Many organisations have proved that machine learning can work, but far fewer can deliver it reliably. Models take months to move from experiment to production, retraining depends on one person remembering the steps, scoring jobs fail silently when an upstream table changes, and cloud bills rise without anyone tracing them to a workload. These are process problems, and they respond to the same disciplines that improved manufacturing and software delivery: mapping the flow of work, removing waste, standardising, automating and measuring.

The Operational Excellence in Data Science & Machine Learning in Practice Training Course applies those disciplines across the model lifecycle. The first five days establish flow metrics, reproducible project standards, data quality controls, efficient experimentation and automated delivery through continuous integration and deployment. The second five go further, covering reliability targets and incident management for model services, a Lean Six Sigma lab on an underperforming pipeline, orchestration and cost efficiency at scale, maturity assessment and team operating models, and an integrated redesign project.

Teaching is hands-on throughout. Participants work in Python with scikit-learn, MLflow, Great Expectations, GitHub Actions, Docker and Airflow on a shared case pipeline drawn from credit scoring, demand forecasting and customer churn, measuring its performance before and after each improvement. Every participant also leaves with a current-state and future-state map of their own team's delivery process.

On completing the programme, participants will be able to:

• Map the end-to-end model delivery process and identify waste, bottlenecks and handoffs using value stream mapping.
• Define flow, quality and reliability metrics for a data science team, including lead time, change failure rate and service level objectives.
• Standardise project structure, environments and code review so that any team member can reproduce and maintain a model.
• Implement automated data validation and data contracts that stop poor-quality inputs before they reach training or scoring.
• Build a CI/CD pipeline that tests, packages and deploys models with minimal manual intervention.
• Operate production models with drift monitoring, alerting, runbooks and structured incident reviews.
• Apply DMAIC and statistical process control to diagnose and reduce recurring pipeline failures.
• Assess MLOps maturity and prioritise improvements that reduce cost and cycle time.

The programme is aimed at people responsible for how machine learning work gets done and kept running, such as:

• Data science team leads and analytics managers
• Machine learning engineers and MLOps engineers
• Data platform and data engineering leads who support model pipelines
• Senior data scientists moving into technical lead roles
• Site reliability and DevOps engineers taking on model services
• Lean Six Sigma and process improvement practitioners working with analytics teams
• IT operations and service managers in banks, insurers and telecom operators
• Heads of analytics in government agencies, utilities and development organisations

By the final day, each participant will have:

• A current-state and future-state value stream map of their own model delivery process.
• A team metrics specification covering lead time, deployment frequency, failure rates and model health.
• A reusable project template with environment files, test folders and a definition of done for models.
• A working data validation suite and a draft data contract for a key upstream source.
• A CI/CD workflow file that runs tests and builds a model container on every change.
• An incident runbook and post-incident review template for a production prediction service.
• A completed MLOps maturity assessment with a prioritised ninety-day improvement backlog.

Learning is organised around one case pipeline that participants measure, break and improve as the days progress:

• Short facilitator briefings on each Lean, DevOps or reliability practice, followed immediately by a lab
• Hands-on work in Python, MLflow, Great Expectations, GitHub Actions, Docker, Evidently and Airflow
• Before-and-after measurement of lead time, failure rate and cost so every improvement is evidenced
• Incident simulations in which teams respond to injected data and model failures
• Process improvement workshops using value stream maps, SIPOC, Pareto charts and control charts
• Peer review of each other's pipeline changes against agreed team standards

A CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded to participants who attend the full ten days and complete the daily labs, including the DMAIC exercise and the integrated pipeline redesign.

The certificate records the course title, dates and delivery format. The value stream maps, runbooks and improvement backlog produced during the programme make useful evidence of applied learning for performance reviews.