Songkla University • Machine Learning Google Developer Expert (Dec 2023-Now) • Machine Learning Researcher with an interest in medical NLP, sentiment analysis, social media analysis, and information extraction • 10 years of working experience in Natural Language Processing projects focused on sentiment analysis, social media monitoring, and machine learning applications • Published scientific articles in international peer-reviewed journals and conferences • Current Affiliations - Lecturer (KMUTT, Bangkok, Thailand)/Adjunct Professor (Parami University, Washington D.C, USA) GDG Yangon/Online
Well-funded projects and cutting-edge tools. • Exposure to full AI lifecycle: R&D to deployment. Cons High-pressure, competitive environment. Focus on product constraints may limit exploratory research. Unstable job security in start-up companies due to failing customer acquisition
to engage directly with clients. • Develop adaptable problem-solving skills. Cons • May lack depth in specific domains. • Often requires extensive travel or fast-paced work cycles. • Unstable job security in start-up companies
solutions. • Integration with broader business strategies. • Opportunities for cross-department collaboration. • Good for job security Cons • Slower pace of innovation compared to AI startups. • Limited exposure to diverse problems.
ideas. • Influence through teaching and mentoring. • Potential for collaborative grants and projects. • Good for job security Cons • Limited funding and slower implementation cycles. • Balancing teaching, research, and administrative duties.
• Access to extensive computational resources and collaborations. • Publish in high-impact venues. Cons • May lack immediate industry applications. • Competitive environment for funding and recognition. • Job security strongly depends on the number of publications and grants
on the company's market performance and profitability. ◦ Risk of layoffs during economic downturns or product de-prioritization. ◦ Best for: Those willing to take some risk for innovation and career growth. Service Companies Moderate ◦ Projects are tied to client contracts, which can lead to fluctuations in demand. ◦ Job security improves with seniority and client relationships. ◦ Best for: Professionals who can adapt to changing roles and environments.
to the company’s core operations rather than market trends. ◦ Lower risk of layoffs compared to startups or product companies. ◦ Best for: Those seeking a stable, domain-specific role. Universities Very High (with tenure), Moderate (without tenure) • Tenured faculty enjoy exceptional job security. • Non-tenured roles or adjunct positions may have limited stability • Best for: Those committed to academia and long-term career growth.
funding availability and research grants. ◦ Secure for well-established researchers or permanent positions. ◦ Best for: Researchers with proven track records and funding acquisition skills.
product development experience from client projects. ◦ Showcase understanding of scalable and user-focused AI solutions. • From Academia/Research: ◦ Emphasize applied research with a tangible impact. ◦ Build a portfolio showcasing deployed models or prototypes. Moving to Service Companies • From Product Companies: ◦ Highlight versatility in solving diverse problems. ◦ Demonstrate adaptability to new industries and client-focused collaboration. • From Academia/Research: ◦ Focus on practical problem-solving and interdisciplinary skills. ◦ Show ability to manage multiple projects simultaneously.
Emphasize experience with domain-specific AI applications. ◦ Demonstrate understanding of aligning AI solutions with business needs. • From Academia/Research: ◦ Showcase deep technical knowledge applied to enterprise challenges. ◦ Highlight ability to work on long-term, stable projects. Moving to Universities • From Industry (Product/Service/Enterprise): ◦ Build academic credentials: publish papers, attend conferences. ◦ Highlight mentoring and teaching experience within teams. • From Research Institutions: ◦ Leverage existing publications and funding acquisition experience. ◦ Demonstrate commitment to teaching and student development.
and academic network. ◦ Highlight impactful and collaborative research projects. • From Industry (Product/Service/Enterprise): ◦ Show ability to translate practical challenges into research opportunities. ◦ Build credibility by publishing in peer-reviewed journals or presenting at conferences.
Skill Adaptation: Tailor your skills to align with the target organization's priorities. Networking: Leverage connections within the target industry or academic field. Personal Branding: Update your portfolio, LinkedIn, and resume to showcase relevant achievements. Upskilling: Take certifications or online courses to fill skill gaps (e.g., teaching skills for academia). Mentorship: Seek advice from those who have transitioned successfully.
Company Background • Name: Dr. Maya Patel • Previous Role: Assistant Professor in Computer Science at a mid-tier university. • Expertise: NLP research, with publications • Goal: Transition to a role in a product company to work on real-world NLP applications. Challenges • Lack of Industry Experience: a. No prior exposure to working in fast-paced industry environments. • Limited Exposure to Scalable Systems: a. Research was focused on theoretical advancements rather than deployment. • Networking: a. Few connections in the tech industry outside of academia. Strategies for Transition 1. Skill Enhancement: ◦ Took an online course in ML Ops to understand model deployment and monitoring. ◦ Completed Kaggle competitions to showcase hands-on data processing skills. 2. Portfolio Development: ◦ Open-sourced research code on GitHub with detailed documentation. ◦ Built a prototype of a sentiment analysis app using a pre-trained NLP model. 3. Networking: ◦ Attended industry-focused conferences ◦ Reached out to former academic peers now working in the tech industry. 4. Targeted Applications: ◦ Applied to companies which value R&D skill, emphasizing transferable skills. ◦ Tailored her resume to highlight real-world impact of her research (e.g., improving chatbot algorithms). 5. Interview Preparation: ◦ Practiced coding interviews on LeetCode. ◦ Researched company-specific use cases for NLP to align her pitch.
often comes from a standard software engineering pipeline. The ML Engineer requires a rare mix of ML and Software Engineering skills. This person is either an engineer with significant self-teaching OR a science/engineering Ph.D. who works as a traditional software engineer after graduate school. The ML Researcher is an ML expert who usually has an MS or Ph.D. degree in Computer Science or Statistics or finishes an industrial fellowship program. The ML Product Manager is just like a traditional Product Manager but with a deep knowledge of the ML development process and mindset. The Data Scientist role constitutes a wide range of backgrounds, from undergraduate to Ph.D. students. https://fullstackdeeplearning.com/course/2022/lecture-8-teams-and-pm/#common-roles
• Researcher: Publish in peer-reviewed journals; mentor Ph.D. students. • Postdoctoral Fellow: Conduct cutting-edge research. Key Characteristics: • Emphasis on academic exploration and knowledge dissemination. • Flexibility in choosing research topics. Growth Path: • Lecturer → Assistant Professor → Associate Professor → Full Professor
model that recommends restaurants that users will most likely order from, and believe they can do so by using a more complex model with more data. •Sales team – wants a model that recommends the more expensive restaurants since these restaurants bring in more service fees. •Product team – wants a model that return the recommended restaurants in less than 100 milliseconds. •ML platform team – want to hold off on model updates to prioritize improving the ML platform. •Manager – wants to maximize the margin, and one way to achieve this might be to let go of the ML team
(Burmese, Sinhala, and Tamil languages) ML Solution – Classification Problem (Binary – Hate speech/No-Hate Speech) / Regression (A numerical score for hate speech intensity) Challenges • How to define hate speech?? (Took more than 4 months to get data labelling guideline) • Binary classification is not enough • Not all countries have the same hate speech scenarios (need political context knowledge) • hierarchical classification or multiclass classification are requested by the clients • A new category will be added in the future which leads to re-training the model from the beginning
Business objective To maximize profits of shareholders Increase sales Cutting costs High customer satisfaction Increase time spent Chip Huyen, Designing Machine Learning Systems (Chapter 2)
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