How I became ML Engineer
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One of the most popular buzz words nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations to seek experts who can implement Machine Learning into their businesses. The paper will be written for statistical programmers who want to explore Machine Learning career, add Machine Learning skills to their experiences or enter a Machine Learning fields. The paper will discuss about personal journey to become to a Machine Learning Engineer from a statistical programmer. The paper will share my personal experience on what motivated me to start Machine Learning career, how I started it, and what I have learned and done to be a Machine Learning Engineer. In addition, the paper will also discuss the future of Machine Learning in Pharmaceutical Industry, especially in Biometric department.
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How I became ML Engineer
- 1. How I became Machine Learning Engineer from Statistical Programmer Kevin Lee
- 2. Disclaimer The views and opinions presented here represent those of the speaker and should not be considered to represent any companies or organizations.
- 3. Honey,do you know about Machine Learning? 3
- 4. Why people expects me to know about Machine Learning? • Programming • Statistics / Modeling • Data
- 5. What is Machine Learning? An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- 6. Explicit Programming vs Machine Learning Explicit Programming Machine Learning
- 7. How does Human Learn? - Experience
- 8. How does Human Learn? - Experience How does Machine Learn? Data
- 9. How does Machine learn with data? ML Models Test Data (cats) Real Data cat
- 10. Why Machine learning is so popular? • Can solve a lot of complex business problems – New Business • Cost effective • Can work 24 / 7 • Automate a lot of works • “Pretty much anything that a normal person can do in <1 sec, we can now automate with AI” Andrew Ng • Accurate • Can be more accurate than normal people.
- 11. So, who is Machine Learning Engineer? • Develop Machine Learning models. • Validate ML models. • Deploy ML models into the production. • Continuously monitor and update the ML models. Usually working with • Data Scientist • Data Engineer • Cloud Computing Architecture (AWS)
- 12. Sample Job Descriptions • Graduate degree (MS or PhD) in computer science, engineering, mathematics, or related technical/scientific field • 5+ years of professional experience in a business environment • 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems • 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM) • Experience in using Python or other programming languages
- 13. Typical Skill sets for ML Engineer • Strong Programing experience in Python, Java, or C++ • ML modeling experience in CVM, Logistic Regression, Regression, DecisionTrees, Random Forest, K mean Clustering • Deep Learning experience in CNN, RNN, NLP. • ML package experience in Scikit Learn,TensorFlow, Keras, PyTorch • Cloud Computing environment in AWS,AZURE, Databricks, IBM Watson and Google Cloud • Database experience in Hadoop, Data Warehouse, Data Lake, NoSQL, Relational Database • MLOPs experience in data pipelines, feature engineering, ML model selection/training/validation, and finally to the deployment (e.g, Docker, API) in the production. • Excellent communication and presentation skills
- 14. How can I learn new ML Skill Sets?
- 15. PhD or Master’s degree programs in Universities / Colleges • One of the hottest majors in college nowadays are Computer Science and Data Science. • Both on-line / in-person courses. • The fastest way to learn Machine Learning skill sets. • 2 to 6 years to get the degree
- 16. ML Certificate Programs • The most popular certificate program is Machine Learning certificate program. • A lot of major universities currently provide ML Certificate Programs – MIT, Cornell, Harvard, University of Washington, Coursera, Edx and more • 2 to 6 months long • Affordable
- 17. ON-LINE COURSES / MOOC (MASSIVE OPEN ONLINE COURSES) • On-line degrees, certificates • Very popular ways to learn ML • As good as any college courses • Very affordable 17
- 18. Many Machine Learning Courses and Videos
- 19. GitHub. Many ML models and implementations are posted in GitHub in https://github.com/. GitHub also provides self- studying materials and codes. So, many beginners could download sample codes and data, and they could practice and run ML models in their own environments. 19 GitHub – Code Repository • Self-studying materials and codes. • Many ongoing Machine Learning Projects
- 20. 20 • ML Practices and Competition Environment. • Playground for ML Engineers • Collaborative Projects Environment If one becomes Kaggle Grandmaster or master, you will be recognized in ML community.
- 22. Personal Experience in learning ML Concepts, Algorithms Python Programming
- 23. MACHINE LEARNING CONCEPTS /ALGORITHMS • Machine Learning at Stanford University • Neural Network & Deep Learning at DeepLearning.ai • Improving Deep Neural Network : Hyperparameter tuning, Regularization & Optimization at DeepLearning.ai • Convolutional Neural Network at DeepLearning.ai • Sequence Models at DeepLearning.ai • Structuring Machine Learning Projects at DeepLearning.ai • AI for Everyone at DeepLearning.ai • AI for Medical Diagnosis at DeepLearning.ai • AI for Medical Prognosis at DeepLearning.ai
- 24. PYTHON PROGRAMMING Books ➢ Python Crash Courses ➢ Python for Data Analysis ➢ Feature Engineering for Machine Learning ➢ Hands-on ML with Sci-Kit Learn & TensorFlow ➢ Python Machine Learning ➢ Apache Spark Deep Learning Cookbook About 30 GitHub repositories
- 27. Transition from Statistical Programmer to ML Engineer Statistical Programmer Data Scientist ML Engineer
- 28. Machine Learning Engineer / Professional Market • The global machine learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024, growing more than 40% annually. • The current average salary of ML Engineer is about 150K, and the highest paying companies offering more than $200K. • The demand has been increased a lot in recent years.
- 29. ML Implementation in Pharmaceutical Industry • Drug discovery • Drug candidate selection • Supply Chain optimization • Medical image recognition • Medical diagnosis • Optimum site selection or recruitment • Data anomality detection • Personized medicine • Medical coding • Sales and Marketing Optimization • Pharmacovigilance • Drug Development
- 30. Should I transition to Machine Learning Engineer? Should we learn or know about Machine Learning? What kind of impact can we have if we add ML knowledges in Biometric Department? 30
- 31. Any Questions?