Machine learning initiatives rarely fail at the modelling stage. They fail earlier, when scope is vague, data access is assumed rather than secured and nobody has agreed what a successful pilot looks like, or later, when a working model meets legacy systems, untrained users and no support arrangements. Ten days give you a disciplined method for both halves of the job. You learn to run readiness assessments, write problem statements and data requirements specifications, estimate effort and cost, build a delivery plan with milestones and acceptance criteria, and design pilots that produce a clear go or no-go decision.
Implementation sessions are hands-on in Python, pandas and scikit-learn, then move to reproducible pipelines with Git and MLflow, packaging with Docker, serving through FastAPI and scheduling with Apache Airflow. Later days cover system integration, user acceptance testing, cutover and rollback planning, runbooks and handover to operations. Data science leads, project managers, analytics engineers, IT delivery managers and business analysts in banking, telecoms, health and government will benefit. Afterwards you can take a model from approved idea to supported production service. Classroom, online and in-house delivery are available, with a CPD-accredited certificate.
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:
By the final day, each participant holds:
Planning workshops and coding labs alternate throughout, drawing on:
Day 1: From Idea to Approved Project
Day 2: Data Requirements and Access Planning
Day 3: Solution Options and Architecture Choices
Day 4: Estimation, Scheduling and Delivery Risk
Day 5: Pilot Design and a Baseline Model
Day 6: Reproducible Pipelines and Engineering Practice
Day 7: Deployment and Serving
Day 8: Integration, Testing and Rollout
Day 9: Operating Live Models
Day 10: Integrated Implementation Project
Anyone responsible for turning approved analytics ideas into running services will benefit, in particular:
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.
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 Planning and Implementation of Data Science & Machine Learning in Practice programme, you'll 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, and estimate effort, cost and timelines, and choose between building in-house, buying a product and using cloud ml services. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Consultants who implement analytics solutions for clients.
Planning and Implementation of 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.
Planning and Implementation of 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 Planning and Implementation of Data Science & Machine Learning in Practice starts November 2, 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 Planning and Implementation of 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.
Planning and Implementation of 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 Planning and Implementation of 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 — Planning and Implementation of 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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