Live Online Planning and Implementation of Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Nov 2, 2026 · from USD 1,700
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.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Nov 2, 2026, Nov 30, 2026, Dec 28, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
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.
What you'll learn
Key Takeaways
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.
Training Methodology
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
Day 1: From Idea to Approved Project
- Why machine learning projects stall between proof of concept and production
- The data science lifecycle mapped against CRISP-DM and project management stages
- Organisational, data and technical readiness assessment
- Writing a problem statement and defining the decision a model will support
- Baselines, success measures and the cost of being wrong
- Stakeholder mapping and securing sponsorship
- Workshop: drafting a project charter for the course case
Day 2: Data Requirements and Access Planning
- Inventorying candidate data sources and their owners
- Data requirements specification: fields, granularity, history and refresh frequency
- Profiling sample data in Python with pandas
- Data quality thresholds and their effect on feasibility
- Data sharing agreements, privacy and lawful use in plain terms
- Labelling strategies and the cost of ground truth
- Lab: profiling a data extract and writing a data gap report
Day 3: Solution Options and Architecture Choices
- Build, buy or use cloud ML services such as Azure Machine Learning, Amazon SageMaker and Google Vertex AI
- Batch scoring, real-time APIs and embedded models compared
- Reference architecture for ingestion, training, serving and monitoring
- On-premises, cloud and hybrid options, including connectivity and data residency
- Open-source stack choices: Python, scikit-learn, MLflow, Airflow and PostgreSQL
- Total cost of ownership and recurring running costs
- Exercise: options appraisal for the course case
Day 4: Estimation, Scheduling and Delivery Risk
- Work breakdown for data, modelling, engineering and change activities
- Estimating uncertain work with ranges, spikes and time-boxed experiments
- Combining agile sprints with stage gates and governance checkpoints
- Resourcing plans and a RACI for data, IT and business teams
- Delivery risk register: data delays, model underperformance, integration and adoption risks
- Budgeting for infrastructure, licences and ongoing support
- Workshop: building a delivery schedule and risk register
Day 5: Pilot Design and a Baseline Model
- Designing a pilot that answers a business question as well as a technical one
- Train, validation and test splits and how to avoid leakage
- Building a baseline model with scikit-learn
- Matching evaluation metrics to business costs: precision, recall, AUC and forecast error
- Acceptance criteria and go or no-go decision rules
- Lab: baseline model and pilot evaluation report
- Review of the planning pack produced in the first week
Day 6: Reproducible Pipelines and Engineering Practice
- Moving from notebooks to structured Python projects
- Version control with Git and branching conventions for data science teams
- Pipelines with scikit-learn Pipeline and ColumnTransformer
- Experiment tracking and the model registry in MLflow
- Unit tests and data validation checks with pytest and Great Expectations
- Environment management and dependency pinning
- Lab: refactoring the pilot into a reproducible pipeline
Day 7: Deployment and Serving
- Packaging a model and its dependencies with Docker
- Serving predictions through a FastAPI endpoint
- Batch scoring jobs scheduled with Apache Airflow
- Authentication, logging and security for model services
- Latency, throughput and load considerations
- Continuous integration and delivery with GitHub Actions or GitLab CI
- Lab: deploying the course model as a container
Day 8: Integration, Testing and Rollout
- Feeding model outputs into core systems, dashboards and mobile workflows
- Integration testing, user acceptance testing and shadow-mode trials
- Staged release patterns: shadow, canary and A/B rollout
- Cutover planning, rollback triggers and contingency procedures
- Training end users and redesigning the business process around the model
- Communication plans for affected teams and customers
- Simulation: running a go-live readiness review
Day 9: Operating Live Models
- Monitoring data drift, prediction drift and business KPIs
- Alert thresholds and retraining schedules
- Runbooks, service levels and support handover to IT operations
- Handling incidents and rolling back a model in practice
- Tracking cloud compute and storage costs
- Operational documentation: model cards and change logs
- Lab: configuring drift monitoring with Evidently
Day 10: Integrated Implementation Project
- Assembling the end-to-end implementation plan for the course case
- Linking the charter, data plan, architecture, schedule and risk register
- Benefits realisation planning and post-implementation review
- Scaling from one model to a pipeline of projects
- Presentation to a mock steering committee
- Peer and facilitator feedback
- Personal action plan for the first ninety days back at work
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
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.
Choose Where You Want to Learn
Planning and Implementation of Data Science & Machine Learning in Practice Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Planning and Implementation of Data Science & Machine Learning in Practice Training Course
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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