Datasciencewithsam

📣 My first Coursera guided project “TensorFlow for Beginners: Basic Binary Image Classification” is available for public access now.

It was a unique experience working with world’s leading online education platform provider to design and develop course contents on the Rhyme platform that give learners an interactive hands-on experience on the topic.

This course is designed for newbies who want to take their first step into the world of computer vision and By taking this project, learners will gather basic knowledge about building neural network model using popular Python library TensorFlow that can be used for image classification purpose.

I dedicated this project to my pet dog 🐕 whose images have been used as a sample to test the efficacy of the model that learner will develop during the project.

If this sounds interesting to you, then go ahead and sign up for this project via the link below, which will help you to learn basic neural network model building skills in less than 2 hours.

TensorFlow for Beginners: Basic Binary Image Classification Complete this Guided Project in under 2 hours. The goal of this project is to introduce beginners to the basic concepts of machine learning using ...

Have you ever heard about the technical term "Observability"?

Listen to my latest podcast episode that shed some lights on this new technical concept by addressing the following relevant topics:

✅ What Observability means in the context of modern software development and system monitoring? How does Observability differ from traditional monitoring and logging practices?
✅ Why has Observability gained so much attention in recent years? What are the driving factors behind its increasing popularity?
✅ From cybersecurity perspective, what role does Observability play in identifying and mitigating potential threats or vulnerabilities?
✅ How do you see the future of Observability evolving in the next few years from the context of recent AI innovation surge?
✅ What resources or learning opportunities would you recommend for individuals or teams interested in diving deeper into the topic of Observability?

📍 Podcast links:
Apple 👉🏼 https://podcasts.apple.com/us/podcast/data-science-with-sam/id1587954336?i=1000623550107

Amazon Music 👉🏼 https://music.amazon.com/podcasts/ff82ee49-c8af-49b3-83aa-c3e156017eed/episodes/b9e0d176-ef8b-42d8-a345-4010e09b490b/data-science-with-sam-what-is-observability?ref=dm_sh_QQNxitoPsayJ8nyrsjCwBvIHG

DISCLAIMER: The views and opinions expressed in this podcast are solely those of the host(s) or guest(s) and do not necessarily reflect the policy or position of any organization. The podcast is intended to provide general educational information and entertainment purposes only.

This article is worth reading as it attempts to uncover the strategies that companies can consider to tap into the potential of 🤖 by utilizing proprietary data.

In my opinion, these are the advantages of transforming the theoretical concepts into more actionable items for organizations👇

☑️ Leveraging company’s proprietary information would enable data scientists/AI engineers to bring further innovative ideas to drive business decisions that aligned with organizational long-term objectives.

☑️ Developing a homegrown LLM model or generative AI app 💻 instead of using an external pre-existing model (such as GPT-4, BERT etc.) would reduce the risk of data security breach or infiltration attack by hackers 🛡️

☑️ Building a knowledge-based platform 📚 that would assist internal employees to gather information about organization’s intellectual capital or institutional knowledge based on their job functionalities in more convenient way.

⚠️Caveats:

Building a LLM model from scratch is expensive as it would require significant amount of investments to build framework and hire a team of SMEs consist of data scientists and NLP/AI experts.

Therefore, it is necessary to prepare a blueprint of ROI metrics from budgetary perspective before kicking off the generative AI endeavor for your respective organizations.

📍Important Note 📝

In the midst of rapid technological growth, organizations may aim for bringing generative AI to enhance processes that accelerate business decisions WITHOUT eliminating the existence of human-based job roles that require human intellect.

Check out this article and share it with your business and technology leaders to get their perspectives on this. Happy reading!

How to Train Generative AI Using Your Company’s Data Leveraging a company’s proprietary knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and kn...