Introduction
There has been an increase in the availability of data and the need for businesses to make technology related and data-driven decisions. Developing sophisticated machine learning algorithms and artificial intelligence techniques has led to a demand for skilled professionals in companies such as Google and Micorsoft.
“Did you know that there is no study guide in the market of Google”
Did you know that? Well, if you didn’t, let’s learn more unknown facts with Ms. Mona! Get ready to be inspired by a person who, after facing challenges, never gave up but came up with solutions to overcome those challenges.
Ms. Mona is a Machine Learning Specialist currently working at Google while having previously worked at Amazon Web Services (AWS). Machine Learning Specialists are especially in demand as they have expertise in developing and implementing algorithms and models using tools such as Python, R, Tensor Flow, etc. to analyze large amounts of data and extract meaningful insights. Working with data scientists and other professionals helps identify problems and develop solutions to make informed decisions about a business/organization.
In this article, you will get to know the following things:
- Gain insights into the career trajectory of a successful AI/ML specialist and the skills and experience required to achieve such a role.
- Understand the value of pursuing a master’s degree in computer science or a related field to gain expertise in AI and ML.
- Learn about the importance of building a solid network and seeking out mentorship opportunities to advance one’s career.
- Gain an understanding of the challenges faced by professionals in the field of AI/ML and the strategies used to overcome these challenges, such as evangelizing solutions and creating awareness.
- Explore the ways in which AI/ML can be used to solve real-world problems and create value for organizations in various industries.
The Journey from a Java Developer to AI/ML Specialist
AV: Hello, Welcome to analytics vidhya! How are you? Can you please share your professional journey and how you got to where you are today at Google?
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Ms. Mona: Hello! Thank you so much for having me. As for my professional journey, after graduating from Indraprastha University, Delhi, I started working as a Java Developer. Then, I completed my post-graduation in Computer Information Systems from Georgia State University.
During my master’s course, my major was in Big Data Analytics. I was also introduced to Machine Learning courses which inspired me most as I loved Machine Learning. I wanted to become a Data Scientist. However, getting into a Data Science career without a Ph.D. was hard. Before coming to the US, I already had multiple certifications, such as AWS Solutions Architect, Scrum Master, and others. My resume got picked by AWS for the Associate Solution Architect program, which was the first of its kind. I was fortunate to interview and selected to join Amazon in 2017 as a Solution Architect. After joining Amazon as an Associate Solution Architect, I got the opportunity to specialize in Machine Learning and move to the Machine Learning Specialist Solution Architect role.
AV: That’s an inspiring story! So, after joining Amazon as an associate solution architect, You got the opportunity to specialize in machine learning and move to the machine learning specialist solution architect role. Can you share an example of a project or initiative you’ve worked on at Amazon that you’re particularly proud of, And what impact did it have?
Source: ostomylifestyle.org
Ms. Mona: I authored 17 blogs and got the chance to work on a research paper on Neural Search called Cord 19 search. I also spoke at multiple conferences and wrote launch blogs. I also had all AWS cloud certifications and authored a book called Natural Language Processing using AWS AI services.
I was promoted twice in the 4.5 years of my career at Amazon. I joined Google in 2021 as an AI/ML specialist to learn about the Google Cloud platform. Google was my dream company, a leader in AI and ML.
AV: Wow, Your professional journey is truly inspiring! It’s fascinating to hear about your background at AWS. What prompted you to shift to Google?
Ms. Mona: Google was my dream company as they are leaders in AI research and ML. I have been curious to understand Google Cloud AI offerings since Google launched the Vertex AI platform in 2021. I wanted to be at a place of innovation when their ML roadmap was being defined. I wanted to contribute to the AI/ML roadmap of Google Cloud and learn from its broad applicability across domains such as Healthcare, Public Sector, Finance, etc.
AV: Thank you for sharing the insights behind your decision to move to Google; It’s fascinating to hear about your thought process. How has your experience been working with two major FAANG companies, And what are the differences you’ve noticed between working at Google and AWS?
Ms. Mona: I owe everything to Amazon, such as my author experience, writing multiple technical blogs, and speaking at multiple conferences. Amazon is a rapidly changing place, and you must be on your toes to succeed. I loved the fast-paced environment of Amazon, and I was able to scale myself to multiple innovative things at Amazon. Google is a fun and relaxed place to work with excellent employee benefits. Google’s working style is thriving in ambiguity.
AV: Definitely, You have worked on many projects at Google. Can you please share an example of a project or initiative you’ve worked on at Google that you’re particularly proud of, And what impact did it have?
Ms. Mona: I worked on the RadLab platform for alphaFold protein folding. I presented this at the International Conference for Molecular Biology in 2022. This RadLab AlphaFold provides an automated cloud environment to researchers and solves protein folding problems. Previously it used to take decades to fold a protein. With Deepmind’ making the AlphaFold model available, it is possible to fold protein sequence and visualize it in hours rather than decades.
Source: 9to5Google
You can refer to my blog to learn more
Another initiative is the book I am about to publish called Google Cloud Certified ML Engineer which is currently in preview. This is going to be an official Google Study Guide.
After joining Google and taking this certification, I realized there is no such study guide in the market, and also, no Googler has ever created any Google study guide. I took this as an opportunity and submitted a proposal with Wiley four months after joining Google.
AV: Obviously, While working on real-life projects, There are some challenges everyone has to face, so can you please tell me some of the challenges that you faced in the project and how did you overcome them?
Ms. Mona: Challenges faced were evangelizing the solution and making it available to researchers. As the research community does not know such a solution exists. I created blogs and presented at conferences to discuss this solution and create awareness. I plan to publish a research paper in a bioinformatics journal, a go-to place for researchers to leverage this fantastic solution.
AV: As you have worked in a data role currently, What drew you to work with data, And what do you find most exciting about your role as an AI/ML specialist?
Ms. Mona: During my masters, I loved the AI class by Prof and Dept Chair Prof Bala Subramaniam. That led me to play with various kaggle datasets and apply ML methods to solve a particular problem. I love my role as an AI/ML specialist creating an impact with Government and public sector customers to help them solve problems using cutting-edge AI/ML techniques. These problems can be Document automation, Document search, Summarization, or using deep learning models to identify something to improve their productivity and save costs.
AV: You also spoke for Analytics Vidhya’s data hour in April 2023. How was your experience as a guest speaker?
Ms. Mona: This was my second time speaking at Analytics Vidhya. I absolutely loved the opportunity both of these times. It was fulfilling as a speaker to have people from different countries asking questions and wanting to learn more about you and your work. I am extremely grateful for these opportunities and the attendees for my sessions for showering a lot of love and their feedback. I want each of them to succeed in their careers.
AV: What are 3 things you learned from being a guest speaker at analytics Vidhya’s data hour?
Ms. Mona: Firstly, I observed the massive Audience in my session. I saw 4498 people registered for my session, evident with the 3 million + data science community members of the Analytics Vidhya platform. Secondly, the Influence of my session on the data science community, I got exposure to helping the community during the session. Lastly, since this session was on a Global scale, I was surprised to see people across multiple countries really eager to learn due to the global community provided by Analytics Vidhya for free of cost.
AV: Oh! That’s great to hear from your side. So according to you, What are some of the most critical technical and soft skills an AI/ML specialist should possess, and how have you developed these skills over time?
Ms. Mona: So, talking about the Technical skills, which include, Ability to code and understand overall architecture such as networking, security, storage, computing, and AI/ML.
On the other hand, Soft skills include, since I came from a coding background, so for me the hard part of developing presentation skills. I remember being nervous during my first customer call. You must also be a good listener, listening to customers and understanding their problems before solving them. Asking meaningful questions was another skill needed for discovering customer use cases. I would say developing soft skills are more complex than technical skills, but with the Amazon Tech U associate solution architect program, I was trained for 6 months to learn these skills. We would record our presentations and listen to ourselves how we sound in front of customers. We would also shadow a senior Solution architect before speaking to the customer.
AV: As there are many approaches in the market to solve problems, How do you approach problem-solving as a data engineer, And what methods have you found most effective?
Ms. Mona: Problem-solving is about understanding the problem first. Some of the ways I do
- Listen actively to what the customer wants or what the problem is.
- Ask clarifying, meaningful questions to understand the problem.
- If I don’t know the solution, search internally or ask for experts in the domain area for help. It’s always helpful to seek expert guidance before killing yourself to find an answer.
- Provide a holistic cloud AI solution taking 1,2 and 3 into consideration.
AV: Also, what are some of the biggest challenges you face as an AI/ML specialist, And how did you overcome them?
Ms. Mona: The biggest challenge is that space AI is evolving in a space that is difficult to keep up. It’s exciting and overwhelming at the same time. With Google Cloud Generative AI offerings, many new use cases can be solved easily with LLMs and large language models, which was not possible before.
I overcame this challenge by taking it one day at a time. I try to spend every day reading and learning about new advances in AI and ML.
AV: Now, talking about the collaboration of organizations, So how does Google collaborate with other companies, organizations, and academic institutions to advance the field of AI/ML, And what benefits does this collaboration bring?
Ms. Mona: Google Cloud collaborates with organizations and academic institutions through the Google Cloud sales team and group of AI/ML experts like me. The benefit of the collaboration is the ability to solve complex problems with automation and improve employee productivity using Google Cloud technologies in a secure, cost-effective, highly available, and scalable manner.
AV: Since Google is a very big organization, What sets Google apart from other companies in the field of AI/ML, And how has the company’s approach to these technologies evolved over time?
Ms. Mona: Google Cloud products have been directly influenced by Google research. Google Cloud recently launched Generative AI offerings that are directly influenced by Google’s open-source work on large language models. Google is committed to bringing its AI research in the form of Google Cloud products so that everyone can benefit from the heavy investment in research and innovation by Google.
AV: How do you approach ethical considerations in developing and deploying AI/ML technologies at Google? What steps does the company take to ensure that these technologies are used for positive purposes?
Ms. Mona: Google Cloud has responsible AI practices integrated into all its AI/ML offerings
They have built model cards and provided explainable AI SDK to help implement AI responsibly.
AV: Based on your experience, what advice would you give someone interested in pursuing career as AI/ML specialist, And how can they prepare themselves for success in this field?
Ms. Mona: I would recommend that you keep exploring Cloud AI solutions. As we would need more people in this space. My idea for writing the book “Natural language processing using AWS AI Services was to empower students and IT professionals to get started with machine learning with no previous expertise with low/code no code AWS AI services.
My book can be a great starting point to give students and It professionals an idea of how easy it is to solve a business problem using Cloud AI services. The book has been part of the Georgia state university curriculum. It comes with dataset, notebook examples, and industry use cases to solve problems such as building a chatbot or NLP-based search. Secondly, I would advise you to gain any cloud certification to get started. Thirdly, try doing multiple hands-on projects with the guidance and data provided in my book and add it to your Github. That showcases your deep understanding.
AV: Thanks for your time! This will be helpful for all aspirants who want to go into data sciences or data fields. So, Let’s conclude today’s discussion.
Conclusion
To conclude this success story, Ms. Mona’s journey is truly inspirational for anyone who wants to pursue a career in the field of AI/ML. From her humble beginnings in India to her current position at Google, Ms. Mona’s dedication and hard work have brought her success and recognition in the industry.
Her work on the RadLab platform for alphaFold protein folding and the upcoming Google Cloud Certified ML Engineer book exemplify her expertise in the field. Through her efforts, Ms. Mona has contributed to the AI/ML community and helped solve real-world problems using cutting-edge technology.
Her experience as a guest speaker for Analytics Vidhya’s Data Hour also highlights her willingness to share her knowledge with others and help them succeed in their careers. Ms. Mona’s story is a testament to the fact that anyone can succeed in their chosen field with passion and hard work.
