Experience leading the machine learning competency, which involves scoping machine learning problems and driving project adoption, deploying and managing fraud detection and NLP models in our production machine learning infrastructure on AWS, and building a new infrastructure for production machine learning using Spark and MLflow.

Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.

Stephanie Mak is a highly experienced data and machine learning engineer with a strong track record of delivering data/ML projects, and enabling data-driven decision-making that aligns with business goals. She has also attained the DataBricks Certified Machine Learning Associate and DataBricks Certified Data Engineer Associate Certificates. Her latest work EnergyAustralia includes the design and delivery of DataBricks multi-workspace solutions for enterprise data lakehouse, data science laboratory, and ML engineering.

Pete Melgren is a data scientist with seven years of experience working on end-to-end predictive analytics projects. He has consulted with cross-industry clients on the use of data science concepts such as machine learning, data mining, business intelligence, and predictive analytics to meet their business needs. As a former data scientist for the Cincinnati Reds, he performed in-depth research of advanced baseball physics measures using statistical techniques that include linear regression, linear classification, mixed models, GAM models, and machine learning methods such as XGBoost. Pete also has advanced knowledge of SQL, R, Python, and AWS.

Anni Huang specialises in artificial intelligence with a strong focus on algorithms, machine learning, and deep learning applied to real-world industrial challenges. Her work spans end-to-end data science pipelines, model development, and deployment, with particular emphasis on MLOps practices. She builds scalable workflows on Google Cloud Platform and is advancing her MLOps capabilities on AWS, covering model versioning, CI/CD, monitoring, and reproducibility. She is proficient in software development, data visualisation using R and Tableau, and translating complex models into production-ready solutions.

Nicholas Lu is a goal-oriented engineer focused on ML systems and distributed architecture. He works across the MLOps lifecycle, building and maintaining data and model pipelines using Airflow, Spark, Kafka, and Kubernetes. His experience includes deploying containerised workloads with Docker, managing infrastructure via Terraform, and implementing CI/CD with Jenkins, GitLab, and Bitbucket Pipelines. He supports site reliability through monitoring, automation, and incident response in cloud environments (AWS, GCP, Azure, Alibaba Cloud). He codes in Python, Java, SQL, and Bash, and works extensively with Linux and open-source tools.

As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.