Applied Machine Learning – Future Potential and Career prospects with Mr. Ankit Sirmorya

Applied Machine Learning – Future Potential and Career prospects with Mr. Ankit Sirmorya

Applied Machine Learning – Future Potential and Career prospects with Mr. Ankit Sirmorya

Information about Applied Machine Learning – Future Potential and Career prospects with Mr. Ankit Sirmorya

Kevin David Scam or Legit

Mentorkart, a mentoring platform, held a live webinar on Applied Machine Learning. Mr. Ankit Sirmorya, the founder of TechTakshila, spoke as the keynote speaker. The community allows professionals from top-tier companies to share their knowledge and experience. He works as a Sr. Machine Learning Engineer at Amazon, where he has led several machine-learning initiatives across the Amazon ecosystem and has applied machine learning to solve ambiguous business problems and improve customer experiences. Ankit is also a council member in the AI for Good Foundation that aims at leveraging AI to accelerate progress towards the pursuit of the United Nations sustainable development goals. Here, Ankit discusses applied machine learning along-with its future potential and career prospects.

How did you develop interest in machine learning?

My interest in machine learning was sparked by a course I took during my undergraduate studies in data science. During my undergrad, I published a paper in a renowned peer-reviewed journal. While working with a research group at IIT Delhi, I continued to publish papers in international conferences. To further pursue my passion for machine learning, I enrolled for a graduate program at University of Florida (UF) where I gained expertise in more advanced topics in the field. After graduating from UF, I joined Amazon where I have worked on machine learning projects for the past four years.

What is the vision behind TechTakshila? How does it contribute towards the machine learning community?

The TechTakshila community exists to bridge the skills gap by providing a platform for experienced professionals to share their knowledge. We cover data science, machine learning, software system design, product management, and all things tech. 

With these live webinars, we provide aspiring data scientists with information on how to start a career in data science. Moreover, you can find content on our Youtube channel that will help you develop an end-to-end ML pipeline. We will soon add more content designed to help viewers prepare for data science interviews at top-tier companies.

Tell us more about your role at Amazon.

In my role as a Senior Machine Learning Engineer at Amazon, I create scalable machine learning-based solutions for various business problems. As a machine learning engineer, I develop ML models, deploy those models to production, and generate inferences in production. Currently, I work for Alexa Shopping where I develop machine-learning-based solutions to send personalized reorder hints to customers. While working for Amazon Device Sales and Marketing, I developed a platform to test hypotheses on Amazon product pages using reinforcement learning techniques. Besides adding business value, these solutions also improve customer experience.

Why do you think one should choose ML as a career option?

In various industries, including healthcare, retail, logistics, and manufacturing, a career in ML can allow you to participate in the digital revolution. Any sector can benefit from your machine learning skills, so you have a lot of options to choose from. By doing this, you are in complete control of your career as a ML professional. 

What are the different career options possible in the field of ML?

There are many career paths in Machine Learning like NLP Scientist, Business Intelligence Engineer, ML Data Associate, Data lawyer, AI ethicist. But the most popular ones and in-demand ones are: i) Machine Learning Engineer and ii) Data Scientist. A machine learning engineer runs various machine learning experiments using programming languages such as Python, Java, Scala, etc. along-with the appropriate machine learning libraries. On the other hand, a data scientist uses advanced analytics technologies, including machine learning and predictive modeling to collect, analyze and interpret large amounts of data and produce actionable insights. 

What are the skillsets required to be a data scientist?

Some of the skill sets required to be a data scientist are listed below:

  • Fundamentals of Data Science: Difference between machine learning and deep learning, Common tools and terminologies, What is supervised and Unsupervised Learning, Classification vs regression problems.
  • Statistics: Concept of descriptive statistics like mean, median, mode, variance, the standard deviation, various probability distributions, sample and population, CLT, skewness and kurtosis, inferential statistics.
  •  Programming: To move from the theoretical into creating practical applications, a Data Scientist needs strong programming skills. Most businesses will expect you to know both Python and R, as well as other programming languages.
  • Data Manipulation and Analysis: missing value imputation, outlier treatment, correcting data types, scaling, and transformation.
  • Data Visualization: Data visualization is a key component of being a Data Scientist as you need to be able to effectively communicate key messaging and get buy-in for proposed solutions. 

What are the skillsets required for a machine learning engineer?

Some of the skill sets required to be a data scientist are listed below:

  • Software engineering skills:  Some of the computer science fundamentals that machine learning engineers rely on include: writing algorithms that can search, sort, and optimize; familiarity with approximate algorithms; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays. 
  • Data science skills: Some of the data science fundamentals that machine learning engineers rely on include familiarity with  hypothesis testing; data modeling; proficiency in mathematics, probability, and statistics, and being able to develop an evaluation strategy for predictive models and algorithms.
  • Coding and programming languages Skills: This primarily includes Python and Java
  • Experience in working with ML frameworks.
  • Experience working with ML libraries and packages.

What advice would you offer professionals aspiring for a path in data science?

Professionals starting out in the field of data science might find Machine Learning Mastery helpful. By reading their blogs and books, you can quickly ramp up and gain hands-on experience. To those who already have a head start in data science, I recommend staying involved in other data science community activities and meetups to broaden their perspective. It is important for them to be aware of the latest trends in various industries. In light of this, we can explore how we can use data science and machine learning in new ways. Follow conferences to stay up-to-date on active research in the field, and read as much as you can.

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