How can a company start automating repetitive tasks? What are the standard tools of the trade? What kind of processes could be automated with a relatively small investment?
2. Where should we use automation?
- Repetitive Tasks
- Grunt work time/cost > development time/cost
- Task can be described as clear* steps
* the meaning of “clear” is subtle and will require clarification
4. 3 Criteria for automation:
- Is it repetitive enough?
- Does it require enough man-hours?
- Can we describe it as an algorithm?
5. 4 Tools (for different processes)
Show by
Clicking
Show with
Scripting
Show with
Code
Machine
Learning
6. Click & Tell
1. Click on
2. Type “Excel”
3. Click on
4. Paste text from previous task ...
Use a software which reads & executes instructions:
7. PROS
- $ Cheap
- Easy to learn
- Quick-Start
CONS
- Not resilient (image change)
- Somewhat Limited: click, open, close,
save, write, copy, paste, move, etc.
- Does not scale with complexity
Click & Tell
8. Examples:
- Sikuli (or SikuliX)
- Automa
- PyWinAuto
-
- Etc ...
Click & Tell
Ref: Automate the Boring Stuff with Python
Open all documents in folder
X, compare the 3rd item, and
save the result to an excel
sheet. Every end-of-the
month, open a website, take
a screenshot of the stock,
paste to excel and save that
excel sheet in the VP’s
drive and delete all the
documents.
11. PROS
- Uses your current software
- Not difficult learning curve
- Affordable
- Can automate progressively
CONS
- Not resilient (program change)
- Often Incompatible: Program A can’t talk
to program B which uses a different format.
- Not one single project: Too many
moving parts
Script & tell
12. Examples:
- VBA (Excel)
- Windows Automation API
- AutoIT
- Etc ...
Open all documents in folder
X, compare the 3rd item, and
save the result to an excel
sheet. Calculate the rolling
average of the price & the
contribution of each
department. At the end of
the month, open a website,
take a screenshot of the
stock, and create a .doc in
the VP’s drive after
deleting all the documents.
Script & tell
13. Code & Tell
Use a programming language to manipulate your documents
and interact with your software.
15. Code & Tell
PROS
- More versatile & powerful
- All in one platform
- Can automate progressively
- Many building blocks already exist
- Data analysis is easy to add
CONS
- Steeper learning curve
- Investment in staff ? time ?
- Requires some maintenance
16. Code & Tell
Examples:
- Python (PyWinAuto + Pandas + Numpy + … )
- . NET (White + RogueWave, …)
- Java, perl, BASH, …
Pull the raw data, make the
usual statistics, create a
PDF report from it with
graphs. Make backups of all
the documents and copy the
first line of each in an
email that will go to the
SVP of XYZ.
17. Machine Learning
Use software and programming tools inspired by the brain.
For more fuzzy tasks:
- Recognize objects in an image
- Transcribe handwriting / solve Captchas
- Transcribe speech
- Make decisions based on data
- Identify trends or patterns
- Does this scan contain a seal and a signature?
18. Machine Learning (3 main schools*)
Biology &
Physics
inspired
networks
Statistical
learning
(Bayesian)
Learning
by Analogy
(SVM)
19. Machine Learning
PROS
- Great for intuitive tasks (image,
patterns, trends, voice)
- Many ready-to-use tools
- Can run parallel (or on top) of
other tasks.
- Mostly free & Open source
CONS
- Longer/Steeper learning curve
- Harder to hire experts
- May need large training data sets
20. Machine Learning
Examples:
- OpenCV (image)
- Pyocr, tesseract, FreeOCR (OCR)
- Theano, Sci-Kit, TensorFlow
Find object in image, check
signature, check stamp,
count bullet points on a
scan, find deep correlations
in data, track point in
video, transcribe
handwriting or voice, etc
