The new software engineer how ML will shape the future of your company

The haufe group

No presentation without code…

Why is ai so different?

μ

A year ago

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Haufe as a Data Driven Company

Foundations vs. what you see

Seven things every executive should knowabout Machine Learning

1. Iterate fast and simple

2. Centralize or decentralize ml teams?

3. Consider the biggest roadblocks for data scientists and engineers. No data, no learning.

4. The separation between data

science and devopsis over

5. Keep the right data scientist to engineer ratio

6. Have clear kpi’s

7. Build vs. buy

3 pillars

Ai foundation

Data lake Up-skilling experiments

3 pillars

Ai foundation

Data lake Up-skilling experiments

the #1 thing your teams will complain is lack of data or the quality

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Democratizing data

3 pillars

Ai foundation

Data lake Up-skilling experiments

Upskill ML Engineers

Upskill ML Engineers

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

M L F R A M E W O R K S &

I N F R A S T R U C T U R E

A I S E R V I C E S

R E K O G N I T I O N

I M A G E

P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N

V I D E O

Vis i on Speech Language Chatbo ts

A M A Z O N

S A G E M A K E R

B U I L D T R A I N

F O R E C A S T

Forecast ing

T E X T R A C T P E R S O N A L I Z E

Recommendat i ons

D E P L O Y

Pre-bu i l t a l gor i thms & no tebooks

Data l abe l ing ( G R O U N D T R U T H )

One-c l ick mode l t ra i n ing & tun ing

Opt imi za t ion ( N E O )

One-c l ick dep loyment & hos t i ng

M L S E R V I C E S

F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e

E C 2 P 3

& P 3 N

E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C

I N F E R E N C E

Rein fo rcement l earn ingAlgor i thms & mode l s ( A W S M A R K E T P L A C E

F O R M A C H I N E L E A R N I N G )

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

M L F R A M E W O R K S &

I N F R A S T R U C T U R E

A I S E R V I C E S

R E K O G N I T I O N

I M A G E

P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N

V I D E O

Vis i on Speech Language Chatbo ts

A M A Z O N

S A G E M A K E R

B U I L D T R A I N

F O R E C A S T

Forecast ing

T E X T R A C T P E R S O N A L I Z E

Recommendat i ons

D E P L O Y

Pre-bu i l t a l gor i thms & no tebooks

Data l abe l ing ( G R O U N D T R U T H )

One-c l ick mode l t ra i n ing & tun ing

Opt imi za t ion ( N E O )

One-c l ick dep loyment & hos t i ng

M L S E R V I C E S

F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e

E C 2 P 3

& P 3 N

E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C

I N F E R E N C E

Rein fo rcement l earn ingAlgor i thms & mode l s ( A W S M A R K E T P L A C E

F O R M A C H I N E L E A R N I N G )

Upskill ML Engineers

3 pillars

Ai foundation

Data lake Up-skilling experiments

Enable experimentation

Enable experimentation

Enable experimentation

Enable experimentation

Enable experimentation

Where are We today?

Takeaways

The engineer gets disrupted!

“In the future, all the code you ever write will be business logic.”

Werner Vogels

Thank you.

The Amazon machine learning stack

“Starting at the bottom”

μ

Semantics analytics based recommender

“Starting in the middle”Organizational analytics – semantics based clustering

“Starting at the top”NLP Based search for all haufe content

Takeaways