• Building MLOps in Technology Financial Company• Building and maintaining MLOps infrastructure (Kubernetes cluster for serving API, training pipeline, and development) and platform (Airflow, Database, and repo management)• Responsible for developing and maintaining the product APIs such as Voucher and Product Recommendations, Fraud Detection and Scam Scoring, Credit Score, Face Comparison, etc. The scope of maintaining such as: - Designing the API system to meet the requirements of stakeholders. - Develop and maintain a fast and robust API for current and upcoming projects. - Testing the API case and performance for achieving the desired output and performance. - Build CI/CD for easy integration and deployment. - Managing the cluster infrastructure. - Create a monitoring dashboard. - Create alerting that integrates with the group channel. - Make sure the APIs are scalable and have 100% uptime.• Maintaining our scheduled Machine Learning training and prediction pipeline products (Recommendation, Fraud Detection, Scam Score, Intent Classification, etc) with/without distributed computing (Python Package/Pyspark) using Airflow and/or GitLab CI/CD.• Pair and collaborate with the Product and Project Management, Data Scientist, Data Engineer, Data Quality/Governance, Data Analyst, Site Reliability Engineer, Back End, and IT Security team to maintain and develop all the Machine Learning Engineer products.• Write technical documentation and report it to the team leader, and the VP of Data.

Conduct a mentoring session for 3 groups to discuss and give advice on some problems in machine learning development for their capstone project. The scope as an advisor is more or less the same as the previous batch.

Conduct a mentoring session for 4 groups to discuss and give advice on some problems in machine learning development for their capstone project including:• Underfitting/Overfitting Problem• Data distribution problem• Serving/deploying ML models• ML API• System architecture and its alternatives for serving ML models.

• Help the team to build a Machine Learning project especially NLP for text classification that acts as a virtual assistant to our doctors in the Alodokter application.• Develop and test python microservice applications that serve our machine learning model.• Able to maintain and continuously update and re-training our sub-intent-based text classification project that has up to a 95% precision score in our production.• Pair with the principal architect team to deploy and test our sub-intent-based classification for development, staging, and production server • Develop and experiment with different new and existing machine learning projects.• Monitor our services in Google Cloud Platform (Google Log Driver, Google Kubernetes Engine, Google Compute Engine, BigQuery, Data Studio, and Google Cloud Build)

Machine Learning with Tensorflow

- Introduction to Digital Transformation with Google Cloud- Innovating with Data and Google Cloud- Application and Infrastructure modernization with Google Cloud- Understanding Google Cloud Security and Operations

• Testing reliable AI/ML infrastructure in a cloud environment (GCP).• Reduced training and inference time with various ML models like Logistic Regression and K-Means Clustering for up to 80% faster.• Experimented with the latest state-of-the-art Machine Learning model in face detection in a containerized applications that resulting under 80 ms for inference.• Analyzed different AI-related research to be implemented in production.• Implemented different machine learning models using Google Cloud Platform

• Learn the key concepts and applications of AI to solve a wide range of ML problems.• Get essential hard & soft skills for successful careers in leading technology companies.• Experience to create a project capstone consisting of three main learning paths (Android, Cloud Computing, and Machine Learning).• Prepare for the TensorFlow Developer Certificate exams,