Report of the Summer Internship

ON

AI-ML VIRTUAL INTERNSHIP

Duration: April 2024 –June 2024

BY

Ms. BOGEM VEDA VAMSITHA (2451-21-733-080)

Department of Computer Science and Engineering

M.V.S.R. ENGINEERING COLLEGE
(Affiliated to Osmania University & Recognized by AICTE)
Nadergul, Saroor Nagar Mandal, Hyderabad – 501 510 2023-24

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 M.V.S.R. ENGINEERING COLLEGE
( Affiliated to Osmania University & Recognized by AICTE)
Nadergul, Saroor Nagar Mandal, Hyderabad – 501 510

Department of Computer Science and Engineering

Certificate
This is to certify that the summer internship work entitled AI-ML
Virtual Internship is a bonafide work carried out by Ms. BOGEM
VEDA VAMSITHA (2451-21-733-080) in a partial fulfillment of the
requirements for the award of degree of Bachelor of Engineering in
Computer Science and Engineering from Maturi Venkata Subba Rao
(MVSR) Engineering College, Hyderabad during the academic year 2024-
2025 under our guidance and supervision.
Internal Guide External Guide Head of Department
Dr. Abdul Azeem J. Prasanna Kumar
Assistant Professor Professor & Head
Department of CSE Department of CSE
CERTIFICATE

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3
 DECLARATION

This certification attests that the content documented in the project report titled " AI-ML
Virtual Internship " is an authentic record of the work completed by us during the
internship. The report is a comprehensive representation of the project undertaken solely by
us, and it has not been replicated or borrowed from any external source .

BOGEM VED VAMSITHA

(2451-21-733-080)

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 ACKNOWLEDGEMENT

I would like to express my deepest sense of gratitude to our

esteemed institute Maturi Venkata Subba Rao Engineering College
(Autonomous), which has provided us an opportunity to fulfil our cherished
desire.

It is with a sense of great respect and gratitude that I express my

sincere thanks to Dr. Vemula Sridhar Assistant Professor, Department
of Computer Science and Engineering, Maturi Venkata Subba Rao
Engineering College (A) for his inspiring guidance, supervision, and
encouragement towards the successful completion of my internship.

We are also thankful to our principal Dr. Vijaya Gunturu and Prof. J
Prasanna Kumar, Professor and Head, Department of Computer
Science and Engineering, MVSR Engineering College, Hyderabad for
providing excellent infrastructure and a nice atmosphere for completing
this project successfully as a part of our B.E. Degree (CSE).

We convey our heartfelt thanks to the lab staff for allowing us to use the
required equipment whenever needed.

Finally, we would like to take this opportunity to thank our family

for their support through the work. We sincerely acknowledge and
thank all those who gave directly or indirectly their support in the
completion of this work.

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 BOGEM VEDA VAMSITHA (2451-21-733-080)

VISION
To impart technical education of the highest standards, producing
competent and confident engineers with an ability to use computer
science knowledge to solve societal problems.

MISSION
o To make the learning process exciting, stimulating and interesting.

o To impart adequate fundamental knowledge and soft skills to students.

o To expose students to advanced computer technologies in order to excel

in engineering practices by bringing out the creativity in students.

o To develop economically feasible and socially acceptable software.

PO’s
o PSO1: Demonstrate competence to build effective solutions for
computational real-world problems using software and hardware across
multi-disciplinary domains.

o PSO2: Adapt to current computing trends for meeting the industrial and
societal needs through a holistic professional development leading to
pioneering careers or entrepreneurship.

PEO
The Bachelor’s program in Computer Science and Engineering is aimed
at preparing graduates who will: -

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 o PEO-1: Achieve recognition through demonstration of technical
competence for successful execution of software projects to meet
customer business objectives.

o PEO-2: Practice life-long learning by pursuing professional

certifications, higher education or research in the emerging areas of
information processing and intelligent systems at a global level.

Course Objectives:
• To provide students with real-world experience in mobile app
development.

• To enhance students' coding proficiency with modern development tools

such as Kotlin and Android Studio.

• To familiarize students with key concepts in UI/UX design and API

integration.

• To strengthen problem-solving and debugging skills through hands-on

projects.

• To prepare students for industry-level projects and professional

collaboration.

Course Outcomes:
• Develop fully functional Android applications using Kotlin and related
technologies.

• Apply app architecture principles to create scalable and maintainable

code.

• Integrate Firebase and other APIs to enhance app capabilities.

• Demonstrate proficiency in modern development environments such as

Android Studio.

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 • Exhibit effective problem-solving skills and the ability to troubleshoot
issues.

• Showcase a comprehensive project in their portfolio, reflecting their

technical competence.

TABLE OF CONTENTS PAGE

NOS.
Certificate on company letter head ............................................................
ii
Declaration.................................................................................................ii
i Acknowledgements..................................................................................
iv Vision &
Mission,PO’sPEO....................................................................... v Course
Objectives & Outcomes................................................................. vi
Abstract.................................................................................................... vii
Table of contents......................................................................................
viii Table of
courses......................................................................................... ix

Chapter 1 Introduction

1.1 Description of the Company

1.2 Overview of the Project

1.3 Technology Stack

Chapter 2 Summary of Experience

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Chapter 3 Reflection on Learning
Conclusion

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 CHAPTER 1 INTRODUCTION
1.1 Introduction to Company :
Google, a leading technology company and subsidiary of Alphabet
Inc., is synonymous with innovation, excellence, and a culture that
fosters creativity and learning. Established in 1998 by Larry Page and
Sergey Brin, Google has grown from a search engine startup into a
global powerhouse, providing a diverse range of services such as
search, cloud computing, advertising, hardware, and software. Google's
reputation for pioneering technologies is particularly notable in the
fields of artificial intelligence and machine learning, where the
company continually pushes the boundaries of what's possible.

Google's AI/ML research has led to groundbreaking advancements,

including the development of TensorFlow, an open-source machine
learning framework widely used in academia and industry. TensorFlow
has become a cornerstone for many AI applications, from image and
speech recognition to natural language processing and robotics.
Google's influence in AI/ML extends beyond technology to practical
applications in healthcare, autonomous vehicles, and more.

The company’s mission is to "organize the world's information and

make it universally accessible and useful." This mission underpins
Google's commitment to creating tools and platforms that improve
people's lives and empower them to achieve more. Google's AI/ML
internship programs align with this philosophy, providing participants
with opportunities to work on real-world projects, collaborate with
experienced professionals, and learn from some of the best minds in the
industry.

Google's culture is centered around innovation, teamwork, and

continuous learning. Interns are encouraged to experiment, take risks,
and contribute their unique perspectives. The company fosters an
inclusive environment where diversity is valued, and every individual
is encouraged to bring their authentic self to work. This emphasis on

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 inclusivity and creativity contributes to Google's status as an attractive
workplace for aspiring AI/ML professionalsIn addition to its
technological achievements, Google is committed to social
responsibility and sustainability. The company invests in various
initiatives to promote education, environmental sustainability, and
diversity. Google's efforts to reduce its carbon footprint and support
renewable energy reflect its broader goal of creating a positive impact
on society.

Internships at Google offer a comprehensive experience, combining

technical challenges with opportunities for personal growth and
professional development. Mentorship is a core component, providing
interns with guidance and support as they navigate complex projects
and learn industry best practices. Collaborative projects encourage
teamwork, while structured code reviews and presentations help interns
refine their skills and build confidence.

Google's AI/ML internship is more than a learning opportunity; it's

an invitation to join a community of innovators committed to
advancing technology and making a meaningful difference in the
world. Through this program, interns gain not only technical expertise
but also an understanding of Google's culture and values, setting the
stage for a successful career in the technology industry.

Google’s 10 biggest AI moments so far:

1. DeepMind's AlphaGo Victory

2. Launch of TensorFlow

3. BERT: Breakthrough in Natural Language Processing (NLP)

4. Google Assistant

5. Google Photos' AI Features

6. Project Maven

7. Auto ML

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 Aspect Description

Program In-depth learning experience in AI and

Focus machine learning, with a focus on
computer vision using TensorFlow.
Learning
Modules A series of structured modules that cover key
AI/ML concepts and applications.
Hands- Practical assignments and projects involving
On real-world AI/ML tasks, such as object
Projects detection and image classification.

Technolog Google's TensorFlow framework, Google

y Colab for code implementation, and Google
Developer Profile for tracking
Stack
achievements.
Earning six Google Developer Profile
Achievemen badges, indicating proficiency in object
t detection, image classification, and product
s image search.
Guidance from experienced professionals
Mentorshi in AI/ML, with regular feedback and
p support throughout the internship.
Team-based projects and collaborative
Collaboratio learning, encouraging teamwork and
n knowledge sharing among interns.
Code
Reviews
and Structured code reviews and project
Presentation presentations to refine skills and foster
s effective communication.
Career Opportunities to network with industry
Developmen experts and gain insights into AI/ML career
t paths.
A comprehensive learning experience that
equips interns with practical AI/ML skills
and prepares them for future opportunities in
Outcome the field.
Table 1.2 Overview of the project

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1.3 Technology stack

Technology Stack Used in the AI/ML Internship.

The technology stack for an AI/ML internship at Google is designed

to offer comprehensive exposure to industry-standard tools and
platforms. This stack enables interns to build, test, and deploy machine
learning models while encouraging collaboration and experimentation.
Below are the key components of this technology stack:

TensorFlow

TensorFlow is Google's open-source machine learning framework, a

critical component of the technology stack. It supports a wide range
of machine learning tasks, from simple linear models to complex
neural networks. During the internship, interns use TensorFlow to build
models for various applications, including image classification, object
detection, and natural language processing. The framework provides
tools for both low-level computations and high-level abstractions,
allowing interns to explore and implement diverse AI concepts.

Google Colab

Google Colab is an online Jupyter notebook environment that

facilitates code development and collaboration. It allows interns to
write, execute, and share Python code in a cloud-based setting,
removing the need for local installations and enabling easy access to
powerful hardware resources, such as GPUs and TPUs. Colab is
instrumental in the internship, providing an ideal platform for
developing, testing, and debugging machine learning models in a
collaborative context.

Google Developer Profile

Google Developer Profile is a platform for tracking achievements and

certifications. During the internship, interns earn badges for completing
various learning modules and projects. This platform provides a way
to monitor progress and showcase skills acquired during the internship.
 It also serves as a valuable resource for learning paths and community
engagement, enabling interns to connect with other developers and
explore additional AI/ML content.

Git and GitHub

Version control is essential for collaborative development, and Git,

along with GitHub, is used to manage code repositories and track
changes. Interns work with Git to maintain project history, collaborate
with other team members, and submit code for review. GitHub serves
as a central platform for hosting code repositories, facilitating code
reviews, and enabling collaborative development within the internship.

Additional Libraries and Tools

Beyond TensorFlow, Google Colab, and Python, interns may use

other specialized libraries and tools depending on their project
requirements. Commonly used libraries include:
Keras: A high-level neural network API that integrates with
TensorFlow, used for building deep learning models with less code.

OpenCV: An open-source library for computer vision tasks, often

used for image processing and object detection.

Natural Language Toolkit (NLTK): A library for natural language

processing tasks, such as tokenization, parsing, and text analysis.
These components make up the technology stack for the AI/ML
internship, providing a solid foundation for interns to develop and
refine their machine learning skills. The stack's flexibility and
versatility allow interns to explore a wide range of AI/ML applications
while promoting collaboration and learning throughout the program.
 Component Description
TensorFlow Google's framework for building and
deploying machine learning and
models.

Google Colab Cloud-based Jupyter notebook environment for

developing and sharing AI/ML code.

Python The primary language used for AI/ML development, known

for its simplicity and flexibility.

Google Platform for tracking achievements and earning

Developer Profile badges for completed AI/ML learning modules.

Git & GitHub Tools for version control and collaborative code
management, crucial for teamwork.

Additional language processing tasks.

Libraries

Table 1.4- Components and their description

CHAPTER 2 SUMMARY OF EXPERIENCE

AIML FOUNDATIONS
1. What is Machine Learning?
Machine learning is the scientific study of algorithms and statistical models
to perform a task by using inference instead of instructions.

Fig-2.1 Flow of Machine learning

Artificial intelligence is the broad field of building machines to
perform human tasks.
Machine learning is a subset of AI. It focuses on using data to train
ML models so the models can make predictions.
Deep learning is a technique that was inspired from human biology.
It uses layers of neurons to build networks that solve problems.
Advancements in technology, cloud computing, and algorithm
development have led to a rise in machine learning capabilities and
applications.

Business Problems Solved with Machine Learning Machine

learning is used throughout a person’s digital life. Here are some examples:

Spam –Your spam filter is the result of an ML program that was

trained with examples of spam and regular email messages.
Recommendations –Based on books that you read or products that
you buy, ML programs predict other books or products that you might
want. Again, the ML program was trained with data from other
readers’ habits and purchases.
• Credit card fraud –Similarly, the ML program was trained on examples
of transactions that turned out to be fraudulent, along with transactions
that were legitimate. Machine learning problems can be grouped.

• Supervised learning: You have training data for which you know the answer.

• Unsupervised learning: You have data, but you are looking for insights
within the data.
• Reinforcement learning: The model learns in a way that is based on
experience and feedback.

• Most business problems are supervised learning.

2 . Machine Learning Process

The machine learning pipeline process can guide you through the
process of training and evaluating a model.
The iterative process can be broken into three broad steps –

• Data processing

• Model training

• Model evaluation ML PIPELINE:

Fig 2.2 Machine learning pipe line

 3 Machine Learning Tools Overview
.

• pandas is an open-source Python library. It’s used for data handling

and analysis. It represents data in a table that is similar to a
spreadsheet. This table is known as a panda Data Frame.

• Matplotlib is a library for creating scientific static, animated, and

interactive visualizations in Python.

• Seaborn is another data visualization library for Python. It’s built on

matplotlib, and it provides a high-level interface for drawing
informative statistical graphics.

• NumPy, a key Python package for scientific computing, includes

functions for N-dimensional arrays and essential math operations like
linear algebra, Fourier transform, and random number generation.

• scikit-learn is a open-source ML library for supervised and

unsupervised learning, offering tools for model fitting, data pre-
processing, selection, evaluation, and more.

INTRODUCING COMPUTER VISION

Computer Vision enables machines to identify people, places, and
things in images with accuracy at or above human levels, with greater
speed and efficiency. Often built with deep learning models, computer
vision automates the extraction, analysis, classification, and
understanding of useful information from a single image or a sequence
of images. The image data can take many forms, such as single
images, v Fig 2.3 Applications of Computer Vision: ideo sequences, views
from multiple cameras, or three-dimensional data.
 Fig 2.3 Applications of Computer Vision
Public safety and home security
Computer vision with image and facial recognition can help to quickly
identify unlawful entries or persons of interest. This process can result in
safer communities and a more effective way of deterring crimes.

Authentication and enhanced computer-human interaction

Enhanced human-computer interaction can improve customer

satisfaction. Examples include products that are based on customer
sentiment analysis in retail outlets or faster banking services with
quick authentication that is based on customer identity and preferences.
Content management and analysis
Millions of images are added every day to media and social
channels. The use of computer vision technologies—such as metadata
extraction and image classification—can improve efficiency and revenue
opportunities.
Autonomous driving
By using computer-vision technologies, auto manufacturers can provide
improved and safer self- driving car navigation, which can help realize
autonomous driving and make it a reliable transportation option.
Medical imaging
Medical image analysis with computer vision can improve the
accuracy and speed of a patient's medical diagnosis, which can result
in better treatment outcomes and life expectancy. Manufacturing
process control Well-trained computer vision that is incorporated into
robotics can improve quality assurance and operational efficiencies in
manufacturing applications. This process can result in more reliable and
cost-effective products.

CHAPTER 3

UNIT-1 Program neural networks with TensorFlow

The "Program Neural Networks with TensorFlow" unit in the Google

AI-ML course introduces the fundamentals of building and training
neural networks using TensorFlow, a powerful open- source machine
learning framework. The unit begins with an overview of neural
networks, explaining key concepts such as layers, activation functions,
and the flow of data through a network. Participants learn how to
define and structure neural networks in TensorFlow, using its intuitive
APIs like Keras to create models that can handle various tasks, from
basic image classification to more complex functions. The unit also
covers the process of compiling and training the model, introducing
concepts like loss functions, optimizers, and metrics that are crucial for
evaluating the performance of a neural network.

In addition to theoretical knowledge, this unit emphasizes hands-on

practice by guiding learners through building and training their own
neural networks. By experimenting with different architectures and
hyperparameters, participants gain a deeper understanding of how to
optimize and improve model performance. The unit concludes with
techniques for validating and testing models to ensure they generalize
well to new data, as well as an introduction to saving and loading
trained models for future use. This foundational unit equips learners
with the essential skills needed to leverage TensorFlow for a wide range
of AI and machine learning applications.
 UNIT-2 Get started with object detection

"Get Started with Object Detection" focuses on the techniques and

tools required to identify and locate objects within images or video
frames. This unit introduces fundamental object detection concepts,
including bounding boxes, class labels, and the use of convolutional
neural networks (CNNs) for feature extraction. Learners become
familiar with popular object detection models, such as YOLO (You
Only Look Once) and SSD (Single Shot MultiBox Detector), and
explore how these models can be applied to detect multiple objects in
an image with varying sizes and orientations. The unit emphasizes the
importance of dataset preparation and annotations, guiding participants
through the process of preparing training data and evaluating model
performance.

Hands-on exercises are a key component, allowing learners to

implement and fine-tune object detection models using frameworks like
TensorFlow or PyTorch. By working with real-world datasets and
experimenting with
different hyperparameters and model architectures, participants gain
practical experience in improving detection accuracy and handling
challenges such as occlusions and varying object scales. The unit
equips learners with the skills needed to build robust object detection
systems that can be applied to various
applications, from security surveillance to autonomous vehicles.
 UNIT-3 Go further with object detection

"Go Further with Object Detection" delves into advanced techniques

and improvements for enhancing the performance of object detection
systems. Building on foundational concepts, this unit explores
sophisticated models and algorithms that offer more accurate and
efficient detection. Topics may include region-based approaches like
Faster R-CNN, which refines object detection by proposing regions of
interest before classification, and multi-scale detection methods that
improve performance across different object sizes. The unit also covers
techniques for handling challenging scenarios such as overlapping
objects, varying lighting conditions, and complex backgrounds.

In addition to model enhancements, learners gain insight into

optimizing detection systems for real-world applications. This includes
strategies for deploying models in production environments, improving
inference speed, and integrating object detection with other computer
vision tasks. Practical exercises involve experimenting with advanced
architectures, fine-tuning pre-trained models, and employing data
augmentation techniques to further boost accuracy. The unit aims to
equip learners with a deeper understanding of object detection and the
skills needed to tackle more complex challenges and deploy robust
solutions in diverse scenarios.
 UNIT-4 Get started with product image search

"Get Started with Product Image Search" introduces the techniques

and
tools used to build systems that allow users to search for products
based on images rather than text. The unit begins by explaining the
fundamentals of image retrieval and similarity search, emphasizing the
importance of feature extraction and representation. Learners are
introduced to methods such as extracting visual features from images
using convolutional neural networks (CNNs) and leveraging these
features to compare and rank images in a database. This enables users
to input an image and retrieve visually similar products, facilitating a
more intuitive and efficient search experience.
The unit also covers practical aspects of implementing a product
image search system, including dataset preparation, image indexing,
and query handling. Participants gain hands-on experience by building
and evaluating their own image search systems using frameworks and
libraries that support feature extraction and similarity computation. By
experimenting with
different techniques and optimizations, learners develop the skills
needed to create a functional and accurate image search application,
addressing challenges such as scaling, relevance ranking, and user
experience.

UNIT-5 Go further with product image search

"Go Further with Product Image Search" builds on the foundational

concepts introduced in the previous unit by exploring advanced
techniques and optimizations for enhancing product image search
systems. This unit delves into sophisticated methods for improving
search accuracy and efficiency, such as leveraging deep learning-based
feature embeddings and advanced similarity metrics. Learners explore
techniques for refining image representations, such as using transfer
learning with pre-trained models or implementing custom architectures
tailored to specific product categories. Additionally, the unit covers
approaches for handling large-scale image databases, including
distributed indexing and retrieval systems to ensure fast and scalable
search performance.

The unit also addresses real-world challenges and practical

considerations in deploying image search systems. This includes
techniques for handling diverse and high-dimensional data, optimizing
search algorithms for both relevance and speed, and integrating
feedback mechanisms to improve search results over time. Learners
gain hands-on experience with advanced tools and frameworks,
applying these techniques to build and fine-tune a robust product
image search system. By focusing on these advanced aspects, the unit
equips learners with the knowledge and skills needed to implement
sophisticated image search solutions in production environments .

UNIT-6 Go further with image classification

"Go Further with Image Classification" extends the foundational

knowledge of image classification by exploring more advanced
techniques and methodologies to enhance model performance and adapt
to complex scenarios. This unit delves into deep learning architectures
such as convolutional neural networks (CNNs), including advanced
variants like ResNet, Inception, and DenseNet, which address
challenges like vanishing gradients and improved feature extraction.
Learners gain insight into techniques for fine-tuning these models on
specialized datasets, leveraging transfer learning to adapt pre-trained
models for new classification tasks, and implementing sophisticated
regularization methods to prevent overfitting and improve generalization.

Additionally, the unit covers strategies for addressing real-world

challenges in image classification, such as handling class imbalance,
improving model
robustness against adversarial attacks, and deploying models in
resource-constrained environments. Practical exercises involve
 experimenting with data augmentation, hyperparameter tuning, and
evaluating model performance using advanced metrics and validation
techniques. By integrating these advanced concepts and tools, the unit
aims to equip learners with the expertise needed to tackle complex
image classification problems and develop high-performing models for
diverse applications.

LEARNINGS AFTER INTERNSHIP

1 . Enhanced Understanding of AI and ML o Deepened my

knowledge of various AI and ML techniques, including supervised and
unsupervised learning, neural networks, and deep learning models.
o Gained a more refined understanding of how different algorithms and
models are applied to address real-world challenges.

2. Practical Application of Theoretical Concepts o Bridged the

gap between theory and practice by learning to implement and fine-tune
models using real-world datasets.
o Acquired hands-on experience with tools like Python, TensorFlow, Keras,
and
Scikit- Learn, applying them effectively in various projects.

3 . Data Handling and Preprocessing Skills o Enhanced

my abilities in data cleaning, preprocessing, and visualization,
essential for preparing data for analysis and model training.
o Gained experience in tackling data challenges, such as handling missing
values,
normalization, and feature extraction.

3 . Model Development and Evaluation o Developed and

evaluated diverse machine learning models, learning the
significance of performance metrics and optimizing hyperparameters.
o Utilized tools for model evaluation, including cross-validation and
performance metrics, to ensure the accuracy and robustness of models.

5 . Collaboration and Communication

o Improved my ability to work collaboratively with peers and mentors,
sharing
insights, reviewing code, and actively contributing to group discussions.
o Enhanced my skills in documenting work, preparing reports, and
presenting
findings clearly and effectively.

6. Ethical Considerations in AI o Cultivated an awareness of the ethical

implications of AI technologies,
particularly
o Gained experience in managing end-to-end projects, from initial data
collection
and model development to final deployment and presentation of results.
o Learned to manage project timelines, deliverables, and integrate
various
components to achieve project goals.
Overall, this internship provided invaluable practical experiences,
deepening my understanding of AI and ML concepts, while also
enhancing the technical and soft skills essential for a career in the field.
 CONCLUSION

These chapters described how model explain ability relates to AI/ML

solutions, giving customers insight to explain ability requirements when
initiating AI/ML use cases. Four pillars were presented to assess model
explain ability options to bridge knowledge gaps and requirements for
simple to complex algorithms. To help convey how these models
explain ability options relate to real-world scenarios, examples from a
range of industries were demonstrated. It is recommended that AI/ML
owners or business leaders follow these steps when initiating a new AI/ML
solution:

1 . Collect business requirements to identify the level of explain

ability required for your business to accept the solution.
2 . Based on business requirements, implement an assessment for
model explain ability.
3 . Work with an AI/ML technician to communicate model explain
ability assessment and find the optimal AI/ML solution to meet your
business objectives.
4. After the solution is completed, revisit the model explain ability
assessment to evaluate that business requirements are continuously met.
5. By taking these steps, we will mitigate regulation risks and ensure
trust in our model. With this trust, when the event comes to push
your AI/ML solution into an Google production environment, we will
be ready to create business value for our use case.
 BIBLIOGRAPHY

1 . https://www.ibm.com/topics/artificial-intelligence
2 . https://www.w3schools.com/python/
3 . https://www.tutorialspoint.com/python/index.htm
4 . https:// www.geeksforgeeks.org/artificial-intelligence-an-
introduction/
5 . https://www.javatpoint.com/artificial-intelligence-ai
6 . https:// www.geeksforgeeks.org/artificial-neural-networks-and-its-
applications/