The United Nations has designated 2025 as the International Year of Quantum Science and Technology (IYQ), marking 100 years since the birth of quantum mechanics. This global initiative aims to raise awareness about quantum science's profound impact on society and inspire the next generation of quantum pioneers.

The Significance of Quantum Science

Quantum science, dating back a century, has fundamentally transformed our understanding of the physical world. Its applications span various fields, including computing, cryptography, and materials science, leading to groundbreaking technological advancements. As we look to the future, quantum science holds the potential to address critical global challenges, such as climate change, energy sustainability, and healthcare.

Global Collaboration and Endorsements

The journey towards this international recognition began with the collaboration of numerous national scientific societies and unions. Spearheaded by Mexico, the initiative gained momentum with endorsements from UNESCO and support from nearly 60 countries. In June 2024, the UN General Assembly officially proclaimed 2025 as the International Year of Quantum Science and Technology, following a proposal from Ghana.

What to Expect in 2025

Throughout 2025, a series of global initiatives and events will take place, aiming to engage diverse audiences and enhance public understanding of quantum science and technology. Educational programs, public lectures, workshops, and exhibitions will be organized to showcase the role of quantum science in driving innovation and solving real-world problems.

Get Involved

Everyone is encouraged to participate in this momentous year. Whether you are an educator, student, researcher, or simply a curious individual, there are numerous ways to get involved. You can organize events, create educational resources, or join existing initiatives to help spread the knowledge and excitement of quantum science.

For more information on how to participate in and support the International Year of Quantum Science and Technology, visit the official IYQ website.

Looking Forward

As we all celebrate this milestone, it’s important to recognize that the journey of quantum science is just beginning. By fostering a deeper understanding and appreciation of quantum science, we can inspire the next generation of scientists and innovators to explore the quantum realm and harness its potential for the betterment of society.

Join in celebrating the International Year of Quantum Science and Technology and be part of the quantum revolution!

CRED, a FinTech business run by Kunal Shah, will be simplified in this blog. Because of its marketing methods, design, user interface, experience, Kunal’s tweets, and absurd business model, CRED has gotten everyone’s attention (some believe it will never make money). Below are the different sections we will be covering in this article:
I. Introduction
II. The Traditional Credit Sector in India
III. The Rise of Cred
IV. Cred’s Impact on the Credit Sector in India
V. Limitations and Challenges for Cred
VI. Conclusion

Introduction

Cred is a fintech company based in India that provides a platform for managing credit cards. Its services include a mobile app that allows users to manage multiple credit cards, pay bills, earn rewards, and access exclusive offers from partner brands. Cred has gained popularity in India for its user-friendly interface, and rewards program that incentivizes responsible credit behavior and focuses on high-credit-score users. In addition to credit card management, Cred has expanded into other financial services such as personal loans and insurance. The company has been successful in disrupting the traditional model of credit card usage in India, and its rise to popularity has had a significant impact on the credit sector in the country.

The Traditional Credit Sector in India

Before the emergence of fintech companies like Cred, the credit sector in India was dominated by traditional banks and credit card issuers. The credit card industry in India had a reputation for being complex and challenging to navigate, with many different card types and reward programs that made it difficult for users to manage multiple cards effectively. There was a lack of transparency around rewards and benefits, and many users found it challenging to understand the fees and charges associated with their credit cards.

Below are a few of the several challenges faced by credit card users in India, including:

1. Complexity: Credit card usage in India can be complex and challenging to navigate, with many different types of credit cards available, each with its own set of benefits and fees. Managing multiple cards can be time-consuming and confusing, making it difficult for users to optimize their credit card usage.
2. Lack of Transparency: Many credit card issuers in India do not provide clear and transparent information about their fees and charges, making it difficult for users to understand the costs associated with their credit card usage. This can lead to unexpected charges and fees, which can be a significant burden on credit card users.
3. High-interest rates: Credit card interest rates in India are often high, making credit card usage less appealing for many Indians, particularly those who are concerned about accumulating high levels of debt.
4. Limited Acceptance: Credit cards are not accepted by all merchants in India, which can limit the utility of credit cards for users who are looking to use them for everyday purchases.
5. Security Concerns: Credit card fraud is a concern for many users in India, and there have been several high-profile cases of credit card fraud in the country in recent years. This has made users more cautious about using their credit cards, particularly for online purchases.

Traditional banks and credit card issuers have long dominated the credit sector in India. These institutions typically have large customer bases and extensive networks of branches and ATMs, making it easy for users to access credit and manage their finances. However, the dominance of these institutions has also contributed to some of the challenges faced by credit card users in India.

One of the primary issues with traditional banks and credit card issuers in India is their focus on high-credit score users. These institutions typically offer credit cards and other financial products only to users who have a good credit history and a high credit score. This has created a significant barrier to entry for many Indians who do not have a strong credit history or who are new to credit.

Traditional banks and credit card issuers in India have also been slow to adopt new technologies and business models that could make credit management more accessible and user-friendly. Many of these institutions still rely on paper-based processes and require users to visit branches or call customer service representatives to manage their credit cards. This can be time-consuming and frustrating for users, particularly those who are accustomed to the convenience of digital services.

The Rise of Cred

Cred was founded in 2018 by Kunal Shah, a well-known entrepreneur in India, with the goal of revolutionizing the credit card industry in the country. The company quickly gained popularity for its innovative approach to credit management, user-friendly mobile app, and rewards program that incentivizes responsible credit behavior.

One of the key features of Cred’s mobile app is its ability to aggregate and manage multiple credit cards from different issuers in one place. This makes it easy for users to track their spending, pay their bills, and earn rewards across multiple cards. The app also provides users with detailed information about fees and charges associated with their credit cards, making it easier to understand the true cost of credit card usage.
They also expanded into other financial services such as personal loans and insurance. This has allowed the company to provide users with a more comprehensive suite of financial products and services, further increasing its popularity among Indian consumers.

Its marketing and user acquisition strategies have been key factors in the company’s rapid growth and success. Some of the main strategies used by Cred include:

1. Targeted Advertising: It focused on targeted advertising to reach potential users who are likely to be interested in its services. The company has used social media and online advertising to target users who are interested in financial products, credit card rewards programs, and other related topics.
2. Referral Program: Its referral program has been a significant driver of user acquisition for the company. The program incentivizes users to invite their friends and family to join Cred by offering rewards for successful referrals. This has helped the company to rapidly expand its user base and has been a key factor in its success.
3. Rewards Program: This program has also been a significant marketing tool for the company. The program offers users exclusive rewards for responsible credit behavior, which has helped to incentivize users to sign up for Cred and use its services.
4. Partnerships: Cred has formed partnerships with a range of brands and companies in India, including popular e-commerce sites, travel companies, and lifestyle brands. These partnerships have allowed Cred to offer exclusive rewards and discounts to its users, further incentivizing them to use the app.
5. User Experience: Cred has focused on creating a user-friendly and intuitive experience for its users. This has helped to differentiate the company from traditional banks and credit card issuers in India, which often have complex and confusing interfaces. Cred’s focus on user experience has helped to create a loyal user base and has been a significant factor in its success.

Cred’s Impact on the Credit Sector in India

It has disrupted the traditional model of credit card usage in India in several ways. Here are some of the key pieces of evidence to support this:

1. Increased transparency: Cred’s mobile app provides users with detailed information about their credit card bills, fees, and charges, making it easier for users to understand the true cost of credit card usage. This increased transparency is a significant departure from the traditional model of credit card usage in India, where hidden fees and charges are common.
2. Incentivizing responsible credit behavior: Cred’s rewards program incentivizes responsible credit behavior, such as paying bills on time and maintaining a good credit score. This approach is a significant departure from traditional credit card issuers in India, which often focus on encouraging users to spend more without regard for responsible credit behavior.
3. Aggregating multiple credit cards: Cred’s app allows users to manage multiple credit cards from different issuers in one place. This is a significant departure from the traditional model of credit card usage in India, where users often have to manage each credit card separately, leading to confusion and missed payments.
4. User-friendly interface: Cred’s app is highly user-friendly and intuitive, making it easy for users to manage their credit cards and rewards program. This is a significant departure from the traditional model of credit card usage in India, which often involves complex and confusing interfaces that can be difficult for users to navigate.
5. Introduction of new financial products: In addition to its credit card management services, Cred has introduced new financial products such as personal loans and insurance. This has disrupted the traditional model of credit card usage in India by providing users with a more comprehensive suite of financial products and services.

User reviews and feedback on Cred’s services have generally been positive, with many users praising the app for its ease of use, rewards program, and customer support. Here are some examples of user reviews and feedback on Cred’s services:

1. Ease of use: Many users have praised Cred’s app for its user-friendly interface and ease of use. Users have noted that the app is easy to navigate and makes it simple to manage multiple credit cards in one place.
2. Rewards program: Cred’s rewards program has been a major draw for users, with many praising the program for its exclusive offers and incentives for responsible credit behavior. Users have noted that the rewards program has helped them to save money and be more responsible with their credit card usage.
3. Customer support: Cred’s customer support team has also received positive reviews, with many users noting that the team is responsive and helpful in resolving issues and answering questions.
4. Security: Cred’s app has also been praised for its security features, including two-factor authentication and data encryption. Users have noted that they feel safe using the app and managing their credit cards through Cred.
5. Personal loans and other services: Some users have also praised Cred for its introduction of new financial products and services, such as personal loans and insurance. Users have noted that these additional services make Cred a more comprehensive financial management tool.

Limitations and Challenges for Cred

While Cred has had a significant impact on the credit sector in India, there are still several limitations and challenges that the company faces. Some of the key limitations and challenges for Cred include:

1. Limited User Base: While Cred has seen rapid growth since its launch, its user base is still relatively small compared to other credit card issuers in India. This limits the impact that Cred can have on the credit sector and its ability to negotiate favorable terms with credit card issuers.
2. Dependence on Credit Card Issuers: Cred’s business model is dependent on credit card issuers, which provide the company with revenue through interchange fees and interest charges. This dependence on credit card issuers could limit Cred’s ability to negotiate favorable terms or disrupt the credit sector in significant ways.
3. Limited Product Offerings: While Cred has introduced new financial products and services such as personal loans and insurance, its product offerings are still relatively limited compared to traditional banks and financial institutions in India. This could limit Cred’s ability to compete with these institutions and expand its user base.
4. High Cash Burn: Cred has been spending significant amounts on advertising and customer acquisition, leading to high cash burn. This could limit the company’s ability to sustain its operations and could require additional funding to continue growing.
5. Limited Geographic Reach: Cred’s services are currently only available in India, which limits its potential user base and impact on the global credit sector.

As a fintech company operating in the financial services sector, Cred is subject to a range of regulatory concerns and compliance issues in India. Some of the key areas of regulatory concern and compliance issues for Cred include:

1. Data Privacy and Security: Cred collects and stores sensitive financial information about its users, which raises concerns about data privacy and security. The company must comply with relevant data protection laws in India, such as the Personal Data Protection Bill, to ensure that user data is kept secure and protected from unauthorized access or misuse.
2. Anti-money Laundering (AML) and Counter-Terrorism Financing (CTF): Cred is required to comply with India’s AML and CTF laws and regulations to prevent the use of its services for illicit activities, such as money laundering or terrorism financing. This requires the company to have adequate customer due diligence processes and risk management systems in place to detect and prevent suspicious transactions.
3. Consumer Protection: Cred is required to comply with India’s consumer protection laws, such as the Consumer Protection Act, to ensure that its services are fair and transparent for users. This requires the company to provide clear and accurate information to users about its services, fees, and charges, and to resolve user complaints and disputes in a timely and efficient manner.
4. Payment and Settlement Systems: Cred must comply with India’s payment and settlement systems regulations, such as the Payment and Settlement Systems Act, to ensure that its payment processing systems are secure, reliable, and efficient. This requires the company to have robust internal controls and risk management systems in place to prevent fraud, errors, and other issues.
5. Capital and Liquidity Requirements: Cred is subject to capital and liquidity requirements under India’s financial regulatory framework, which require the company to maintain adequate levels of capital and liquidity to support its operations and manage financial risks.

Compliance with these regulatory concerns and issues is also crucial for Cred to maintain the trust of its users and regulators and to continue operating in the highly-regulated financial services sector in India.

Conclusion

In conclusion, Cred has emerged as a disruptive force in the Indian credit sector, offering users a convenient and rewarding experience for credit card usage. The company has been able to leverage technology and innovative business models to challenge the dominance of traditional banks and credit card issuers and has shown significant growth in a relatively short period of time.
Cred’s success can be seen in its user acquisition strategies, which have resulted in a user base of over 5 million members within just a few years of its launch. The company has also achieved significant brand recognition, with a strong presence in both traditional and digital media. However, while Cred has had a significant impact on the credit sector in India, the company still faces several limitations and challenges. These include its dependence on credit card issuers, limited product offerings, high cash burn, and regulatory concerns and compliance issues.

Despite these challenges, Cred has shown significant potential for growth, with the company recently announcing a $215 million funding round at a valuation of $2.2 billion. The company has also expanded its offerings beyond credit cards to include personal loans and insurance, indicating a willingness to diversify its revenue streams and expand its user base. Cred’s disruptive impact on the credit sector in India is evident, and the company’s ability to continue innovating and growing will be closely watched by both users and industry observers alike.

Questions? Comments? Feel free to leave them in the comment section, also you can follow me on Twitter, or connect with me on LinkedIn.

Root Mean Squared Error (RMSE) and Root Mean Squared Logarithmic Error (RMSLE) are the techniques to find out the difference between the values predicted by your machine learning model and the actual values.

To understand these concepts and their differences, it is important to know what Mean Squared Error (MSE) means. MSE incorporates both the variance and the bias of the predictor. RMSE is calculated as the square root of the mean of the squared differences between the predicted values and the actual values. It is used to evaluate the performance of a regression model. It is a measure of how well the model is able to predict the target variable, and it is sensitive to the scale of the target variable.

Note: Square root of the variance is the standard deviation.

RMSLE is similar to RMSE, but it is calculated using the logarithmic difference between the predicted values and the actual values. It is used to evaluate the performance of a model when the target variable is skewed or has a large range of values. It is less sensitive to the scale of the target variable compared to RMSE. So basically, what changes is the variance that you are measuring? I believe RMSLE is usually used when you don’t want to penalize huge differences in the predicted and the actual values when both predicted and true values are huge numbers.

1. If both predicted and actual values are small: RMSE and RMSLE are the same.
2. If either predicted or the actual value is big: RMSE > RMSLE
3. If both predicted and actual values are big: RMSE > RMSLE (RMSLE becomes almost negligible)

Here is some example code for calculating RMSE and RMSLE in Python:

import numpy as np

def rmse(predictions, targets):
    """Calculate the root mean squared error between predictions and targets"""
    return np.sqrt(np.mean((predictions - targets) ** 2))

def rmsle(predictions, targets):
    """Calculate the root mean squared logarithmic error between predictions and targets"""
    return np.sqrt(np.mean((np.log(predictions + 1) - np.log(targets + 1)) ** 2))

To use these functions, you can pass in the predicted values and the actual values as arguments. For example:

predictions = [10, 20, 30, 40]
targets = [9, 19, 29, 39]

rmse_error = rmse(predictions, targets)
print(f'RMSE: {rmse_error:.4f}')

rmsle_error = rmsle(predictions, targets)
print(f'RMSLE: {rmsle_error:.4f}')

This will output the following:

RMSE: 1.4142
RMSLE: 0.0177

Here is some example code for calculating RMSE and RMSLE in Python, and also visualizing the results, and comparing the performance of two different models:

import numpy as np
import matplotlib.pyplot as plt

def rmse(predictions, targets):
    """Calculate the root mean squared error between predictions and targets"""
    return np.sqrt(np.mean((predictions - targets) ** 2))

def rmsle(predictions, targets):
    """Calculate the root mean squared logarithmic error between predictions and targets"""
    return np.sqrt(np.mean((np.log(predictions + 1) - np.log(targets + 1)) ** 2))

# Generate some example data
predictions_1 = np.random.normal(100, 10, 1000)
targets = np.random.normal(100, 10, 1000)
predictions_2 = np.random.normal(100, 5, 1000)

# Calculate the RMSE and RMSLE for both models
rmse_1 = rmse(predictions_1, targets)
rmse_2 = rmse(predictions_2, targets)
rmsle_1 = rmsle(predictions_1, targets)
rmsle_2 = rmsle(predictions_2, targets)

# Visualize the results
x = np.arange(2)
errors = [rmse_1, rmse_2]
plt.bar(x, errors)
plt.xticks(x, ['Model 1', 'Model 2'])
plt.ylabel('RMSE')
plt.title('RMSE Comparison')
plt.show()

errors = [rmsle_1, rmsle_2]
plt.bar(x, errors)
plt.xticks(x, ['Model 1', 'Model 2'])
plt.ylabel('RMSLE')
plt.title('RMSLE Comparison')
plt.show()

This code will generate two bar plots, one for RMSE and one for RMSLE, comparing the performance of the two models. A lower value on the y-axis indicates a better-performing model.

Another example of regression models:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Generate some fake data for the plot
true = np.random.normal(loc=10, scale=2, size=100)
pred1 = true + np.random.normal(loc=0, scale=1, size=100)
pred2 = true + np.random.normal(loc=0, scale=3, size=100)

# Calculate the RMSE and RMSLE for each prediction
rmse1 = np.sqrt(np.mean((pred1 - true) ** 2))
rmsle1 = np.sqrt(np.mean((np.log(pred1 + 1) - np.log(true + 1)) ** 2))
rmse2 = np.sqrt(np.mean((pred2 - true) ** 2))
rmsle2 = np.sqrt(np.mean((np.log(pred2 + 1) - np.log(true + 1)) ** 2))

# Create a dataframe with the true values and the two predictions
df = pd.DataFrame({'true': true, 'pred1': pred1, 'pred2': pred2})

# Use seaborn to create a scatterplot with regression lines for each prediction
sns.lmplot(x='true', y='pred1', data=df, scatter_kws={'alpha': 0.5})
# Add text labels with the RMSE and RMSLE values for each prediction
plt.text(x=0, y=12, s='RMSE: {:.2f}\nRMSLE: {:.2f}'.format(rmse1, rmsle1), fontsize=12)

# Use seaborn to create a scatterplot with regression lines for each prediction
sns.lmplot(x='true', y='pred2', data=df, scatter_kws={'alpha': 0.5})
# Add text labels with the RMSE and RMSLE values for each prediction
plt.text(x=0, y=10, s='RMSE: {:.2f}\nRMSLE: {:.2f}'.format(rmse2, rmsle2), fontsize=12)

plt.show()

This code generated a scatterplot with two regression lines, one for each prediction. The RMSE and RMSLE values for each prediction are displayed as text labels in the plot.

You can customize the plot further by using the various options available in seaborn and matplotlib, such as changing the colors, markers, and formatting of the plot elements.

If you find this helpful, feel free to share it. You can also drop a comment or ping me on LinkedIn if you have any doubts or questions.

Disclaimer: Views, thoughts, and opinions expressed in the blog belong solely to the author, and not necessarily to the author’s employer, organisation, committee or other group or individual.

If you are on this page, then you belong to any of these categories:

• Scheduled AWS ML Specialty exam and looking for the right resources & strategy to prepare for it
• Finding more about this exam, and design your strategy to prepare for it before scheduling

Doesn’t matter which category you belong to, this blog will guide you and give you some overview of one of the most challenging certification exams.

Recently I cleared my AWS Machine Learning Specialty Exam on the first attempt without having any prior AWS certification (which is not recommended) but had prior experience working on AWS. The exam was quite challenging but you can easily handle it if you have good experience in machine learning, and an understanding of different ML use cases and AWS services.

Let’s start with understanding more about this exam first from its official page.

What is the AWS Machine Learning Specialty exam?

The AWS Certified Machine Learning — Specialty (MLS-C01) exam is a certification exam for professionals who want to demonstrate their expertise in designing and implementing machine learning (ML) solutions on the Amazon Web Services (AWS) platform. The exam tests the candidate’s ability to use ML tools, techniques, and frameworks to build and deploy ML models on AWS. The exam is designed for professionals who have a strong understanding of ML concepts and hands-on experience using AWS services to design and implement ML solutions.

To earn the AWS Certified Machine Learning — Specialty certification, one must pass the MLS-C01 exam. The exam consists of 65 multiple-choice and multiple response questions, and candidates have 180 minutes to complete it. Results for the exam are reported as a scaled score of 100–1000. The minimum passing score is 750. The exam covers a wide range of topics, including:

• Understanding the AWS ML ecosystem
• Preparing and processing data for ML
• Choosing the appropriate ML algorithm and technique
• Training and deploying ML models
• Evaluating and optimizing ML models
• Managing ML workflows

To prepare for the AWS Certified Machine Learning — Specialty exam, one should have a strong foundation in ML concepts and techniques, as well as hands-on experience using AWS services for ML. It is recommended that candidates have at least two years of experience working with ML, including experience designing, training, and deploying ML models on AWS.

Why take the AWS Machine Learning Specialty exam?

There are several reasons why we might choose to take the AWS Certified Machine Learning — Specialty (MLS-C01) exam:

1. To demonstrate expertise in designing and implementing machine learning (ML) solutions on AWS: The AWS Certified Machine Learning — Specialty certification is a recognized credential that demonstrates a professional’s expertise in designing and implementing ML solutions on AWS. This can be valuable for professionals who want to show their skills and knowledge in this area to potential employers or clients.
2. To advance their career: Earning the AWS Certified Machine Learning — Specialty certification can help professionals advance their careers by demonstrating their skills and knowledge in a high-demand area. This can lead to new job opportunities or promotions in organizations that use AWS for ML.
3. To stay up-to-date with the latest ML technologies and best practices: The AWS Certified Machine Learning — Specialty exam covers a wide range of topics related to ML on AWS, including preparing and processing data, choosing the appropriate ML algorithms and techniques, and managing ML workflows. By taking the exam, individuals can stay up-to-date with the latest ML technologies and best practices and improve their skills in this area.
4. To gain a competitive edge: In a competitive job market, earning a certification can give professionals a competitive edge by demonstrating their commitment to their field and their expertise in a specific area. The AWS Certified Machine Learning — Specialty certification is a valuable credential that can help professionals stand out from other candidates and increase their visibility in the job market.

What are the prerequisites for the AWS ML Specialty exam?

There are no official prerequisites for the AWS Certified Machine Learning — Specialty (MLS-C01) exam. However, it is recommended to have at least two years of experience working with machine learning (ML), including experience designing, training, and deploying ML models on AWS.

To prepare for the exam, have a strong foundation in ML concepts and techniques, as well as hands-on experience using AWS services for ML. It is also recommended to have a solid understanding of core AWS services, such as Amazon S3, Amazon EC2, and Amazon EBS, as well as experience using AWS services for data storage, processing, and analysis, such as Amazon Redshift, Amazon Athena, and Amazon EMR.

In addition to hands-on experience, also be familiar with the exam topics covered on the AWS Certified Machine Learning — Specialty exam. The exam covers a wide range of topics, including understanding the AWS ML ecosystem, preparing and processing data for ML, choosing the appropriate ML algorithm and technique, training and deploying ML models, evaluating and optimizing ML models, and managing ML workflows.

Few experts/certified professionals do recommend having certain AWS certifications before attempting this one like AWS Certified Cloud Practitioner/Solutions Architect/Data Analytics Specialty/Security Specialty, but you can skip these and directly prepare for AWS ML Specialty.

How to prepare for AWS Machine Learning Specialty Exam?

To prepare for the AWS Machine Learning Specialty Exam, we should have a strong understanding of the following topics:

1. Machine Learning Concepts and Techniques: This includes understanding the different types of machine learning algorithms, the types of problems they are best suited for, and how to evaluate their performance.
2. AWS Machine Learning Services: We should be familiar with the different machine learning services offered by AWS, including Amazon SageMaker, Amazon EMR, Amazon EC2, Amazon ECS, Amazon EKS, and AWS Deep Learning AMIs, and how to use them to build, train, and deploy machine learning models.
3. Data Preparation and Exploration: We should be able to prepare and explore data for machine learning, including understanding how to clean and prepare data, select features, and perform exploratory data analysis.
4. Model Training and Evaluation: We should be able to train and evaluate machine learning models using different algorithms and techniques, including using hyperparameter tuning and cross-validation to optimize model performance.
5. Deploying and Managing Machine Learning Models: We should be able to deploy machine learning models to production and manage them in a live environment, including monitoring model performance and making updates as needed.

To prepare for the exam:

• Review the AWS Machine Learning Specialty Exam Guide, which outlines the specific topics that will be covered in the exam
• Study the AWS documentation and take online courses or attend training sessions to gain a deeper understanding of the material
• Practice building and deploying machine learning models using the AWS services in a hands-on setting to gain practical experience
• Review the AWS Machine Learning Competency page, which provides a list of recommended resources for preparing for the exam
• Appear for mocks before the actual attempt

List of Resources to Prepare for the AWS ML Specialty Exam

There are several resources available to prepare for the AWS Certified Machine Learning — Specialty (MLS-C01) exam:

1. AWS Machine Learning Competency page: This page provides a list of recommended resources for preparing for the AWS Certified Machine Learning — Specialty exam, including training courses, technical documentation, and white papers.
2. AWS Certified Machine Learning — Specialty Exam Guide: This guide outlines the exam objectives and provides a list of the topics that will be covered on the AWS Certified Machine Learning — Specialty exam.
3. AWS Machine Learning Blog: The AWS Machine Learning blog provides updates and information on the latest ML technologies and best practices on AWS.
4. AWS Machine Learning documentation: The AWS Machine Learning documentation provides detailed information on the various ML services and tools available on the AWS platform, including Amazon SageMaker, Amazon EMR, and Amazon Redshift.
5. AWS Machine Learning sample notebooks: AWS provides a collection of sample notebooks that demonstrate how to use various ML tools and techniques on the AWS platform.
6. AWS Machine Learning webinars and events: AWS regularly hosts webinars and events on ML topics, which can be a valuable resource for candidates preparing for the AWS Certified Machine Learning — Specialty exam.
7. Online ML courses: There are a variety of online courses and training programs available that cover ML concepts and techniques, including courses specifically designed to prepare individuals for the AWS Certified Machine Learning — Specialty exam. I referred AWS Machine Learning — Specialty Certification Preparation learning path on Cloud Academy and practiced by myself for building end-to-end ML pipelines.
   There are other online courses as well which you can refer to, a few of them are listed below:
   - AWS Certified Machine Learning Specialty 2022 — Hands-On
   - AWS Certified Machine Learning — Specialty (MLS-C01)
   - Amazon SageMaker Technical Deep Dive Series
   - AWS Certified Machine Learning — Speciality

I would recommend to must review the AWS Certified Machine Learning — Specialty Exam Guide and the AWS Machine Learning Competency page to get a thorough understanding of the exam objectives and the recommended resources for preparing for the exam. In addition to these resources, I also gained hands-on experience working with ML on the AWS platform to gain a practical understanding of the concepts and techniques which helped in the exam.

That’s all about my experience with the AWS Machine Learning Speciality exam. The only thing you need to do now is pick the right resources, prepare your plan and start learning.

If you find this helpful, feel free to share it. You can also drop a comment or ping me on LinkedIn if you have any doubts or questions.

Happy learning and Best of luck with the exam!

1. Introduction

Financial institutions are testing early use-cases of Quantum Technologies for NP-hard problems which are uncertain or difficult to optimize. In this article, we are going to make use of quantum computers for building an optimal portfolio out of FAANG (Facebook, Apple, Amazon, Netflix, Google) stocks using the mean-variance portfolio optimization technique. Initially, we will talk about the basics of Quantum Computing and Portfolio Optimization. Later on, we will jump to coding- where we will do the initial setup, load data from Eikon API, do some basic analysis, implement mean-variance portfolio technique classically and then use VQE & QAOA.

1.1 Introduction to Quantum Computing

Quantum computing is an area of computing that focuses on developing computer technology based on quantum theory’s concepts (which explains the behavior of energy and material at the atomic and subatomic levels).

Computers used today can only encode information in bits that take the value of 1 or 0, whereas, in Quantum computing, we have quantum bits, called qubits. QuBits can be in the state 0,1 & 0|1 (superposition- existing simultaneously with a certain probability across the spectrum of 0 to 1). This helps us achieve parallel programming and hence solve computations at an exponential speed compared to a normal computer. When qubits are measured, they will either collapse to 0 or 1.

So, in short, a Quantum Computer makes use of quantum mechanical phenomena such as superposition and entanglement to perform computation.

Superposition

A quantum system’s ability to be in numerous states at the same time until it is measured is known as superposition.

To illustrate this Schrodinger posited a thought experiment involving putting a cat and a sealed bottle of poison in a closed box together. The question now is, how would we know if the poison bottle broke open and the cat died or if the cat is still alive inside the box?

Entanglement

Quantum entanglement is a quantum mechanical phenomenon in which the quantum states of two or more objects must be described in relation to each other, even if the individual objects can be spatially separated.

One of the most commonly used qubits is photon spins. A photon can either have spin up (one state) or spin down (zero states). If we have two entangled photons, then they must have opposite spins, if one is up then the other must be down.

In this article, we will explore about Portfolio Optimization problem and see how we can solve them with Qiskit.

1.2 Why Quantum Computing for Finance

Applications of QC in finance are currently being developed and worked on. Two such applications are:

Portfolio Optimization- It is an optimization problem where we have a collection of assets and we want to select these assets that maximize our return but at the same time minimize the risk. These optimization problems can be formulated as quadratic programs which are well studied classically and are very difficult to solve.

Option Pricing- It is an estimation problem and these problems mostly rely on Monte Carlo simulations. If the function that we want to estimate is a difficult function, then classical computers will have a very slow convergence.

With a quantum computer, there are two possible approaches with which we hope to improve optimization algorithms that we can run in the near-term devices that we already have today and fault-tolerant devices in the next couple of years.

Heuristic algorithms like Variational Quantum Eigensolver (Variational algorithm) have a classical and a quantum processor, so it depends on the problem that we want to solve and the models that we have and there’s no speed up that we can prove here right now.

On the other hand, if we look into the future when we get fault-tolerant quantum computers then there are algorithms based on Grover Search like Grover Adaptive Search where we can expect quadratic speed up.

For Monte Carlo Simulations, there’s a quantum algorithm called the Quantum Amplitude Estimation algorithm with which we can also expect a quadratic speed up.

1.3 Qiskit Finance

Let’s have a short overview of what Qiskit Finance is, and what sub-modules it contains

Two applications module available in Qiskit Finance, that we can see in the above diagram:

• Portfolio Optimization
• Option Pricing

It gives us the tools to solve optimization and option pricing problems like the European Call Option or fixed-income pricing.

It also has the sub-modules containing circuits and tools to build these quantum circuits that we need to run these algorithms.

We also have some modules for data providers where we can load random data or we can even load historical stock market data to test our algorithms. Here we have extended the historical data loading module to load data from Eikon API.

1.4 Mean-Variance Portfolio Optimization

Portfolio optimization is a process of selecting the best assets out of the available options in order to maximize the return and minimize the risk. For more info read here.

First, we need to find the best-performing assets and then decide how much need to be invested in each of the selected assets.
Some of the basic terms before moving ahead:
Risk: Deviation of the return on investment from the expected level.
Return: Reward of investment to a given asset.
Portfolio: Collection of assets such as stocks, currencies, bonds, etc.

There are different strategies/frameworks for Portfolio Optimization, however, in this article, we are going to use Mean-Variance Portfolio Theory also known as Modern Portfolio Theory (MPT) which is a very basic one but good to explore use case on quantum computers and it evaluate assets in two dimensions that is risk and return. Using MPT we will find the assets to invest in out of available options, in our case which is FAANG.

Let’s see how we will be solving the mean-variance portfolio optimization problem for nassets:

where we use the following notation:

• 𝑥∈{0,1}ⁿ denotes the vector of binary decision variables, which indicate which assets to pick (𝑥[𝑖]=1) and which not to pick (𝑥[𝑖]=0),
• 𝜇∈ℝⁿ defines the expected returns for the assets,
• Σ∈ℝⁿ*ⁿ specifies the covariances between the assets,
• 𝑞>0 controls the risk appetite of the decision-maker,
• and 𝐵 denotes the budget, i.e. the number of assets to be selected out of 𝑛.

We assume that one has to select exactly 𝐵 assets.

The equality constraint 1ᵀ𝑥=𝐵 is mapped to a penalty term (1ᵀ𝑥−𝐵)² which is scaled by a parameter and subtracted from the objective function. The resulting problem can be mapped to a Hamiltonian whose ground state corresponds to the optimal solution. In this notebook, we will see how to use the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA) to find the optimal solution for a given set of parameters.

Example of Mean-Variance Analysis

It is possible to calculate which investments have the greatest variance and expected return. Assume the following investments are in an investor’s portfolio:

Asset A: Amount = $1000 with the expected return of 5%

Asset B: Amount = $3000 with the expected return of 10%

In a total portfolio value, the weight of each asset is 25% and 75% respectively.

Therefore, the total expected return of the portfolio is the weight of the asset in the portfolio multiplied by the expected return:

Portfolio expected return = (25% * 5%) + (75% * 10%) = 8.75%. Portfolio variance is more complicated to calculate because it is not a simple weighted average of the investments’ variances. The correlation between the two investments is 0.65. The standard deviation, or the square root of the variance, for Investment A, is 7%, and the standard deviation for Investment B is 14%.

In this example,

Portfolio variance = (25% ^ 2 * 7% ^ 2) + (75% ^ 2 * 14% ^ 2) + (2 * 25% * 75% * 7% * 14% * 0.65) = 0.0137

The portfolio standard deviation is the square root of the answer: 11.71%.

Number of Possible Combination of Assets for FAANG

Since we have 5 assets in the list from which we are going to find optimal asset names to be invested, we will be making use of 5 qubits, where each qubit will be mapped with one asset. A 5-qubit system uses combinations of numbers up to five-place values `11111` and each place value will be representing one of the assets.

There are 2⁵ (32) states (possible combinations of assets):
00000, 00001, 00010, 00100, 01000, 10000, 00011, 00110, 01100, 11000, 10001, 10100, 00101, 01010, 01001, 10010, 00111, 01110, 11100, 11001, 10011, 10101, 01011, 01101, 10110, 11010, 01111, 11110, 10111, 11011, 11101, and 11111.

We will be observing the probabilities and optimal values of these states when running them on classical and quantum computers.

Current Limitations

• Quantum Systems with a lesser number of qubit available publicly
• Fault-tolerant Quantum Systems to be built for quadratic speed up
• More efficient ways to load classical data into quantum states and perform fast computations with them
• An effective quantum error rate of the hardware should be very small, in order to ensure that no errors occur in the applications, however, a quantum error correction algorithm can be used to lower this

2. Environment Setup & Package Installation

Assuming you already have anaconda installed or python supported IDE To execute the below codes, we need to install the following modules:

2.1 Qiskit Setup & Installation

Qiskit: An open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules. For more info, visit here.

Qiskit Finance: It contains uncertainty components for stock/securities problems, Ising translators for portfolio optimizations and data providers to source real or random data to finance experiments. For more info, visit here.

Execute the below cells to install Qiskit and its components:

Check the version of Qiskit and its installed components

[Optional] Set up a token to run the experiment on a real device

If you would like to run the experiment on a real device, you need to set up your account first. You can get your API token from here

Note: If you do not store your token yet, use IBMQ.save_account('MY_API_TOKEN') to store it first.

2.2 Eikon API Installation

Eikon API: It allows to access data directly from Eikon or Refinitv Workspace using python giving capabilities to data scientists, quants to prototype or production solutions. For more info, visit here.

Execute the below cells to install Eikon:

Check version of the Eikon API

Setup Eikon API

I have saved my Eikon Data API key in a text file for security reasons and not displaying here in the notebook.

Other packages which need to be installed if not installed already- numpy, pandas, matplotlib, seaborn

Importing Packages

3. Getting Data using Eikon API & Pre-Processing

Defining EikonDataProvider class for Loading Data as needed by Qiskit

We will inherit BaseDataProvider from the data provider module of Qiskit Finance to extend its functionality of getting data from Eikon API in the desired format and make use of existing functions.

Initializing Required Parameters

Getting data from Eikon API using EikonDataProvider class

Statistical Data of the Loaded Data

Closing Price History Chart

Expected Return (Mean) of each Asset

Covariance Matrix

Diagonal elements of the covariance matrix shows the variance of each of the assets whereas off-diagonal elements shows the correlation of one asset with the other assets.

Correlation Matrix

Let’s explore the data by plotting a 5*5 matrix with the correlation of the returns between the five stocks. We can see that the stocks’ returns are strongly correlated. At the same time, we plotted the histogram of the returns in the diagonal of the matrix. We can estimate that the returns are not distributed normally due to the leptokurtic shape of the histogram. At the same time, we can see that the stocks’ returns are strongly correlated.

Here an Operator instance is created for our Hamiltonian. In this case, the Paulis are from an Ising Hamiltonian translated from the portfolio problem.

Utility Functions

4. Classical Implementation using Eigen Solver

Classical Eigensolver

Let's solve the problem. First classically…

We can now use the Operator we built above without regard to the specifics of how it was created. We set the algorithm for the NumPyMinimumEigensolver so we can have a classical reference. The problem is set for ‘Ising’. Backend is not required since this is computed classically and not using quantum computation. The result is returned as a dictionary.

Classical Benchmark

5. VQE Implementation

First, let’s understand what is VQE

The Variational Quantum Eigensolver (VQE) algorithm is one of the essential algorithms to learn in the NISQ era. It makes the idea of the famous Quantum Phase Estimation Algorithm usable with the small number of qubits Quantum Computers currently possess. VQE presents us with a hybrid quantum-classical approach to tackle the current hardware limits.

VQE exploits the fact that the ground state energy is always less than or equal to the expectation value of the Hamiltonian of the state. Hence, by minimizing the expectation value of the Hamiltonian, we can find the upper bound of the ground state energy. And by varying the state, we can find the wave function on which the value of the expectation value is the lowest.

VQE, in a straightforward language, consists of three steps:

• Converting the Hamiltonian into Pauli basis
• Creation of a variational form
• Parameter Optimization

Goal: Find ground state |Ψ⟩ of the Hamiltonian 𝐻.

Idea: Choose model |𝜙(𝜃)⟩ that can approximate |Ψ⟩ well and minimize the energy

For more info, visit here.

We can now use the Variational Quantum Eigensolver (VQE) to solve the problem. We will specify the optimizer and variational form to be used.

VQE Benchmark

6. QAOA Implementation

The Quantum Approximate Optimization Algorithm (QAOA) is a widely-studied method for solving combinatorial optimization problems on NISQ devices. The applications of QAOA are broad and far-reaching, and the performance of the algorithm is of great interest to the quantum computing research community.

We also show here a result using the Quantum Approximate Optimization Algorithm (QAOA). This is another variational algorithm and it uses an internal variational form that is created based on the problem.

For more info, visit here.

QAOA Benchmark

7. Conclusion

Portfolio optimization is just one of the many applications that can be solved using Quantum Computers. As we can observe above, the output from Classical EigenSolver matches with the output of VQE and QAOA implementation. Quantum computers are expected to have a substantial impact on the Finance industry, as they will eventually be able to solve certain problems considerably faster than the best known classical algorithms.

Currently, it is a major challenge to determine when this impact will occur for each application, and, in fact, one of the most pressing challenges is to redesign quantum algorithms in order to both considerably reduce the quantum hardware requirements and at the same time keep their provable impact.

I hope this article gives the reader a good start in the exploration of the wonderful field of research on portfolio construction and optimization. Advances in quantum algorithms together with better quantum hardware will continue to bring these applications closer to reality.

Downloads

You can download the complete code from this github repo.

References

• https://developers.refinitiv.com/en/article-catalog/article/portfolio-optimisation-ii
• https://developers.refinitiv.com/en/api-catalog/eikon/eikon-data-api
• https://qiskit.org/documentation/tutorials/finance/01_portfolio_optimization.html
• https://developers.refinitiv.com/en/api-catalog/eikon/eikon-data-api/quick-start
• https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html
• https://arxiv.org/pdf/2011.06492.pdf

To learn more about the amazing contents Eikon Data API has to offer, visit at this link.

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