In this insightful video, Viktor Vedmich, Senior Developer Advocate at AWS, delves into a suite of AWS services meticulously crafted for developers. He provides an in-depth exploration of Amazon Q, a generative AI-powered assistant designed to streamline development workflows. Viktor also introduces Amazon Q for Developers, an extension that offers advanced coding assistance, and Amazon Bedrock, a fully managed service that simplifies the creation and scaling of generative AI applications. This comprehensive guide is essential for developers aiming to leverage AWS's cutting-edge AI tools to enhance productivity and innovation.

NAVIGATION

0:00 Start

0:17 Generative AI

1:13 What kinds of AI-related services

does AWS provide for developers?

3:46 What is Amazon Q?

8:29 How can we start working with

Amazon Q Developer?

9:46 More about

Amazon Q Developer

and IDE integration?

11:28 How can you convince your boss

that Amazon Q is safe enough

to use on a project?

14:00 Final words

AI IN AWS

🔸 Amazon Bedrock: https://aws.amazon.com/bedrock

🔸 Amazon Q: https://aws.amazon.com/q/

🔸 Amazon Q for Developers: https://aws.amazon.com/q/developer/

COURSES BY ENGX

👉 Integrate GitHub Copilot and ChatGPT into your daily work for streamlined, efficient development. with course "AI for software Engineers on EngX": https://aw.club/global/en/courses/ai-supported-software-engineering

👉 Integrate a generative AI toolset into your product development processes to boost team performance with course "AI adoption for teams": https://aw.club/global/en/courses/generative-ai-adoption-for-engineering-teams

👉 Mini AI Workshop for Dev Teams: https://aw.club/global/en/courses/engx-ai-adoption-workshop

WHERE TO WATCH US AND LISTEN

🔸 YouTube: https://youtu.be/0kXQHpaVLFM

🔸 Apple Podcasts: https://bit.ly/awclub-en-apple

🔸 Spotify: https://bit.ly/awclub-en-spotify

🔸 Download mp3: https://anywhereclub.simplecast.com/episodes/32

#awclub #engx #ai

In this episode, Aleksas Drozdovskis, an Experience Consultant, and Roman Zatitsky, a Product Manager, dive into the idea of "product thinking" from an engineer’s point of view. They chat about how product thinking helps with career growth, influences business decisions, reduces team stress and burnout, and makes engineers more aligned and effective.

🔹Aleksas Drozdovskis: https://www.linkedin.com/in/drozdovskis/

🔹Roman Zatitskiy: https://www.linkedin.com/in/zatitskiyrb/

NAVIGATION

00:00 Introduction of Aleksas Drozdovskis

00:58 What is product mindset

04:24 Why should i care about discovery and business value

10:03 Who gets more promotion?

11:36 How product mindset can decrease the tension between engineers teams and business teams

14:27 Story about project to product transformation

17:09 How to cultivate a product-oriented culture in a team

20:53 How product mindset influence on the impact that engineers have

24:22 Engineers build features based on discovery that don't get used

28:57 Sum up

31:09 Final

COURSES FROM EPISODE

👉 Generative AI for managers: https://aw.club/global/en/courses/gen-ai-for-managers

👉 AI adoption workshop for engineering teams: https://aw.club/global/en/courses/engx-ai-adoption-workshop

👉 EngX HI: Navigating Team Dynamics: https://aw.club/global/en/courses/navigating-teams-dynamics

WHERE TO WATCH US AND LISTEN

🔸 YouTube: https://youtu.be/R9hKz5pJEwk

🔸 Apple Podcasts: https://bit.ly/awclub-en-apple

🔸 Spotify: https://bit.ly/awclub-en-spotify

🔸 Download mp3: 'simplecast episode link placeholder'

#awclub #engx #productthinking

👉 Course: "AI-supported software engineering": https://epa.ms/ai-swe

👉 Course: "AI-Supported Testing": https://epa.ms/ai-aqa

👉 Course: "Prompt Engineering Foundations": https://epa.ms/ai-pef

👉 Join the EngX: https://epa.ms/engx-club

How AI is Revolutionizing Cancer Research and Treatment? In this episode of the ZuriAI podcast, we explore how AI is transforming the prediction, diagnosis, and treatment of cancer. Artem Shmatko, a doctoral student and ML researcher at the German Cancer Research Center, explains AI’s growing influence in oncology.

We discuss:

What cancer is and why it’s challenging to prevent and diagnose

How AI is impacting drug discovery

Medical data aspects and limitations

The role of computer vision in histopathology

Challenges related to data management and patient privacy

NAVIGATION

0:00 Start

0:43 EngX

1:20 Meet our expert, Artem Shmatko

1:49 What is cancer? Complexity and diversity of cancers.

6:52 Why is it so difficult? Challenges of preventing cancer

10:17 How AI is becoming a pivotal tool in cancer prevention and treatment strategies.

12:40 MRI as a prevention technique.

14:38 The role of AI as a decision-support system in clinical settings.

22:01 AI in drug discovery. Transforming cancer treatment pipelines.

26:42 AI-powered recommendation systems for defining focus groups in clinical trials.

28:14 The complexities of data ingestion and collection in cancer research.

30:39 Handling patient-sensitive data: The ethical and technical challenges.

34:13 Certification and regulation in AI-driven oncology.

36:24 How computer vision and histopathology diagnostics are being redefined by AI.

38:27 The importance of image size and resolution in cancer diagnostics.

41:44 Discussing the potential of 100k*100k pixel resolution in medical imaging.

43:05 The technical limitations in cancer detection using computer vision.

46:03 Multiple instance learning

49:27 Data challenges in AI-driven oncology.

53:35 The role of open-source platforms in advancing medical AI.

57:26 Forecasting the impact of AI on the future of cancer prevention and diagnostics.

1:00:35 What industry challenges can AI overcome in oncology?

LINKS FROM THE EPISODE

🔹 AI's Evolution in Music/ ZuriAI #1: https://www.youtube.com/watch?v=KRJV-1Y61SM

🔹 What is cancer: https://www.cancer.gov/about-cancer/understanding/what-is-cancer

🔹 Classification of brain tumours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328013/

🔹 AI based solutions for determination of cell origin: https://www.nature.com/articles/s41586-021-03512-4

🔹 On diagnosing cancer using blood samples: https://doi.org/10.1093/nar/gkz942

🔹 Content-Based Image Retrieval (CBIR) for histological images: https://doi.org/10.1016/j.media.2020.101757

🔹 Automating drug discovery: https://doi.org/10.1038/nrd.2017.232

🔹AI for computational histopathology: https://www.nature.com/articles/s43018-022-00436-4

🔹 Pretrained feature extractors for Multiple Instance Learning (MIL) pipelines: https://www.nature.com/articles/s41591-024-02857-3

🔹 https://www.nature.com/articles/s41586-024-07441-w

🔹 Predicting DNA mutations from histological images: https://www.nature.com/articles/s43018-020-0085-8

WHERE TO WATCH US AND LISTEN

🔸 YouTube: https://youtu.be/XreqENCkpqE

🔸 Google Podcasts: https://bit.ly/awclub-en-google

🔸 Apple Podcasts: https://bit.ly/awclub-en-apple

🔸 Spotify: https://bit.ly/awclub-en-spotify

🔸 Download mp3: https://anywhereclub.simplecast.com/episodes/30

#awclub #engx #ai #oncology