Crave for new content every day - Programmed by Constant Usage
End up consuming more unwanted content and less quality content
What do we think/feel is right?
Slim body
Acne free skin
Attention seeking machines
Results of Social media
Depression / Anxiety
Deep Dive Engagement
Addiction
Loss of Personality
Manipulative / Biased / Personalized targeted ads
Exploit human tendencies of feedback / Sensitized to bias
Top 10 Happiest Countries <> Top 10 Countries spending time in Social Media
Social Media can be an addiction, It can make you feel lonely. We are not monitored for mental health. Talk more, Walk more. Quit Social Media!!!!
Happiness is not spending time on Social Media or watching others' Lives
mere-exposure effect
The mere-exposure effect is a psychological phenomenon by which people tend to develop a preference for things merely because they are familiar with them. In social psychology, this effect is sometimes called the familiarity principle.
The fact that you liked a song because of mere exposure to it, is something that products all over the world is trying to leverage
Higher the frequency of the usage -> higher the familiarity -> higher the likeness of the product -> higher the ‘share of mind’ of the user -> higher the ‘share of wallet’!
Experiments and lessons learned from implementation
Good estimations come with experience and clarity
Clarity comes from awareness and connecting different learning
The ability to learn a new topic and find the relative comparison or connecting dots is key
Being agile is not about pressure and deadlines, It's more about clarity, prioritizing over priorities on what to learn, focus and continuous experimentation, reuse your experience to build basics right.
Sometimes you need titles to influence/evangelize a decision Selling bottom-up has lot more challenges, You have to convince every level Ideals will get transformed based on the buyer's mindset In many cases, startup is the best way if you truly believe in your ideas Learning needs time, Expertise needs a lot of experiments, Selling needs MVPs and Influencing needs collaboration and connections Balancing code vs ideas vs prototypes vs unblocking needs different mindsets and multiple learning goals Every Level can influence up to +2 levels and beyond that, it's really tough :)
Crave for new content every day - Programmed by Constant Usage
End up consuming more unwanted content and less quality content
Reality
We have limited time
Picking the right items to focus
Deep Dive Engagement
Iterations of Learning
Focus, Build your perspectives
Think Again
Employed vs Employable both are different
Expertise vs Familiarity both are different
Awareness vs Opinion is different
When you recognize you will know you need to spend time on collecting more experience/experiments not on watching things. Be aware/alert and change your habits/focus/priorities. Mass thinking machines or Mass content engaged zombies, Whar are we?
Eliminate duplicate dedication by centroid tracking. The movement of the center point of the image.
Less than the midpoint, After the midpoint
Sample every second to avoid duplicate
The crux is the field of view and how you track / vertical or horizontal line for the need / How we remove duplicates crossing the line, tracking that region to avoid duplicates
If tracking is enabled, Centroid > midpoint track
Centroid < midpoint - new item arrived, Enable Tracking
Support Function- Working for Nestle supporting 100+ Enterprise Apps, Upgrades, Weekend updates
Test Function- Working on Windows98 Customer reported issues, Testing, Service packs
Development - Migrating SQL 2000 to 2008 Xbox Supply Chain platform, Migrating new engine of 220 million serial numbers
Performance - Testing and validating Holiday jobs/loads and ensuring the system up for the holiday season
Data Science- Solving Vision, Data, Text building from several iterations
Data - Creating Cloud / BI / Migrating Inprem to cloud variations
We forget, remember, know some issues, know production lessons. A career is a sum of heterogeneous experiences. Try different roles, and build your own perspective. Everything will sum up and be more empathetic from the customer's point of view.
Extract at least one feature from the images, and recognize the object based on a predetermined model being applied to the extracted feature from the images.
Overhead cameras follow customers' movements as they move around the store—without using face recognition - Maybe Phone Signals
Cameras and smart shelf sensors track when and which products are picked up or put back. - Weight sensors
Combining these two inputs allows Zippin to place the right items in the right shoppers' virtual carts. - Phone + Shelf Activity
On leaving the store, customers receive a receipt detailing their charges. Watch this video to see Zippin in action.
My Feedback - It is a combination of Tech, It could be weight sensors + RFID + Vision. One key takeaway is to look at each tech as a complementary tech. I usually see only Vision-based / RFID-based. True value comes not by replacing one with another but by bringing and leveraging the best out of all tech.
How the model gets updated between multiple clients
Similar to data synchronization need to investigate on infra needs to run
TensorFlow Federated (TFF) is an open-source framework for experimenting with machine learning and other computations on decentralized data. TFF runtimes to become available for the major device platforms
Ideal problems for federated learning have the following properties:
Training on real-world data from mobile devices provides a distinct advantage over training on proxy data
This data is privacy sensitive or large in size (compared to the size of the model)
Federated optimization has several key properties
Massively distributed, Limited communication
There are two primary ways we can add computation:
1) increased parallelism, where we use more clients working independently between each communication round; and,
2) increased computation on each client, where rather than performing a simple computation like a gradient calculation, each client performs a more complex calculation between each communication round
Tensor Processing Units (TPUs) are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads
Differential Privacy - Adding Noise to Ensure Privacy
Secure Aggregation - The server can only see bulk updates
Privacy is paramount in Federated learning
IID - Independently Identifiable Data
Privacy and Fairness are in the opposite direction
CPU vs GPU vs TPU
CPU - Small models with small, effective batch sizes
GPU - Models with a significant number of custom TensorFlow/PyTorch/JAX operations that must run at least partially on CPUs. Medium-to-large models with larger effective batch sizes
TPU - Models that train for weeks or months. Large models with large effective batch sizes
How do I find the most impactful use cases and have quick wins? Some guidelines / potential questions to give you the perspective.
I have participated in Kaggle and achieved a good ranking. I have a good understanding of Data Science, Let's build solutions.If all you have is a hammer, everything looks like a nail. Let's see beyond Kaggle what things we need to understand.
Impact #1 - What are the current challenges / business problems ? Identifying impactful / Potential Ideas ?
Solution - Collaborate work with your business to understand, and get their vision, and priorities. Your use case has to be aligned with business needs / current challenges they are solving. A measurable ROI will always help to prioritize and deploy it to production.
ML Applicability #2 - Is this a Data Science use case, Does this need to change/introduce a new process, introduce new touchpoints, or is it a data or data science problem or Insights
Solution - Apply your domain lens, Data science lens, and take a transparent decision. Don't over-engineer for sake of it. If it makes sense do it.
Data Availability and Readiness #3 - If the first two parts are true, you spot problems, you see the feasibility of data science, evaluate what minimum you can build with the available data
Solution - Work with your Data/BI team, partner to build the required data for your MVP solution. The gap between reality vs expectations, What more data do you need to add more, integrate into the system you will get the clarity in this step.
You need to potentially collaborate with the business, product, and data team effectively to spot a successful opportunity. A lot of collaboration, and teamwork to spot the best use cases. Apply these questions and spot your opportunities.
Kaggle and other learning platforms work on the aspect of Feature Engineering, Model building, Beyond Kaggle this is the reality you need to look to apply Data Science in practice. Data science is #Teamwork. You need multiple lenses and participants to work to build impactful use cases.
Feel free to add other questions/guidelines as well.
If all my ideas have worked I would have climbed a few steps more. For the things it worked, Why it works? Why does it take so long to sell an idea? Does collaboration really work in action vs wishes of leadership expectations? To understand I wanted to look back at some paths in my history.
Sometimes when we look back we know why certain initiatives took so long, Why it works the way it works. This goes back to my Microsoft days XBOX supply chain Team. I was working with Program Management, Product Management, Support, and QA Team. The Product Management Team works closely with the Business Team.
Every Team has its own priorities.
Product management - Support Release Xbox, Xbox360 new type consoles with required code changes
Business - Setup new plants, identify new vendors, reduce Red Light Repair issues, Increase Warranty
Support Team - Reduce the number of customer issues
IT Team - Support all the priorities for business / qa / features etc
One issue of the customer writing an email about the warranty in the system is not correct. After a few executive-level escalations. It boils down, We spot there are issues with the way we store. To sort out the issue we provide a free warranty.
When I moved to Amazon, the Initial few days I was listening to another Team debating a similar workflow of repair order flow. I was thinking that everywhere edge cases are the big discussion items.
As we go along keep fixing, and adding new features. This system was one good enough system that tracks cradle to death of Xbox. Manufacturing, shipment, repair, fulfillment, sales, warranty, refurbishment, scrap. Everything was there about the console.
Every time when I hear these in JDs / Ecom Supply chain / Repair functional roles. I feel long back we had done all this as an in-house product.
The core of it is warranty tied to a customer is a much better approach than a warranty mapped to a console. The order of transactions when they are out of sequence is created out of sequence status and we could keep track of update sequence vs delayed delivery of transactions.
I was able to work with my mentor Roji to build a prototype. It took so long release over release to recommend it to put in production. It had data migration, and a core engine but overall the changes were worth it. After a year when there were no priority projects, An initiative like that picked up this item. At least I didn't see any other announcement of a free warranty extension :) post-implementation.
I was wondering why did it take so long, Idea is necessary, we know dirty records in the system due to the sequence of transactions. There is effort and impact. The priorities across each function, business vs selling / pushing new ideas across functions take time.
What other options could have made it work?
If each function thinks about its own priorities it is not effective collaboration.
Sometimes when we solve a problem we need to think beyond fixing the current fire.
Everything is driven by a cost center. Fix now, Move on let's check later. The perspective of how the next customer should not go through the same issue needs a collective lens, not a function-based
Innovation in a way is looking beyond daily functionsand seeing what matters more from a customer perspective
Keeping every function busy, planning back to back new items vs slow and steady everything has its own tradeoff/results
Sometimes even pushing idea visibility with title / connecting with leadership helps
If every team thinks from a customer point of view there would be more consumer-focused collaborative efforts than thinking based on individual function priorities.
The true essence of collaboration needs closer coordination, understanding, and planning across functions with customers as the epicenter of focus.
Welcome Visitor,
I have 20 years of experience (Coder - Emprical Learner - Teacher). I am currently working on Data Analytics (Video-Image-Text-Data) / Database / BI space. I dabble with "Data". Ping me or send a request to connect if what I do appeals to you and you want to talk about it (Data Science / Databases / Deep Learning / Architecture / Design Discussions / Consulting Projects/ Machine Learning Training's/ Strategic Leadership Roles).
Personal Goal - Reach / Teach up to 10 Million Students through various mediums (Catalyst between Academics and Industry)
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6+ years in AI, AI experience working on Image, Video, Text, Numbers - Data
15+ years in Databases
10+ in developing, deploying, monitoring large scale solutions in Supply Chain, Retail
Its my personal blog. The objective of this blog is to bookmark/share my learning's. Posts reflect my opinions, perspectives and interests. Blog post presented are my personal views and do not represent my employer's view. I have acknowledged all posts with References/Bookmarks.
For questions/feedback/career opportunities/training / consulting assignments/mentoring - please drop a note to sivaram2k10(at)gmail(dot)com
Coach / Code / Innovate
