Models that reach production and stay healthy
For data and engineering leaders whose models stall before launch or break after it. We build the data engineering underneath and the MLOps path on top, on the cloud you already use.
Maatara Consulting is an AI-native technology consultancy, with teams in Concord, California and Bengaluru, India, that designs, builds, secures and runs cloud and AI systems. Our MLOps services and data engineering turn notebooks and fragile scripts into tested, monitored pipelines.
- Assessment
- Architecture
- Pipelines and quality
- Model path to production
- Monitoring
- Scale and hand over
How We Approach MLOps Services
MLOps services cover everything between a working model and a model that keeps working: data pipelines, training, deployment, monitoring and retraining. We treat data, models and prompts as versioned code, so every change is tested, reviewable and reversible.
Data first
Most model failures start with data. We fix ingestion, quality and ownership before tuning models.
- Quality tests on every pipeline run
- A named owner for every dataset
Everything as code
Pipelines, infrastructure, model configs and prompts live in Git and deploy through CI/CD.
- Reproducible training runs
- Rollback to any previous model
Right-sized tooling
We use what your cloud already provides and add open-source tools only where they close a real gap.
- No platform you cannot run
- Managed services where they save effort
Monitored in production
Freshness, drift, accuracy and cost are tracked after launch, with alerts tied to owners.
- Drift and freshness alerts
- Cost per pipeline and per model visible
Four Foundations of Data Engineering and MLOps
We build all four, sized to how many models and pipelines you run.
Data pipelines
Batch and streaming ingestion from apps, SaaS tools and devices into a warehouse or lakehouse, built as tested code.
Data quality and contracts
Schema and value tests, freshness checks and agreed contracts between data producers and consumers.
Model lifecycle
Experiment tracking, model registry, automated training and deployment to batch or real-time endpoints.
LLMOps
Versioned prompts, retrieval indexes, evaluation suites and token-cost tracking for generative AI features.
Our MLOps Services and Data Engineering Capabilities
Start with the gap that hurts most; each piece fits the same foundation.
Data platform design
Warehouse or lakehouse architecture on AWS, Azure or GCP, sized to your volumes.
Pipeline development
Ingestion, transformation and orchestration with tests and lineage.
Data quality engineering
Automated tests, contracts and alerts that stop bad data at the source.
Feature engineering
Reusable, versioned features shared between training and serving.
Model deployment
Packaging and serving models as APIs or batch jobs with CI/CD.
Model monitoring
Drift, accuracy, latency and data freshness dashboards with alerts.
Automated retraining
Scheduled or triggered retraining with evaluation gates before promotion.
Search and retrieval
Indexes and vector search that power retrieval for AI features.
Data and ML cost control
Storage, compute and token spend tracked per pipeline and model.
Why Teams Choose Maatara for MLOps Services
Our engineers have built data products that run at real scale: MoreSpace indexes and moves around 5 PB of files, and SecBoard normalises findings from about 25 security tools into one store. Our CTO, Krish B, led engineering at Dell Technologies and holds 30+ patents in data protection, storage efficiency and distributed systems, and our Chief AI Advisor, Rudrendu Paul, contributes to open-source ML projects including scikit-learn and Ray.
Meet the teamModels stuck in notebooks?
Find out what it takes to put them into production safely.
What Teams Achieve
These are the commitments every MLOps services engagement works to.
Pipelines tested for schema, freshness and values before data is used
Versioned, reproducible and reversible from the registry
On drift, freshness and cost, routed to a named owner
Run-cost tracked from the first deployment
Typical Deliverables
Everything our MLOps services team builds lives in your cloud and repositories.
Data platform as code
Warehouse or lakehouse, storage and access defined in infrastructure as code.
Tested pipelines
Ingestion and transformation jobs with quality tests and lineage.
Model registry and CI/CD
Training, evaluation and deployment pipelines with approval gates.
Monitoring dashboards
Freshness, drift, accuracy, latency and cost per model.
Data contracts
Agreed schemas and expectations between producing and consuming teams.
Runbooks
How to retrain, roll back and respond to data incidents.
Dashboards that disagree?
Get data quality tests and owners on the pipelines that matter.
Data and ML at Scale
Data-heavy products our engineers have built and run.
MoreSpace
Build · AI - An index-first pipeline that deduplicates files and moves them between a person's cloud storage accounts, built for around 5 PB of private files with authentication and encryption in transit on every transfer.
SecBoard
Security · AI - A vulnerability command centre that pulls findings from about 25 security tools across eight categories into one store, scores risk by reachability, asset criticality and active exploitation, and generates compliance evidence from the same data.
RunQuickSurvey
AI · Build - One survey published to WhatsApp, Telegram, email, web and kiosk, with offline collection, conflict-safe sync and a model that surfaces themes from open-text answers while interviews are still running.
Data and MLOps Across How You Go to Market
What MLOps services need to protect depends on your customers.
B2B & Enterprise
Reliable data for customer-facing analytics and AI features, with lineage and access controls your customers' security teams will ask about.
Retraining done by hand?
Automate it with evaluation gates before every promotion.
Our MLOps Services Process
Six steps from today's pipelines to a monitored, repeatable model lifecycle.
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01
Assessment
Review data sources, pipelines, models and pain points; agree priorities.
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02
Architecture
Design the data platform and model lifecycle on your cloud.
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03
Pipelines and quality
Build or rebuild priority pipelines with tests and owners.
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04
Model path to production
Registry, CI/CD and serving for the first model.
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05
Monitoring
Drift, freshness, accuracy and cost dashboards with alerts.
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06
Scale and hand over
Extend to more models and pipelines; train your team.
Our Engagement Model
How an MLOps services engagement runs commercially.
Analysis
Free consultation and NDA, then a review of your current data and model landscape.
Design
A proposal with scope, milestones and a fixed price for the first phase.
Development
Pipelines and model infrastructure built with weekly demos.
Optimization
Performance, quality and cost tuned against agreed targets.
Deployment
Production launch with monitoring, alerts and runbooks.
Support
Monthly managed support for pipelines and models, or full handover.
Data and ML costs climbing?
See spend per pipeline and per model.
Flexible Engagement Models
Three ways to buy MLOps services and data engineering.
Dedicated team
A dedicated data and ML engineering team working on your backlog, billed monthly.
Hire a teamFixed scope
A data platform foundation or a first model in production, at a fixed, milestone-based price.
Get a quoteAdvisory & hourly
Senior review of your data architecture, MLOps tooling or platform choices, by the hour.
Book advisoryReady for dependable data and models?
Start with a free MLOps services review.
Are MLOps Services a Fit for You?
A good fit if
- You have models or AI features that are hard to deploy or keep accurate.
- Data pipelines break, run late or disagree with each other.
- You want to use your existing cloud rather than a new platform.
- You need someone to run pipelines and models after launch.
The wrong start if
- You have no data sources or models yet - start with AI strategy.
- You want a one-off report rather than running pipelines.
- There is no one to own the data once it is built.
MLOps Services FAQs
MLOps services cover the practices and tooling that take a machine learning model from development to reliable production: data pipelines, experiment tracking, a model registry, automated deployment, monitoring and retraining. At Maatara they also include the data engineering underneath, because most model problems start with data quality.
Still have a question?
Ask an engineer, not a call centre. You get a straight answer within one business day.
Ask our teamMLOps services cover the practices and tooling that take a machine learning model from development to reliable production: data pipelines, experiment tracking, a model registry, automated deployment, monitoring and retraining. At Maatara they also include the data engineering underneath, because most model problems start with data quality.
DevOps automates how code is built, tested and released. MLOps adds what machine learning needs on top: versioned data and models, reproducible training, evaluation gates before promotion, and monitoring for drift, because a model can degrade even when its code has not changed.
Usually not at first. AWS, Azure and Google Cloud already provide most of what you need, and open-source tools like MLflow fill gaps. We recommend a dedicated platform only when the number of models and teams makes the extra cost worthwhile.
Yes. For generative AI we version prompts and retrieval indexes, run evaluation suites before release, and track token cost and latency per feature. This sits alongside our AI Harness & Governance practice, which adds runtime guardrails, human review and the audit evidence regulators and customers ask for.
Yes. Most engagements pair our engineers with your data scientists and analysts. We build the shared foundations, document them and hand them over, so your team can add pipelines and models without us. We can also stay on for monthly support while your team grows into the work.
As a dedicated team billed monthly, a fixed-scope project priced by milestone, or advisory by the hour. After a free consultation we send a fixed-price proposal for the first phase, with no hidden costs and an estimate of monthly cloud spend.
Ready for Data and Models You Can Rely On?
Tell us which pipelines or models cause the most trouble. An engineer replies within one business day with a first view of the fix. Every MLOps services engagement starts with a free consultation.