PROVEN IMPACT
Results clients can feel.
98%
CAST Score
40%
Average Productivity Gain
90%
Repeat Client Rate
TRUSTED BY ENTERPRISES RUNNING AI IN PRODUCTION
AI Lab
Most enterprise ML models never reach production. The ones that do, decay quietly. Apptware's MLOps services deliver automated training pipelines, model registries, drift monitoring, and continuous retraining — so your data science investment actually earns its ROI.
PROVEN IMPACT
98%
CAST Score
40%
Average Productivity Gain
90%
Repeat Client Rate
Our Services
From strategic consulting to production operations — Apptware covers every layer of your machine learning operations stack with senior ML engineers, not junior data scientists.
Maturity assessment across your data pipelines, model lifecycle, deployment flows, and governance. We deliver a prioritized MLOps roadmap scoped to your team size, budget, and compliance requirements — not a generic framework.
Custom-built MLOps platforms designed for your stack. Feature stores, model registries, training orchestration, and deployment pipelines — integrated with your existing data infrastructure on AWS, Azure, GCP, or on-premise GPU clusters.
Automated, reproducible training pipelines that take raw data to deployed models. Data validation, feature engineering, hyperparameter tuning, model evaluation, and promotion workflows — all version-controlled and CI/CD-integrated.
Production model health requires more than infrastructure metrics. We build drift detection, prediction quality monitoring, data distribution tracking, and bias auditing — with alert routing to your on-call and SRE teams.
Model cards, audit trails, data lineage, and reproducibility for every prediction. Required for GDPR, EU AI Act, SR 11-7, and HIPAA. We implement governance frameworks that satisfy your legal team without slowing down your ML engineers.
Let Apptware run your MLOps stack while your team focuses on model development. 24/7 on-call, incident response, quarterly platform upgrades, and SLA-backed uptime for your production ML infrastructure.
MLOps Maturity
Most enterprises self-assess as Level 2 and are actually at Level 0. Our MLOps maturity assessment benchmarks your current state across five dimensions: data pipelines, model development, deployment automation, monitoring, and governance.
The output is a concrete gap analysis and a roadmap to reach your target maturity level — typically in 2–4 months depending on starting point and scope.
Notebook-based. No version control for models or data. No automated training or deployment.
Code version-controlled. Scripted training pipelines exist. Deployment still largely manual.
CI/CD for training. Model registry. Deployment is semi-automated. Limited monitoring.
Full CI/CD for models. Feature store. Drift monitoring with alerts. Manual retraining triggers.
Fully automated retraining. Drift-triggered pipelines. Shadow deployments. Full governance.
Before vs After
Real metrics from enterprises before and after implementing Apptware MLOps services. Your starting point may differ — these are averages across 40+ production MLOps engagements.
Time from model PoC to production
Model deployment frequency
Time to detect model drift
Model retraining cadence
Reproducibility of predictions
Audit readiness (GDPR / EU AI Act)
Rollback time on bad deploy
3–9 months
1–2 per quarter
Weeks or never
Ad-hoc / manual
Partial / none
Not audit-ready
Hours to days
2–6 weeks
Multiple / week
< 15 minutes
Automated / drift-triggered
100% traceable
Fully audit-ready
< 60 seconds
Dimensions | | |
|---|---|---|
Time from model PoC to production | 3–9 months | 2–6 weeks |
Model deployment frequency | 1–2 per quarter | Multiple / week |
Time to detect model drift | Weeks or never | < 15 minutes |
Model retraining cadence | Ad-hoc / manual | Automated / drift-triggered |
Reproducibility of predictions | Partial / none | 100% traceable |
Audit readiness (GDPR / EU AI Act) | Not audit-ready | Fully audit-ready |
Rollback time on bad deploy | Hours to days | < 60 seconds |
Reference Architecture
MLOps is not "DevOps with ML models." It is a distinct discipline requiring six integrated layers. This is the architecture Apptware implements — adapted to your data stack, cloud, and compliance needs.
OUR PROCESS
A structured engagement model with clear deliverables at every phase. PoC at week two. Production-ready MLOps platform by week six. Continuous improvement thereafter.
We audit your current state across five dimensions: data infrastructure, model development workflows, deployment automation, monitoring, and governance. We interview your data science, engineering, and compliance stakeholders to understand constraints and objectives.
The output is a scored maturity benchmark (L0–L4), a prioritized gap list, and ROI projections for closing each gap.
DELIVERABLES
Our CV Stack
We work across the full MLOps landscape — open-source, commercial, and cloud-native. Tool selection is driven by your constraints, not our preferences.
Orchestration & Pipelines
Experiment Tracking & Registry
Feature Stores
Serving & Deployment
Monitoring & Observability
Cloud Platforms
Industries
MLOps done right is a regulatory superpower. Hover to explore how Apptware implements MLOps for industries where compliance and auditability are non-negotiable.
AI built for clinical environments, trained on the workflows, compliance frameworks, and medical terminology that actually
From fraud detection to underwriting, we build AI that holds up under regulatory scrutiny and still runs fast enough for real-time decisions.
AI built for the automotive industry, trained on driving workflows, safety protocols, and vehicle dynamics that actually improve performance.
AI built for the shop floor, predictive maintenance, quality control, and supply chain intelligence that actually keeps pace with production.
Crop monitoring, yield prediction, and resource optimisation: AI that works at the scale real agricultural operations demand.
Deliver personalized shopping experiences, optimize inventory, and automate operations with AI built for today's fast-moving retail landscape.
The Problem
Data science teams build brilliant models. Then they sit in Jupyter notebooks. Without MLOps, enterprise ML initiatives die between experimentation and production — and the ones that do ship degrade silently.
Models that work on a data scientist's laptop fail in production. Manual handoffs to engineering teams introduce errors, delays, and environment mismatches that block deployment for months.
Models in production degrade as data patterns shift. Without monitoring, drift detection, and automated retraining, accuracy quietly drops — and the business decisions built on those predictions silently go wrong.
Which model predicted this? On what data? When? — questions your compliance team will ask and you won't be able to answer. Without MLOps governance, you can't reproduce predictions, explain decisions, or pass a regulatory audit.
Why Apptware
We are an engineering team, not a staffing agency. Every MLOps engagement is led by senior ML infrastructure engineers with production experience — not junior data scientists learning on your dime.
01
Every engagement is led by engineers with 8+ years of production ML infrastructure experience. No resume-padding, no juniors billed at senior rates. You get the team that has done this before.
02
For regulated industries and sovereign data requirements, we build MLOps platforms that run entirely within your security perimeter — no cloud dependencies, no external data transfer, full operational autonomy.
03
Our delivery framework gets you from MLOps chaos to production-grade platform in six weeks — with a working PoC at week two to validate value before full investment.
04
We are not reselling a vendor platform. Our tool selections are opinionated but independent — driven by your team capability, budget, compliance needs, and lock-in tolerance.
Apptware Solutions' work helped the client improve their time-to-market. The team retained all core resources throughout the contract. Apptware Solutions assigned a project manager to oversee the tasks and timelines. The team was proactive in communicating and responding to the client.
Get Started
Stop evaluating AI in the abstract. We'll audit your data, workflows, and systems to show you exactly where custom AI can create real ROI, before you commit to anything. No pitch decks. No generic demos. Just a clear, honest picture of what's possible for your business.
No-cost AI audit
100% IP ownership
No commitment required
US & India presenceFAQ'S
Cloud ML platforms give you the primitives — compute, training jobs, basic deployment. They do not give you pipelines, governance, drift monitoring, or team workflows out of the box. Most enterprises using SageMaker or Vertex AI are at MLOps Level 1 — reproducible training but manual everything else. MLOps is the discipline of connecting those primitives into an automated, governed, observable system. We help you get there on your existing platform investment, not replace it.
Give us 45 minutes to understand your goals. We’ll recommend where AI can create measurable impact and share a practical roadmap with timelines, architecture, and delivery estimates within five business days.
USA
INDIA
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