Building "Deep Brew": The Minimal Viable Product

Class Learning Outcomes:
Our objective for today's lecture is twofold:
first, to explore the critical role that AI and ML play in transforming marketing strategies of businesses globally,
second, to delve into the practicalities of building AI and ML applications using Python and PyTorch {Application Development aspects} and Google Machine Learning Flow to manage the CI / CD.
Our focus here is not just to understand the concepts, but also to develop hands-on skills necessary for applying these technologies.

Prelude: How we got here

First it was the introduction of the personal computer in the 1980s.
And then word processing, spread sheets, presentation software like PowerPoint, and - biggest business impact of all - databases. Today, Large language learning models ARE the new databases.
IF you were to create a CASE tool: which would let the user design graphically in some UML-like language an LLM:
What functionalities would your Tool have and what would they look like?

Entire industries have sprung up around front ending databases and Excel Sheets with Analytical Dashboards like PowerBI.
In the 1990s, the Internet sprung into the public space and all those Word Documents, PowerPoints, Excel Sheets, and Databases were bolted on to front-end presentation portals called web servers and made available to the world.
What is the key difference between a database and a large language learning model?
With a Database, you must understand the data model and all the tables.
You need to know WHAT you are looking for.
In the 2000s, Social Media, sparked by ubiquitous smart phones and pervasive WIFI, lead businesses to make their business processes centric around social-media enablement of customer engagement with the company's core value generating back end processes.
In the 2010s, this convergence of technology creted the phenomen we call Big Data, which amplified the application of real-time customer demographic analyze to get you want you wanted, faster, better, and for less cost.

Now here we are in 2023, and cognition-boosting cybernetics systems are carrying the load of boring, checklist-based job tasks to free human creativity for interesting, value-generating thinking.
Generative AI language models are what we wanted the Internet to be.The Internet was heralded as the Global Brain, but that promise fizzed out to being the global filing cabinet.
Generative AI Language models like ChatGPT are the emerging global brain.
I. Introduction
Brief on the lecture's objective and relevance
Quick look at Starbucks and its role as a leading global brand
II. Context Setting: The Evolution of Marketing and Role of AI/ML
Traditional Marketing vs. Personalized Marketing
The importance and impact of AI/ML in Personalized Marketing
III. Starbucks and Personalized Marketing
Introduction to Starbucks' Personalized Marketing strategy
Role of Digital in Starbucks' Marketing approach
IV. Python Basics: Introduction and Use in AI/ML
Introduction to Python programming language
Importance of Python in AI/ML
Brief tutorial on Python syntax and functions
V. Building AI/ML Models with Python: PyTorch and Bayesian Model Training
Introduction to PyTorch
How to install and use PyTorch for AI/ML applications
Basics of Bayesian model training
Practical example: building a simple Bayesian model in PyTorch
VI. Role of AI in Starbucks' Personalized Marketing: Deep Dive with Python
Detailed examination of Starbucks' 'Deep Brew' AI initiative
Practical Python session: how to use Python for understanding and predicting customer preferences
Building a basic AI model in PyTorch, simulating Starbucks' real-time personalization
VII. Role of ML in Starbucks' Personalized Marketing: Deep Dive with Python
Detailed examination of Starbucks' use of ML for predicting customer behavior and optimizing marketing messages
Practical Python session: how to use Python and PyTorch to build a basic ML model for personalized marketing
VIII. Building a Minimal Viable Product: Python, AI and ML
Concept and importance of Minimal Viable Product (MVP)
Steps to build an MVP for an AI/ML platform, similar to Starbucks' using Python and PyTorch
IX. Conclusion: Practical Lessons and Future Trends
Key takeaways from Starbucks' approach to AI and ML
Future trends in AI/ML applications for personalized marketing
Role of Python in these future trends
X. Interactive Q&A and Code Review
Open floor for questions and answers
Review of Python codes used in the session
Discussion and clarification of technical concepts

Today, we are standing at the crossroads of technology and commerce, at a juncture where artificial intelligence (AI) and machine learning (ML) are not just buzzwords but game-changers in our modern digital economy.
Here will will practice our AI Application Development Build Skills, were we dive deep into the nexus of these advanced technologies and the world of marketing.

As a case study, we will be examining a brand that needs no introduction: Starbucks. This global giant has made waves, not just for its quality coffee but also for its innovative use of technology. It is a perfect example of a company leveraging AI and ML to redefine its marketing, using personalized strategies to connect with its customers at a deeper level.
Starbucks's journey showcases how businesses can intelligently harness AI and ML to drive growth, profitability, and customer satisfaction.
It highlights the future of marketing, a future where AI and ML are central to understanding consumer behavior and crafting customized consumer experiences.
As we embark on this journey to decode the successful amalgamation of technology and marketing at Starbucks, we will also learn how to code in Python and use PyTorch. We'll see how to build a simplified model similar to Starbucks's AI/ML platform. So, not only will we unravel the strategy behind a global brand's success, but we'll also work towards creating a similar success blueprint.
The purpose of this is to build familiarity, comfort, exposure: When you walk into Day 1 of the Job, you will be used to thinking about things in the way of doing.
Let's get started on this exciting journey, where technology meets coffee, and innovation brews success!

II. Context Setting: The Evolution of Marketing and Role of AI/ML

A. Traditional Marketing vs. Personalized Marketing

Let's embark on our journey by traversing the road from traditional marketing to personalized marketing.
Traditional marketing:
Push only, no feedback loop
Making guesses about our users: who are they, what do they want, where will be advertize the product to connect with them (go to market channels, demand-generation).
We created customer Segments.

Now everybody can be a segment of one.
In the past, marketing was generally a "one-size-fits-all" approach. Advertisements were broad, targeting a mass audience rather than catering to the individual tastes and preferences of consumers.
This practice resulted from a lack of actionable data to understand each customer's unique psychometrics : thinkings, wants profile, coupled with the absence of advanced tools to process such information.
However, as the digital age evolved, so did marketing strategies. With an unprecedented amount of consumer data becoming accessible, companies began realizing the power of personalization.
Personalized marketing, as opposed to the one-size-fits-all methodology, is a strategic approach aiming to deliver individualized content to recipients through data collection, analysis, and the automated delivery of content.
The paradigm shift from traditional to personalized marketing was significant. Personalized marketing proved itself more effective, garnering higher customer engagement, satisfaction, and ultimately leading to increased loyalty and revenue growth.

B. The Importance and Impact of AI/ML in Personalized Marketing

The crux of this evolution from traditional to personalized marketing lies in two revolutionary technologies: Artificial Intelligence (AI) and Machine Learning (ML).
AI and ML have emerged as game-changers, allowing companies to understand, predict, and cater to customer preferences on an unprecedented scale. With AI, companies can analyze vast amounts of data to identify patterns and insights that can guide marketing strategies.
Meanwhile, Machine Learning, a subset of AI, allows systems to learn from data traing sets, identify patterns, and make decisions with minimal human intervention. {Baysian Training Methods.}
In marketing, ML models can be trained to predict customer behavior, segment customers into Demographic Profiles, create personalized content, and optimize marketing campaigns.
To paint a vivid picture, consider this: Every interaction a customer has with a brand, every click they make on a website, every product they view or purchase, every review they leave - all these data points feed into AI and ML models. These models then analyze and learn from this data, creating a comprehensive understanding of each customer's preferences, behaviors, and needs.
This granular understanding of the customer enables brands to deliver highly personalized marketing that resonates with each individual's unique profile.
Personalized product recommendations, targeted promotions, optimized email campaigns - these are just a few examples of how AI/ML can power personalized marketing.
In essence, AI/ML have enabled the shift from a 'segmentation' approach in marketing to a 'segment of one' approach, where each customer is their own segment, receiving highly personalized marketing content. It's this transformative power of AI/ML that we'll explore in this course, delving deeper into the how and the why of it, using Starbucks' application of these technologies as our guiding case study.

III. Starbucks and Personalized Marketing

A. Introduction to Starbucks' Personalized Marketing Strategy

As we embark on exploring Starbucks' approach to personalized marketing, it's important to understand the unique nature of the brand's strategy.
Starbucks, a global leader in the coffee industry, has consistently been at the forefront of employing innovative strategies to engage with its customers. The brand has carved a niche in the realm of personalized marketing, transforming it from being a mere coffee retailer to a tech-savvy, customer-centric organization.
Starbucks' personalized marketing strategy revolves around their loyalty program, the Starbucks Rewards app. Each customer's past purchases, preferences, and behaviors are tracked through this platform, enabling Starbucks to create an individualized customer profile.
Starbucks uses this profile to send personalized offers and product recommendations to each customer, often in real-time. The approach is simple yet effective: understand each customer as an individual, anticipate their needs and preferences, and provide highly relevant and engaging offers. This unique approach not only drives customer loyalty but also encourages higher frequency of purchases.

B. Role of Digital in Starbucks' Marketing Approach

An integral component of Starbucks' marketing strategy is its embracement of digital technology. The Starbucks Rewards app, a central piece of their strategy, is an embodiment of this digital transformation.
The app allows Starbucks to connect directly with its customers, offering a seamless and personalized customer experience. Every interaction a customer has with the app, from orders to payments, feeds valuable data back to Starbucks.
This data-driven, digital approach has several advantages. It allows Starbucks to:
Collect valuable data: Every order, click, and review is a piece of the customer's preference puzzle. Starbucks uses these data points to understand and anticipate customer needs.
Engage in real-time: The app allows Starbucks to interact with customers in real-time. Personalized offers can be sent when a customer is in or near a Starbucks store, increasing their relevance and effectiveness.
Personalize at scale: The digital platform enables Starbucks to offer personalized experiences to millions of customers simultaneously, something that would be nearly impossible with traditional marketing methods.
Boost loyalty: By providing a seamless, engaging, and personalized experience, Starbucks strengthens its relationship with customers, enhancing loyalty and driving repeat business.
In conclusion, Starbucks' personalized marketing strategy is a powerful blend of customer-centricity and digital innovation.
Leveraging AI/ML technologies, Starbucks has managed to understand and cater to each customer's unique preferences, revolutionizing the customer experience in the process.
We will delve deeper into the AI/ML technologies powering this strategy in the following sections, while also exploring how to create such solutions using Python and PyTorch.

IV. Python Basics: Introduction and Use in AI/ML
A. Introduction to Python Programming Language
Python, named after the British comedy group "Monty Python," is a high-level, interpreted programming language that is known for its simplicity and readability. Python was developed by Guido van Rossum and first released in 1991. It is now one of the most popular programming languages, particularly in the field of AI/ML.
Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It has a large standard library that includes areas like web service tools, string operations, Internet, operating system interfaces, and protocols. Most of the Python libraries are cross-platform compatible on UNIX, Windows, and Macintosh.
Python is used in many application domains including web development, automation, scientific computing, data science, AI, and ML.
B. Importance of Python in AI/ML
Python has become a leading language in AI/ML for several reasons:
Simplicity and Consistency: Python’s syntax is simple, making it easy to learn and use. Python code is readable and maintainable.
Extensive Libraries: Python has a rich set of libraries useful for AI/ML, such as TensorFlow, PyTorch, Scikit-learn, Keras, and Pandas.
Community and Support: Python has a large community of developers who contribute to its vast set of libraries and packages. This community also provides excellent support and documentation.
C. Brief Tutorial on Python Syntax and Functions
Let's take a quick tour of Python's syntax and functions.
1. Variables and Types
In Python, variables don't need explicit declaration. The declaration happens automatically when you assign a value to a variable.
pythonCopy codex = 3y = "Hello, World!"print(x)print(y)
Python supports several types like int, float, str, list, tuple, dict, set, bool.
2. Control Structures
Python uses indentation to define control and loop structures. This contributes to its readability.
For instance, here's an example of an if-else control structure:
pythonCopy codex = 5if x > 0: print("Positive number")else: print("Non-positive number")
Here's an example of a for loop:
pythonCopy codefor i in range(5): print(i)
3. Functions
In Python, functions are defined using the def keyword. Here's an example of a function:
pythonCopy codedef greet(name): print(f"Hello, {name}!")
D. Python Lab Exercises
Now that you have an overview of Python basics, let's practice with some lab exercises:
Exercise 1: Write a Python function to find the max of three numbers.
Exercise 2: Write a Python function to sum all the numbers in a list.
Exercise 3: Write a Python function to check whether a number is in a given range.
Exercise 4: Write a Python function that accepts a string and calculates the number of upper case letters and lower case letters.
Exercise 5: Write a Python program to count the occurrences of each word in a given sentence.
Note: I'll provide the solutions and the code explanation in the following response. Let's solve these exercises first.

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