Adrian Gonzalez Sanchez gives a presentation on the realities of managing AI projects. He discusses 2021 highlights in AI/ML, including the democratization of AI adoption, the rise of MLOps to define the AI lifecycle, and progress on responsible AI. He then shares key learnings from managing AI projects, such as the importance of early end-user involvement, executive sponsorship, narrow use cases, and granular planning to mitigate risks. The presentation concludes with a Q&A section.

1. 16 diciembre 2021
Adrián González Sánchez
La realidad de la
gestión de
proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez

2. CC BY 4.0 - Adrian Gonzalez Sanchez
[AI Lead Talk] La realidad de la gestión de proyectos IA
x
www.linkedin.com/in/adriangs86
https://adrian-gonzalezsanchez.medium.com
2
Vuestro amigo y vecino…
Adrián González Sánchez
Telco & IT Engineer, MSc. + MBA
Tech Executive + University Lecturer
Montreal, Canada _

3. CC BY 4.0 - Adrian Gonzalez Sanchez
[AI Lead Talk] La realidad de la gestión de proyectos IA
Nuestro plan hoy - AI Lead Talk
2021 AI/ML Highlights – ¿Dónde estamos?
Key Project Learnings – La realidad del día a día
Q&A
3
1
2
3

4. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
2021 AI/ML Highlights
4
1
I. Democratization of the AI adoption is generating new opportunities for all kinds of AI professionals (tech, biz, and
hybrid profiles). I see more people with "atypical profiles" re/upskilling so they can help with new internal AI initiatives for
small and medium businesses. We are going more granular in terms of AI skills and responsibilities. Cloud-enabled /
aaS platforms are helping a lot.
II. MLOps is the key industry trend, and an organic answer to the need for a well-defined end-to-end AI/ML lifecycle. This is
also contributing to clarify tasks / micro-tasks and responsibilities. I think data and ML engineers will have a lot of fun in
2022 :) their roles are evolving very quickly, same as their importance within the adopter organizations.
III. Responsible AI is finally becoming something tangible. Main concern for AI professionals was how to actually evaluate
responsible AI factors during the analysis, implementation, and prod phases. Big companies are releasing both
commercial and open source solutions to check fairness, explicability, data bias, etc. Still a WIP but I really like what I
see from Microsoft, TensorFlow/Google, etc.
IV. The data scientist skill set is also evolving. Some of the DS/DE tasks are becoming a bit more of a "commodity" (e.g.,
initial naive approaches, model benchmarking), and technical complexity requires more and better soft skills than ever. I
have met wonderful data scientist, but my favourite ones are those who are able to explain choices and results in a very
simple way, so everyone can understand the key pieces. Superstar data scientists will bring that to the companies, and
they will become key decision influencers.
Fuente propia: https://www.linkedin.com/posts/adriangs86_ai2021-2022predictions-ai-activity-6875764835808808961-RjnI

5. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
5
The impact of early
involvement of end-users in
designing AI solutions
1
Key Project Learnings
2

6. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
6
The benefits of AI education for
stakeholders to facilitate
adoption and collaboration
2
Key Project Learnings
2

7. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
7
The importance of
executive-level sponsorship
and their understanding of AI
3
Key Project Learnings
2

8. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
8
4 The incredible power of
mission-oriented projects
for AI implementations
Key Project Learnings
2

9. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
9
The notion of “good enough”
AI model performance to
manage expectations up-front
5
Key Project Learnings
2

10. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
10
The trade-off between fully
automated and HITL
Human-in-the-loop systems
6
Key Project Learnings
2

11. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
11
The necessity of using AI for
specific “narrow” tasks and
leveraging SME knowledge
7
Key Project Learnings
2

12. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
12
The scalability of AI use cases
and their connection with
previous and future projects
8
Key Project Learnings
2

13. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
13
The required effort for granular
AI project planning and risk
mitigation
9
Key Project Learnings
2

14. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
14
The role of AI tooling and
infrastructure to accelerate
implementations
10
Key Project Learnings
2

15. [AI Lead Talk] La realidad de la gestión de proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez
15
Mi regalo de navidad - Lecturas para gestión 360 de IA/ML
+

16. CC BY 4.0 - Adrian Gonzalez Sanchez
[AI Lead Talk] La realidad de la gestión de proyectos IA
16
Q&A

17. 16 diciembre 2021
Adrian Gonzalez Sanchez
La realidad de la
gestión de
proyectos IA
CC BY 4.0 - Adrian Gonzalez Sanchez