MLOps Services That Keep ML Models Accurate
Acquaint Softtech provides MLOps Services (Machine Learning Operations) to automate the machine learning lifecycle, from deployment to monitoring and retraining, using SageMaker, Vertex AI, Azure ML, or MLflow. Drift alerts and one-click rollbacks help maintain model performance. Our 13+ years of engineering experience and 70+ in-house engineers support reliable ML operations.
- Any platform: SageMaker, Vertex AI, Azure ML or MLflow.
- Safe releases: drift alerts and one-click rollback.
- Clear pricing: from $30/hour, dedicated $4,400/month.
- Proven expertise: 4.8/5 Clutch, ISO 27001, 70+ engineers.
What Are MLOps Services for Your Business?
How MLOps works for models in production.
MLOps services automate and manage the full lifecycle of a machine learning model: data, training, validation, registry, deployment, monitoring and retraining. Every model gets a version, a home and a health check, so nobody wonders which one is live or whether it still works.
Is MLOps better than DevOps? Neither; MLOps extends DevOps, shipping code, data and models and checking that models stay accurate. Does MLOps need coding? Yes, mostly Python, SQL and infrastructure as code, which our MLOps engineers handle for you.
Signs your models are ready for MLOps.
MLOps earns its place when models are trained in notebooks and copied to servers by hand, when nobody knows which model version is live, or when accuracy quietly slips. It also fits when releases need manual steps, when cloud costs are unclear, or when auditors ask which model made a decision.
Why Invest in MLOps Services Now?
A machine learning model that is not monitored slowly loses accuracy and trust. Our MLOps services keep production models versioned, measured and easy to improve.
Models that stay accurate
Drift detected and retraining started before users notice.
Releases without fear
Validation gates and one-click rollback on every model.
Faster time to production
Automated pipelines replace manual copy-and-deploy steps.
Every prediction traceable
Lineage from model to data, code and approval.
Costs you can see
Compute and serving spend tracked per model and team.
Less firefighting
Alerts reach the right person with context to act.
Which MLOps Approach Fits Your Models First?
The right MLOps platform depends on your cloud, your data and how many models you run. As an MLOps company, we recommend one MLOps approach in writing before any build starts.
Managed Cloud MLOps
Open-Source MLOps Stack
MLOps for Existing Models
LLMOps for Generative AI
Six Problems Our MLOps Services Solve
Each problem below comes from real machine learning work in production, and names who it was built for where we can. They show where MLOps services move the needle for live models.
Valuations rebuilt by hand every time
The ML fix: price prediction models fed by one ingestion pipeline.
Static alerts bury real risk
The ML fix: behavior-based scoring on transaction stream pipelines.
Nobody knows which model is live
The ML fix: a model registry with versions and lineage.
Accuracy slips without warning
The ML fix: drift alerts that trigger retraining.
Every release is a risk
The ML fix: CI/CD with validation gates and rollback.
LLM quality and cost drift
The ML fix: LLMOps with evaluation, tracing and spend alerts.
Manual ML Deployment vs Automated MLOps
A model deployed by hand and a model run with automated MLOps can make the same predictions on day one. The difference shows a few months later, in accuracy, releases and audit trails.
Nine MLOps Services We Deliver
Each of our MLOps services has a defined scope and a clear deliverable. Hire our MLOps company for a single fix, such as model monitoring, or a complete machine learning operations platform.
MLOps Consulting and Audit
MLOps Architecture Design
MLOps Pipeline Automation
CI/CD for Machine Learning
Model Deployment and Serving
Model Monitoring and Drift Detection
SageMaker, Vertex AI and Azure ML
LLMOps
Managed MLOps Support
SageMaker, Vertex AI, Azure ML or Open-Source MLOps?
Each MLOps platform suits a different cloud and team. We compare them for your machine learning workloads before recommending one MLOps platform.
Our default: use the managed platform of the cloud you already run, and choose open source only when residency or lock-in rules require it.
Batch, Real-Time or Edge Model Serving?
How a model serves predictions shapes speed, cost and monitoring. Our MLOps services choose the model serving pattern during design, per model.
Everything Production MLOps Needs to Stay Trusted
MLOps is never a single script that deploys a model once. Every engagement from our MLOps company ships with the engineering that keeps models accurate and traceable.
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MLOps maturity assessment
A clear picture of where your models, data and pipelines stand today. Risks are ranked by impact.
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Versioned data and models
Every dataset, feature set and model version tracked. Any result can be reproduced.
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Automated MLOps pipelines
Training, validation and packaging run the same way every time. No manual steps remain.
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Validation gates
Accuracy, bias and performance checks a new model must pass. Nothing ships without them.
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Staged deployment and rollback
Canary or shadow releases for every new version. Rolling back takes one click.
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Model monitoring dashboards
Drift, accuracy, latency and cost in one place. Alerts reach the right person.
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Retraining triggers
Schedules and drift thresholds that start retraining automatically. Human approval is added where needed.
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Runbooks and handover
Documentation and knowledge transfer on your own MLOps workflow. The code is 100% yours.
Model Governance Built Into Every MLOps Platform
Once models make real decisions, leaders need to know which version made each prediction and who approved it. Our MLOps services build that model governance into the platform, not a spreadsheet.
ISO 27001 certified
Information security management across the company.
NDA before any discussion
Signed before models, data or pipelines are shared.
Model lineage
Every model traced to its data, code and parameters.
Approval workflows
Named people sign off before a model goes live.
Access control
Role-based permissions on data, experiments and endpoints.
Audit logs
Deployments, rollbacks and retraining runs recorded.
Privacy by design
Anonymization and data minimization for regulated data.
Cost monitoring
Compute and serving spend per model and team.
MLOps Services for the Industries We Serve
Each industry runs different models under different rules. Where our MLOps company has delivered for a named client, we mark it; elsewhere we list typical production ML use cases.
Real Estate and PropTech
Banking
FinTech and Payments
Healthcare
eCommerce and Retail
Manufacturing
Energy and Utilities
Logistics
SaaS and Software
MLOps Consulting Scoped With Leadership in the Room
MLOps touches production systems, data and cloud costs. Acquaint Softtech scopes every MLOps consulting engagement with leadership involved from the first call, so accountability for your models is clear.
Mukesh Ram
Founder and CEO, Acquaint Softtech
Our MLOps Workflow, Assessment to Managed Support
Our MLOps workflow keeps your existing models serving predictions throughout. Each step ends with something your team can review, and a focused MLOps setup typically lands within a few months.
Assess
Models, data, pipelines and risks ranked by impact.
Architect
MLOps architecture and platform choice agreed.
Automate
Pipelines, registry and CI/CD for ML in sprints.
Deploy
Staged rollout with validation gates and rollback.
Monitor
Drift, accuracy, latency and cost alerts switched on.
Operate and Hand Over
Runbooks and training, or managed MLOps support.
MLOps Case Studies from Live Models
One detailed production ML snapshot, followed by data, scoring and monitoring work for named clients. Each MLOps case study links to a published Acquaint Softtech case study.
Property Brokers: price prediction models built for an agency with 80+ locations.
"The technology felt practical rather than experimental, which was exactly what we needed."
Sales history and pricing sat across spreadsheets, agents gathered appraisal evidence by hand, and leadership compiled regional reports office by office.
Read the Property Brokers case study
A centralized data ingestion system, an analytics engine, property price prediction models, market dashboards and a secure API layer, rolled out first to a pilot group of agents.
Behavior-Based Transaction Scoring
Challenge: Ameriabank's static rule alerts were repetitive and lacked context.
Solution: Transaction stream pipelines, behavior-based scoring and compliance review dashboards.
Outcome: Alerts with behavioral context and faster investigations.
Production Monitoring for Peak Traffic
Challenge: Empik's legacy modules caused uneven load times and checkout friction at peak.
Solution: Refactored modules, controlled releases and ongoing API and page speed monitoring.
Outcome: A critical cart API failure fixed during a live sale.
Event-Driven Data and Dashboards
Challenge: Croisette ran listings, agents and leads across disconnected tools.
Solution: One data framework, event-driven workflows and managerial dashboards.
Outcome: Managers gained real-time visibility into pipeline activity.
What Clients Say About Our MLOps Work
Rated 4.8/5 across 79 verified Clutch reviews of our AI and data engineering. Below, clients answer the questions buyers ask before choosing an MLOps company.
CEOs ask: Will the models be trusted?
"They delivered analytics that were trusted, not just technically correct."
Data leaders ask: Will they explain the value?
"They didn't just meet deadlines; they showed true commitment to our success."
CTOs ask: Will they understand our models?
The client highlights the team's ability to understand the technology and context, with clear communication throughout.
Directors ask: Will we see results quickly?
The client saw immediate improvements in operational clarity, lead management and pipeline activity.
Why Choose Us as Your MLOps Company
As an MLOps company in India serving the US, UK, Europe and Australia, we pair offshore value with in-house accountability. The same team can build your models and run their MLOps.
Engineers who ship
13+ years running production software, not just notebooks.
Proof you can check
Named clients, case studies and verified Clutch reviews.
Platform-neutral
SageMaker, Vertex AI, Azure ML or open source.
Step by step
Live models never go offline during the change.
Stable in-house team
24+ months average tenure, so your builders stay.
Your code, your IP
Pipelines, runbooks and repositories in your name.
MLOps Tech Stack We Build With
No single MLOps tool covers the whole lifecycle, so we choose per layer. We always pick the smallest set of MLOps tools your own team can maintain.
Managed platforms
Experiments and registry
Orchestration
Data and features
Training
Serving
Monitoring
LLMOps
CI/CD
Infrastructure as code
Languages
Cloud
How Much Do MLOps Services Cost?
MLOps services start at $30 per hour part-time, and a dedicated MLOps engineer costs $4,400 per month. Fixed-scope MLOps projects start from $8,000, and the final cost depends on how many models and platforms are involved.
Part-Time MLOps Help
- Up to 4 hours a day
- Monthly billing
- Quick risk reduction
Fixed-Scope MLOps Project
- Pipelines, registry and CI/CD
- Monitoring and rollback
- Milestone payments
Dedicated MLOps Engineer
- Managed monitoring and retraining
- Starts within 48 hours
- One-week trial
What drives MLOps services cost
MLOps Services FAQs
Clear answers to the questions teams ask most before putting their models on MLOps.
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What are MLOps services?
MLOps services automate and manage the machine learning lifecycle: data, training, validation, registry, deployment, monitoring and retraining, so models stay accurate in production.
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Is MLOps better than DevOps?
Neither. MLOps extends DevOps: DevOps ships code, while MLOps ships code, data and models and keeps checking that models are still accurate.
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Does MLOps need coding?
Yes, mostly Python, SQL and infrastructure as code. Our MLOps engineers handle that work so your business team does not have to.
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How much do MLOps services cost?
MLOps help starts at $30 per hour part-time, fixed-scope MLOps projects start from $8,000 and a dedicated MLOps engineer costs $4,400 per month.
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Which MLOps platforms do you work with?
We work with Amazon SageMaker, Google Vertex AI, Azure ML and open-source stacks built on MLflow, Kubeflow and Airflow.
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Can you add MLOps to models already in production?
Yes. We add monitoring first, then versioning, retraining and automated deployment, without taking live models offline.
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How do you detect model drift?
We monitor input data and prediction quality against baselines, alert the right person when drift appears and trigger retraining with approval where needed.
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Who owns the MLOps pipelines and code?
You do. We sign an NDA before any discussion and hand over 100% of the pipelines, runbooks and documentation.
Related AI Development Services
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