MLOps: From Model to Production


Creating an ML model is just a starting point. To bring it into production, you need to solve various real-world issues, such as building a pipeline for continuous training, automated validation of the model, scalable serving infrastructure, and supporting multiple environments in increasingly common hybrid and multi-cloud setups. While many organisations have experimented with AI proofs of concept, there are still major blockers to operationalising its development. Tech teams must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. In this session, we will discuss the role of MLOps and how they can help machine learning models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.

Session Outline
1. ML project development life cycle.
2. What is MLOPS?
3. Feature engineering
4. Model Deployment
5. Model Monitoring

Background Knowledge
Basic knowledge of machine learning.


Yiliang is VP, Head of Data Science with Openspace Ventures, where he is helping OSV’s portfolio companies to be more successful in machine learning and data science operation. He is also teaching applied machine learning courses in NUS and SMU as adjunct faculty. Yiliang has 10+ years of experience in managing and developing end-to-end machine learning projects from ideation to production. He has broad knowledge in predictive modelling, machine learning, natural language processing (NLP) and computer vision (CV). He has solid background in fundamentals of computer science, rich hands-on experience in complete software product development, solid software engineering capabilities and deep understanding of big data system, architecture and optimization. He has extensive experience in driving effective digital transformation using AI/machine learning to derive business insights and make intelligent decisions with quantifiable business impact.

Prior to joining OSV, Yiliang was J/APAC Machine Learning Practice Lead with Google Cloud, where he led the ML practice group, oversaw machine learning pipelines and managed training/enablement programs/initiatives in the region. He worked with multinational industry leaders including Fast Retailing, Netmarble, AirAsia, AU Optronics and UOB on various machine learning projects. Yiliang also had extensive experience working in Singapore government as data scientist and tech lead, helping government agencies to solve machine learning and data related problems. Working as a senior data scientist and tech lead at Shopee, Yiliang gained practical understanding of how B2C/C2C ecommerce works in south-east Asia, the related challenges and how data and machine learning can be used to tackle these problems.

Yiliang has a Ph.D. in Computer Science from NUS and a B.Eng degree in Computer Engineering from NTU with 1st Class Honours.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google