Data science teams often spend more time waiting on data, rerunning fragile notebooks and firefighting broken scoring jobs than building models. Months pass between a promising experiment and a model in daily use, and when production fails nobody is sure who owns the fix. Over ten days, you learn to run machine learning work as a measurable, repeatable operation. Topics include value stream mapping of the model lifecycle, flow metrics such as lead time and work in progress, standard project templates, data contracts and validation with Great Expectations, experiment tracking in MLflow, and CI/CD pipelines with automated tests and Docker.
The second week moves into service level objectives for prediction services, drift monitoring, incident runbooks and blameless post-incident reviews, plus a DMAIC lab using statistical process control. Orchestration with Airflow, feature stores, compute cost control and MLOps maturity follow, ending with a pipeline redesign project. It suits data science leads, ML and MLOps engineers, data platform engineers, analytics managers and process improvement specialists in banking, telecoms, insurance, utilities and government. Participants leave able to measure, standardise and continually improve how their team delivers models. Choose classroom, online or in-house delivery, with a CPD-accredited certificate.
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:
By the final day, each participant will have:
Learning is organised around one case pipeline that participants measure, break and improve as the days progress:
Day 1: Operational Excellence Principles for Machine Learning Work
Day 2: Standard Work and Reproducible Projects
Day 3: Data Quality Control and DataOps
Day 4: Efficient Experimentation and Model Development
Day 5: Automated Delivery with CI/CD for Machine Learning
Day 6: Reliability Engineering for Model Services
Day 7: Lean Six Sigma Lab on a Machine Learning Pipeline
Day 8: Scaling, Orchestration and Cost Efficiency
Day 9: MLOps Maturity, Governance and Team Operating Model
Day 10: Integrated Project on Redesigning a Model Delivery Pipeline
The programme is aimed at people responsible for how machine learning work gets done and kept running, such as:
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.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Operational Excellence in Data Science & Machine Learning in Practice programme, you'll 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, and implement automated data validation and data contracts that stop poor-quality inputs before they reach training or scoring. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Heads of analytics in government agencies, utilities and development organisations.
Operational Excellence in Data Science & Machine Learning in Practice Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Operational Excellence in Data Science & Machine Learning in Practice Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Operational Excellence in Data Science & Machine Learning in Practice starts October 26, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Operational Excellence in Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Operational Excellence in Data Science & Machine Learning in Practice Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Operational Excellence in Data Science & Machine Learning in Practice Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Operational Excellence in Data Science & Machine Learning in Practice Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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