet industry and market needs, with roles, skills and capabilities needed for the job, including specializations, hands-on experience and practice requirements.
This is our complete portfolio of training and certification for Azure, designed to meet industry and market needs, with roles, skills and capabilities needed for the job, including specializations, hands-on experience and practice requirements.
This is our complete portfolio of training and certification for Azure, designed to meet industry and market needs, with roles, skills and capabilities needed for the job, including specializations, hands-on experience and practice requirements.
Digital Skilling - Build practical job skills with easily accessible, free self-paced courses
Events - Choose from workshops, conferences, and other events
ILT - Attend in-depth training taught by Microsoft Certified Trainers
Certifications - Validate skills with Microsoft fundamentals, role-based and specialty certifications
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