A machine learning proof of concept can often be built in a few weeks, but turning it into a dependable service that staff use every day can take far longer when the work is poorly planned. Pilots stall because the production data feed was never designed, the receiving system cannot call the model, budgets ignored infrastructure and support costs, or the operations team first hears about the model on the day it goes live. Careful planning and disciplined implementation are where most of the value of data science is either secured or lost.

The Planning and Implementation of Data Science & Machine Learning in Practice Training Course follows a project through its whole life. The first five days deal with planning: assessing organisational and data readiness, defining scope and success measures, specifying data requirements, choosing between building, buying and cloud services, estimating effort, scheduling work and registering delivery risks. Participants also build a baseline model in Python so that their estimates rest on hands-on experience. The second five days concentrate on implementation, including production-grade pipelines, experiment tracking, containerised deployment, workflow orchestration, integration testing, staged rollout, monitoring, handover to support teams and post-implementation review.

Teaching alternates between planning workshops and coding labs. Participants carry a single case through all ten days, either a provided scenario from banking, telecoms or public health or a project from their own workplace, and finish with a costed implementation plan, a working deployed model and a go-live checklist they can reuse.

On completing the programme, participants will be able to:

• Assess organisational, data and technical readiness before committing resources to a machine learning initiative.
• Translate a business problem into a scoped project with measurable success criteria and a clear baseline.
• Specify data requirements, sources, access arrangements and quality thresholds for both training and production.
• Estimate effort, cost and timelines, and choose between building in-house, buying a product and using cloud ML services.
• Design a pilot with acceptance criteria that supports a confident go or no-go decision.
• Implement reproducible pipelines in Python with version control, experiment tracking and automated tests.
• Deploy a model as a containerised service and integrate it with the systems and processes that will use it.
• Plan cutover, rollback, monitoring and handover so that a live model stays supported after launch.

Anyone responsible for turning approved analytics ideas into running services will benefit, in particular:

• Data science leads and senior data scientists
• Project, programme and PMO managers handling analytics portfolios
• Machine learning engineers and analytics engineers
• IT delivery, solutions architecture and enterprise integration staff
• Business analysts and product owners writing requirements for AI features
• Heads of data and digital in banks, insurers, telecoms and utilities
• Monitoring and information officers in health, government and development organisations
• Consultants who implement analytics solutions for clients

By the final day, each participant holds:

• A readiness assessment and project charter for a real or provided machine learning initiative.
• A data requirements specification covering sources, owners, refresh frequency and quality rules.
• An effort and cost estimate with a build, buy or cloud recommendation and the reasoning behind it.
• A pilot design with a baseline, success measures and go or no-go criteria.
• A version-controlled Python pipeline with experiments tracked in MLflow.
• A model packaged in Docker and served through a FastAPI endpoint.
• A go-live checklist, rollback plan and operations runbook ready to adapt.
• A post-implementation review template for measuring benefits after launch.

Planning workshops and coding labs alternate throughout, drawing on:

• A single case carried from charter to go-live over ten days, taken from banking, telecoms or public health or from the participant's own organisation
• Planning templates completed in class, including the charter, data requirements specification, options appraisal and risk register
• Guided Python labs in Jupyter, with Git, MLflow, Docker and FastAPI on a prepared environment
• Go-live simulations in which teams defend a readiness review before a mock steering committee
• Short briefings on what separated stalled projects from successful ones
• Facilitator clinics for participants who bring their own implementation challenges

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 planning workshops and implementation labs.

The implementation plan, deployed model and go-live checklist built during the course remain with each participant and serve as evidence of applied learning for professional development records.