Automation and machine learning in the enterprise
- 1. Automation & Machine Learning Dr. Alvaro Feito Boirac 22 Aug 2016, Bermuda
- 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
- 3. Effortless for Humans ≠ Easy for Machines
- 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.
- 9. Click & Tell Ref: Automa
- 10. Script & tell Combine scripts from: Windows, VBA, your API
- 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