Data Platform and MLOps
The infrastructure that makes your data ready for reporting and models.
ALGOFACT pulls data scattered across business systems into a single data warehouse or lakehouse through reliable pipelines and makes it usable for reporting and machine learning. On the same foundation we add labeling, experiment tracking, a model registry, deployment and monitoring, so models become systems you operate rather than one-off projects. We set it up on AWS, Azure, GCP, Kubernetes or your own servers, with delivery from our engineering offices in Türkiye and a US entity in Delaware.
At a glance
Last updated:
- Scope
- Pipelines, storage, labeling, MLOps, BI
- Data store
- Warehouse, lakehouse or PostgreSQL
- Model lifecycle
- Experiments and registry in MLflow
- Deployment
- AWS, Azure, GCP, Kubernetes or on premises
- Data quality
- Automated tests at every pipeline step
- Governance
- Role-based access and a metrics glossary
When do you need a data platform?
If the same report means merging spreadsheets by hand every month, two departments quote different revenue figures or your data scientist spends most of the week collecting data, the problem is infrastructure, not the model. A data platform pulls data from ERP, CRM, production, e-commerce and field systems on a schedule, cleans it, stores it under one definition and serves both reports and models from the same source.
MLOps is the machine learning side of that infrastructure. Instead of training a model once and leaving it on a laptop, it records which data a model was trained on, which version is live and how its performance changes over time. If you have no model in production yet, the platform you build for reporting becomes the ground your models will run on later, so you do not build two stacks.
What the platform covers
Most projects start as a reporting problem and grow into a modeling need. The platform solves both on the same data.
- 01
Reporting from a single source
Management, sales and operations dashboards draw on the same defined metrics, so revenue, stock or active customer counts come from one query for everyone. - 02
Joining data across systems
Matches customer, product and order records from ERP, CRM, e-commerce and field apps and combines them into one view. - 03
Consistent features for models
Feature calculations are defined once and used the same way in training and live prediction, which removes a common reason models look good in training and break in production. - 04
Labeling operations
Workflow, labeler instructions, double review and inter-annotator agreement for image, text or record labeling. - 05
Transaction and behavior analytics
One of our published use cases: for Soldo we built a user transaction data analytics platform, and insights were drawn from that platform.
How the platform is built
- 01
Data inventory
We list source systems, tables, update frequency, data owners and fields that hold personal data. The business questions the platform answers first are prioritized here. - 02
Architecture decision
Based on data volume, real-time needs, your existing cloud contract and your team’s skills, we choose between a data warehouse, a lakehouse or a leaner PostgreSQL-based setup. - 03
Pipelines
Jobs that extract, transform and load data are written under version control, and row count, null and business rule checks run at every step. - 04
Metrics layer and reports
Definitions such as active customer, net sales or scrap rate are written in one place. Your BI tool connects to that layer, so every dashboard uses the same definition. - 05
MLOps layer
MLflow for experiment tracking and the model registry, an automated training and deployment pipeline, model serving and an approval step before anything reaches production. - 06
Monitoring and handover
Alerts for data freshness, pipeline failures, model drift and infrastructure cost, then handover to your team with runbooks.
What you receive
- 01
Architecture document
Data flow diagram, storage layers, access model and an estimate of infrastructure cost. - 02
Pipelines and quality tests
Transformation code under version control, data quality tests and scheduled jobs. - 03
Warehouse or lakehouse
A data store organized into raw, cleaned and business layers, with every table documented. - 04
BI dashboards and metrics glossary
Ready reports for the prioritized questions and a written definition of every metric. - 05
Model registry and deployment pipeline
The MLOps pipeline where models are versioned, approved and released, plus model monitoring dashboards.
Governance, security and monitoring
As a platform grows, the real risk is not a single data error but a loss of trust: once a dashboard shows a wrong number, people go back to spreadsheets. That is why every table has an owner, every metric has a written definition and every pipeline has quality tests. When a check fails, bad data is stopped before it reaches reports and the table owner is notified.
Access is role based. Columns with personal data are masked or opened only to authorized roles, and who queried what is logged. These measures help meet the technical obligations of the GDPR or Türkiye’s Personal Data Protection Law No. 6698; legal assessment stays with your counsel.
On the model side, every live prediction can be traced to the model version and data snapshot it came from. The model risk management and governance platform we built for Luwian (MVP) is the example of this approach on /work.
Questions
Approach01Should we choose a data warehouse or a lakehouse?
If most of your data is structured tables and the need is reporting, a warehouse is usually simpler and sufficient. If unstructured data such as images, logs or documents takes up a large share, or the same data feeds both reporting and model training, a lakehouse fits better. At small and medium volumes a PostgreSQL-based setup can be enough.
02Can we keep our current BI tool?
Yes. The platform does not require a new BI tool; your existing one connects to the curated data layer. We only recommend a new tool, with reasons, if the current one cannot meet the need.
03Do we need MLOps with only one model?
Even one live model needs version tracking, monitoring and retraining. With the first model we keep the scope small, starting with experiment tracking, a model registry and drift monitoring, and add automated training pipelines as the number of models grows.
04Who does the labeling?
Labels that require domain knowledge come from your experts; we set up the labeling tool, instructions and quality control. At high volume, part of the labeling is sped up with model suggestions and experts only correct them.
05What drives the timeline and cost?
The number of source systems and how easy they are to access, data quality, real-time data needs, the number of reports, the MLOps scope and cloud resource usage. After the data inventory we present a phased plan and an estimated infrastructure cost in writing.
Sources
- Personal Data Protection Law (Law No. 6698), Personal Data Protection Authority (KVKK)
Tell us about your project
Describe the problem briefly and we will reply with a time for a 30-minute discovery call.
Book a discovery call