Machine learning has moved from experimentation to everyday business use in credit scoring, fraud detection, demand forecasting, customer churn prediction, document processing and, increasingly, generative AI assistants. Managing this work is different from managing conventional IT projects. Outcomes are uncertain, data quality determines success, models degrade as conditions change, and the consequences of unfair or unexplainable decisions fall on the organisation rather than the algorithm.

The Management of Data Science & Machine Learning in Practice Training Course prepares managers and technical leads to direct this work with confidence. The first five days establish the foundations: identifying and prioritising use cases, framing problems, assessing data readiness, structuring projects with CRISP-DM and agile methods, building balanced teams, and understanding enough of the modelling process to ask the right questions. Participants complete guided Python labs so that terms such as overfitting, cross-validation, precision and recall become concrete. The second five days go deeper into production: MLOps pipelines and tooling, deployment patterns, experimentation, monitoring and retraining, model validation, responsible AI, cost management on cloud platforms, and the particular demands of large language model projects. An integrated capstone brings these elements together.

Every session links technique to management decisions. Labs use realistic data sets from banking, telecoms and public services, and case discussions examine projects that succeeded, stalled or failed, so participants learn to recognise warning signs early.

Graduates of the programme will be able to:

• Identify, frame and prioritise machine learning use cases using value, feasibility and risk criteria.
• Plan and run data science projects using CRISP-DM combined with agile delivery practices.
• Assess data readiness, quality and governance requirements before modelling begins.
• Interpret model evaluation metrics and challenge results presented by technical teams.
• Design MLOps processes for versioning, deployment, monitoring and retraining.
• Apply model risk management, validation and responsible AI controls across the model lifecycle.
• Structure, staff and develop data science teams and their collaboration with business units.
• Track the business value, cost and performance of a portfolio of models.

It is built for people who sponsor, lead or oversee data science work:

• Data science and analytics managers
• Lead data scientists and machine learning engineers moving into management
• Heads of data, business intelligence and digital transformation
• Product owners and product managers for data-driven products
• IT and software delivery managers supporting ML deployment
• Model risk, validation and internal audit staff
• Business unit heads sponsoring analytics initiatives
• Research and M&E managers using predictive models
• Data governance and data protection officers

Participants leave with a practical toolkit that includes:

• A scored use-case portfolio with value, feasibility and risk ratings.
• A project charter and CRISP-DM delivery plan for a priority use case.
• A data readiness assessment checklist.
• A baseline model built and evaluated in a Python notebook during the labs.
• An MLOps blueprint showing tools, environments, roles and approval gates.
• A model risk and responsible AI control framework with a model card template.
• A team structure and skills plan for the data science function.
• A capstone management plan with KPIs for value tracking.

Management judgement is built through a blend of hands-on work and case analysis. The programme includes:

• Guided Python labs in Jupyter notebooks, designed for managers as well as practitioners
• MLflow and deployment walkthroughs showing how models move into production
• Case discussions of successful, stalled and failed ML initiatives
• Portfolio and prioritisation workshops using participants' own use cases
• Model review exercises in which participants challenge evaluation results
• A capstone management plan presented to a panel of peers and facilitators

The CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded on full attendance across the ten days, completion of the Python and MLOps labs, and presentation of the capstone portfolio plan.

The notebooks, charter and management plan created during the programme remain with each participant for continued use.