Thank You and Looking Ahead to 2025!

To all our customers, clients, and friends—thank you for your tremendous support throughout 2024.

As we step into 2025, I’d like to share my wishlist for GenAI Product Development and Responsible AI Adoption:

Promoting Positive AI Impact:

• Ensuring fair compensation for creators whose content is used in training AI models.
• Leveraging AI to bridge gaps in education and healthcare access.
• Accelerating infrastructure development through AI-driven innovations.
• Enhancing employability by creating AI-driven solutions to upskill individuals.
• Fostering the next generation of knowledge workers and problem solvers.

Establishing Regular Fair Use and Audit Policies:

• Implementing focused regulations for monitoring and auditing GenAI adoption to ensure long-term societal benefits.
• Prioritizing ethical considerations and purposeful implementations over profit motives, with clear timelines.
• Establish strict controls to prevent unethical usage and hold model creators accountable for adverse AI impacts.
• Addressing concerns around AI companionship and mitigating risks of digital addiction.

A Personal Milestone

2024, we reached an exciting milestone—50 students enrolled in my course! It was such a proud moment to receive an appraisal from my ex-boss about the course.

A heartfelt thank you to everyone who enrolled and provided valuable feedback. My network is my greatest support, and I deeply appreciate all the conversations with customers and potential clients.

Since its launch, students have collectively viewed 4,175.33 minutes of lecture content—an incredible achievement that motivates me to keep improving.

Wishing Everyone a Successful, Safe, and Happy 2025!

Here’s to a year of success, safety, productivity, and joy!

Multimodal data platform

ETL and data pipelines are redefined in #GenAI Applications. Your #ETL now will support 

• #images, #docs, #numbers, #pdfs. Extracting and storing insights/vectors / structured databases 
• Everything together creates the new #GenAI #Multimodal data platform. 
• #Multimodal #insights from all forms of data
• #Proplens is working to align this perspective for our customers for richer insights and perspectives. #productlessons #genai #data

Keep Loading and Learning!!!

My 2023 Lessons from three lens

The "Been there, Done that" Opinion

• GenAI Adoption is Real
• AI-Assisted Design is here to stay but the balance of autonomy and assistance is evolving
• Text is promising, Image is Evolving
• SpeedyPrototyping is here to Supercharge your innovation 

Industry Lens

• Domain-specific LLMs and use case adoption in Fintech, Healthcare - BloombergGPT, ClimateBERT, KAI-GPT, ChatLAW, FinGPT, BioMedLM 
• Upcoming new opportunities with MultimodalKnowledge - Images + Text + Data for personalization

Functional Domain Lens

• Retail Seems adopting Fast from field work
• RAG is going to replace lot of FAQ, Support Systems
• Healthcare is showing promising ambitions to adopt AI

Keep Thinking!!

AI Influencer

To be an AI influencer it needs a lot of learning

• Pick Ideas / Relevant Research for industry solutions
• Find opportunities across domains
• Keep demo-ready solutions
• Mentor / Have a ton of ideas
• Never stop reading / coding/experimenting

Keep Exploring!!!

Research Paper Reads - DS Development Challenges

Paper #1 - Automating Data Science: Prospects and Challenges

Key Notes

• Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization
• The breadth and complexity of these and many other data science scenarios means that the modern data scientist requires broad knowledge and experience across a multitude of topics

• In classical goal-oriented projects, the process often consists of activities in the following order: Data Exploration, Data Engineering, Model Building and Exploitation.

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Key Notes

The stages addressed in terms of Machine Learning Model Development

• A Model requirements stage which is related to the agreement between stakeholders and the way the model should work.
• Data processing stage which involves data collection, cleaning and labelling (in case of supervised learning).
• Feature engineering stage which involves the modification of the selected data.
• Model training stage which is related to the way the selected model is trained and tuned on the (labeled) data.
• Model evaluation stage which regards to the measurements used in order to evaluate the model.
• Model deployment stage which includes deploying, monitoring and maintaining the model.

Data Science Methodologies: Current Challenges and Future Approaches

Key Notes

• Leveraging data science within a business organizational context involves additional challenges beyond the analytical ones. 

Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment

Key Notes

Keep Thinking!!!