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MLOps

Pharos Production provides end-to-end Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) services that take machine learning models from experimentation to reliable, cost-efficient production.

  • 90+ engineers
  • 18 industries
  • 13+ years in business
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Aligned with these frameworks. Audit reports and certifications available on request.

Reviewed and updated
Last reviewed April 29, 2026 by Dmytro Nasyrov, Founder and CTO. Content reflects Pharos Production delivery data as of the review date. Editorial policy.

What changed on this review: 2026-04-21 CORE-EEAT uplift: definition body enriched with audience callout and three authoritative inline citations (Google Cloud MLOps, NIST AI RMF, McKinsey State of AI 2025); authored_insights block added mirroring AI hub pattern; FAQ expanded from 5 to 9 items adding pricing tiers, cloud platform selection guidance, compliance governance (HIPAA/GDPR/SOC 2) and ROI measurement framework with inline citations. | 2026-04-20 visual review: registry topology diagram migrated to Pharos token cascade with four-tier version hierarchy (archived, rollback target, active, experimental), SLO burn chart received editorial polish including legend tint, threshold label halo, axis tabular-nums and severity-tinted alert glow, AI/ML tech-stack grid gained keyboard focus-visible ring, micro-typography rhythm on category titles and count chips plus block-level focus-within lift.

Dmytro Nasyrov - Founder and CTO of Pharos Production

Reviewed by Dmytro Nasyrov

Founder and CTO

23+ years in custom software development. Led 70+ projects across FinTech, healthcare, Web3 and enterprise. aligned with ISO 27001 team.

What is MLOps?

MLOps is the operational discipline that keeps machine-learning systems running correctly, cheaply and auditably after launch. It covers training pipelines, deployment, monitoring, drift detection, retraining triggers, model registries, rollback topology and incident response - not just the model itself. Google Cloud describes MLOps as the unified practice of ML system development plus ML system operations with three automation maturity levels. NIST AI RMF frames post-deployment monitoring and governance as core risk-management functions, not afterthoughts. McKinsey State of AI 2025 reports 78% of organizations using AI in at least one function - the majority of real ML failure modes now live in operations, not in the model itself. This page is for MLOps buyers, ML platform leads and CTOs at FinTech, healthcare, insurance and SaaS companies evaluating drift-aware, SLO-driven ML operations - with an honest view of when an MLOps investment pays back and when a lighter observability extension is the right call.
Authoritative citations 12 sources
  1. Stanford AI Index The Stanford AI Index tracks multi-year movement on ML benchmarks, training compute, responsible AI metrics and enterprise adoption across industries, making it the most cited yearly reference for grounding ML investment cases. aiindex.stanford.edu
  2. Papers With Code Papers With Code maintains live state-of-the-art leaderboards for ML tasks across image classification, object detection, NLP and tabular prediction, which we use to pick baselines before committing to a model family. paperswithcode.com
  3. arXiv, Chen and Guestrin 2016 The XGBoost paper by Chen and Guestrin remains the most cited gradient boosting reference and underpins tabular ML baselines we still ship in FinTech and logistics systems a decade after publication. arxiv.org
  4. arXiv, LightGBM Microsoft Research LightGBM introduced leaf-wise tree growth and histogram-based splits, giving lower latency and memory footprint than XGBoost on wide tabular data, which is why our fraud detection stack defaults to it. arxiv.org
  5. McKinsey State of AI McKinsey documents annual enterprise ML adoption across functions like marketing, service operations and supply chain, and consistently reports that scaled ML correlates with higher EBIT contribution versus pilot-only organizations. mckinsey.com
  6. Gartner AI Hype Cycle Gartner maps enterprise ML techniques across the hype cycle phases, flagging which capabilities are production-ready for mid-market adoption versus still speculative, which we cross-check before recommending a build path. gartner.com 2024
  7. IDC Worldwide AI Spending Guide IDC publishes the worldwide AI spending guide with multi-year forecasts by industry, use case and geography, which we reference when sizing three-year total cost of ownership for ML platform engagements. idc.com
  8. NIST AI Risk Management Framework The NIST AI RMF defines a govern, map, measure and manage lifecycle for AI systems that we apply to production ML including model cards, bias testing and incident response procedures for regulated deployments. nist.gov
  9. OWASP ML Security Top 10 OWASP maintains a ranked list of the top machine learning security risks including input manipulation, training data poisoning, model theft and adversarial attacks, which we use as a threat model checklist before exposing any ML endpoint. owasp.org
  10. O'Reilly AI Adoption in the Enterprise The O'Reilly AI adoption survey tracks ML maturity stages across enterprises, reporting on deployment percentages, skills gaps and the most common production blockers which consistently include data quality and monitoring rather than model choice. oreilly.com 2022
  11. Google Cloud MLOps Architecture Google Research published the canonical MLOps continuous delivery reference describing three maturity levels from manual to fully automated pipelines, which we use as the template for client MLOps roadmaps and capability gap assessments. cloud.google.com
  12. PyTorch Blog The PyTorch engineering blog tracks the 2.x production tooling surface including torch.compile, TorchServe updates and quantization workflows, which shape our default serving stack for sub-50ms p99 inference on GPU and CPU targets. pytorch.org
What we do not do
  • Novel research into new model architectures (not our lane)
  • Full data-platform replacements beyond what MLOps needs
  • Training models from scratch without an agreed objective metric
  • Compliance attestations that require a certified auditor
  • Engagements with no operational owner on the client side

MLOps at Pharos at a glance

  • Engagements: 20+ MLOps engagements since 2021 across FinTech, retail, insurance and SaaS
  • Tooling: MLflow, Weights & Biases, Airflow, Argo, Datadog; we use the client's existing stack when viable
  • Cloud: AWS SageMaker, GCP Vertex AI, Azure ML; multi-cloud when the business case supports it
  • Model volume: Engagements range from 2 to 120+ models per client
  • Pricing: Full MLOps buildout from $85,000 (8-14 weeks); ongoing operations from $9,500/month
  • On-call: We can staff on-call during stabilization phase; handed off to client team with playbook and runbook
  • Honest default: Drift monitor, SLO and rollback path are required in every engagement

Managed MLOps platform vs custom in-house stack: which is better?

Managed platforms (Vertex AI, SageMaker, Databricks) ship faster and require less specialized hiring[7]. Custom stacks layered on Datadog, Grafana and Airflow cost less at scale and stay flexible when the workload is unusual. The right choice depends on team size, compliance posture and how many models you are operating, not on what is trendy.

Factor Managed MLOps platform Custom stack on general observability
Time to first model in production 2-4 weeks on a tuned platform with existing cloud account 6-10 weeks to assemble feature store, registry, monitoring and orchestration
Hiring pool Standard cloud and ML engineers; platform docs carry the load Needs senior platform engineers comfortable stitching components
Cost at 2-5 models Platform subscription plus usage; often more expensive per model Lower fixed cost; pay for compute and storage only
Cost at 50+ models Scales linearly; platform fees can dominate Amortizes platform build across many models; usually cheaper
Flexibility for unusual workloads Constrained by platform primitives and supported model types Full control; can run any framework, any hardware target
Compliance posture Inherited platform controls (SOC 2, HIPAA eligibility, ISO 27001) Controls must be mapped and attested per-component
Drift monitoring depth Opinionated defaults; may not cover custom feature distributions Custom KL and PSI thresholds and business-KPI signals configurable
Exit cost and lock-in Non-trivial migration cost when switching platforms Components swap independently; lower lock-in risk

How we operate ML systems without 2am pages

Pharos Verified Delivery in MLOps means every model that ships has a production SLO, a drift monitor, a rollback path, a retraining trigger and a named on-call owner. No model ships without those five things.

Pharos Verified Delivery 4-phase methodology with typical durations and deliverables
  1. Phase 01 / 04

    Paid Discovery

    2-4 weeks
    • Technical validation
    • Architecture proposal
    • Scope refined estimate
    82% on-schedule with discovery
  2. Phase 02 / 04

    Iterative Build

    2-week sprints
    • Working demos every sprint
    • CTO review at milestones
    • ADRs documented
    Transparent progress tracking
  3. Phase 03 / 04

    Production Readiness

    • Monitoring and alerting
    • Security audit Pen test
    • Runbooks and rollback
    ISO 27001 aligned
  4. Phase 04 / 04

    Support

    Ongoing
    • Security patches
    • Performance tuning
    • 4h SLA response
    Continuous improvement

Pharos Verified Delivery applied to 70+ production applications since 2013

Operations work that stopped incidents

Anonymized before/after snapshots from production projects. Metrics measured against client-reported pre-engagement baselines.

Drift detection rollout (Q4 2024) Q4 2024 · FinTech, EU
Before

14 ML models in production with no drift monitoring; 3 silent accuracy regressions in the prior 6 months.

After

Instrumented feature and prediction drift monitors with PagerDuty tie-ins. 2 drift incidents caught and rolled back within a single business day[11] in the following quarter. Pattern mirrors our production ML work in Pro Gambling sports forecasting platform.

We did not rebuild the models. We built the nervous system around them so the team could hear when something went wrong.

Training cost reduction (Q2 2025) Q2 2025 · Retail, US
Before

$48,000/month cloud spend on model retraining with unclear value per run.

After

Built a retraining trigger tied to drift thresholds and business KPI movement. Training cost dropped to $9,800/month[10] with no accuracy regression measured over 6 months.

We stopped retraining on a schedule and started retraining on evidence. That single change did most of the work.

Deployment cadence (Q1 2025) Q1 2025 · SaaS, US
Before

Model deployments took 2-3 weeks from training to production due to manual steps.

After

Rebuilt deployment pipeline with staged rollout, canary analysis and automatic rollback. Deployment lead time dropped to 2 hours[12]. Similar deployment patterns apply to our work documented in Pharos Claude enterprise deployment.

MLOps is mostly plumbing done well. The plumbing is invisible when it works and extremely visible when it does not.

Client names anonymized under NDA. Full case studies at /cases/.

When you do not need a full MLOps investment

We decline roughly 30% of RFPs we receive. Forcing a bad fit costs both sides 3-6 months and damages outcomes. Here is how we think about scope:

When simpler is better
  • You run 1-2 models that change infrequently and do not touch revenue-critical paths
  • Your team is small and adding MLOps tooling would be more complex than the models themselves
  • You are still validating whether ML is the right answer (build the pilot first)
  • You already have an excellent general observability stack and just need a few extensions
  • Regulatory constraints require a different, specialized tooling stack
Lighter alternatives we recommend

For small ML footprints, a weekly eval job plus a drift dashboard on existing observability stack (Datadog, Grafana) gives most of the value at a fraction of the cost. MLOps investments should scale with model footprint, not with hype.

Pharos MLOps portfolio

Pharos MLOps delivery portfolio observations, 2021-2026

Ranges we consistently see across 20+ MLOps engagements delivered since 2021. Qualitative patterns from the delivery portfolio, not formal benchmarks. Individual engagement numbers vary with model footprint, cloud posture, compliance requirement and team maturity.

  • KL 0.2-0.5 Default drift alert threshold band

    Initial drift alert thresholds land in KL divergence 0.2 to 0.5 against the training distribution, calibrated per feature and per model[4]. Start at 0.5 on launch, tighten to 0.2-0.3 after 30 days of baseline data, and retune quarterly as feature distributions stabilize. Tighter than 0.2 generates on-call noise faster than signal.

  • 8-14 weeks Typical MLOps platform buildout duration

    Full MLOps platform buildout from discovery close to production handover runs 8-14 weeks depending on model footprint, cloud posture and whether the client has existing observability infrastructure we can extend[7]. Discovery adds 2-3 weeks. Extensions of existing stacks usually complete in the lower half of the band.

  • $9.5k-$25k/month Ongoing MLOps operations fee range

    Ongoing MLOps operations contracts run from $9,500 per month for clients with 2-10 models on a managed platform to $25,000 per month for clients with 50+ models on custom stacks requiring on-call rotation[5]. Every contract commits to a 4-hour SLA on critical incidents (model down, prediction errors or drift breach).

  • 3 signals Confirmed drift alert pattern

    We page on-call only when at least two of three drift signals cross threshold (feature distribution, prediction distribution or business KPI). Single-signal alerts generate noise, three-signal confirmation reduces false pages while catching real regressions. Revenue-critical models get a fourth signal: direct revenue-per-request tracking with a 15-minute rolling window.

  • 2-4 hours Target deployment lead time post-platform

    Once the MLOps platform is in place, target deployment lead time from training artifact to production lands at 2-4 hours including canary analysis and automatic rollback gates. Clients arriving with 2-3 week manual deployment pipelines typically compress to this band within 60 days of platform handover.

Observations reflect the Pharos Production MLOps delivery portfolio 2021-2026. Shared for discovery planning, not as fixed commitments. Full MLOps portfolio visible in our case studies library.

MLOps outlook 2026-2027

Three shifts we are already pricing into MLOps engagements for mid-market and enterprise clients.

  • Managed platforms consolidate, custom stacks lose ground below 20 models

    For clients with fewer than 20 production models, managed platforms (Vertex AI, SageMaker, Databricks) are outcompeting bespoke stacks on total cost of ownership when the three-year view includes hiring, on-call rotation and drift-tooling maintenance.[11] We still build custom when model count exceeds 50, when workloads require specialized hardware, or when compliance posture rules out a multi-tenant managed option.

  • LLMOps converges into MLOps stacks rather than remaining separate

    Prompt versioning, eval harnesses, cost monitoring and guardrails for LLM-powered features are increasingly merging into the same registry, drift monitoring and rollback infrastructure we use for traditional models.[10] Teams running both traditional ML and LLM features are consolidating on one operational plane to reduce on-call complexity, which reshapes how we scope cross-model platform work.

  • Observability vendors absorb MLOps-only tools

    Datadog, Grafana, New Relic and Splunk are shipping ML drift and model-performance modules that hit 70-80 percent of what a standalone MLOps observability tool delivers. For clients already standardized on one of these platforms, a thin integration layer often replaces a full MLOps monitoring tool and cuts platform cost by 30-50 percent.[6] We now start every discovery by checking the client existing observability spend before recommending a new MLOps tool.

Our four-dimension MLOps evaluation template

Every MLOps engagement we ship runs against the same four-dimension readiness evaluation before handover. Weights flex by workload, but the dimensions stay constant.

  1. 25%

    Drift detection coverage

    Three signals instrumented and alerting: feature distribution drift via KL divergence or population stability index, prediction distribution drift via expected vs observed class balance and score histograms, and business KPI drift via primary outcome metric compared to a 30-day rolling baseline[1]. Alert thresholds tied to measurable downstream business impact, not to statistical significance alone. False-positive rate kept under 2 alerts per model per month.

  2. 20%

    Retraining discipline

    Retraining triggers defined: threshold-based on drift beyond declared tolerance, KPI-based on business outcome degradation, or upstream-event-based on new data schema, merchandising refresh or regulatory change[5]. Scheduled retraining only allowed when upstream data has known periodicity. Every retraining run ships through canary analysis before promotion.

  3. 30%

    Serving latency and availability

    p50, p95 and p99 latency measured on realistic traffic shapes with quantization and batching enabled[2]. SLO declared during discovery with explicit error budget. Shadow mode evaluation runs against live traffic for at least three days before promotion. Graceful degradation path documented (stale prediction, fallback model or rule-based default) and tested in a game day.

  4. 25%

    Governance and audit

    Model registry with versioned artifacts, training data lineage, feature provenance, evaluation run history and owner attestation[9]. Rollback path validated before promotion. NIST AI RMF govern, map, measure and manage lifecycle applied end-to-end, with model cards for every production model. Incident disclosure procedure rehearsed at least once per quarter.

Eval template refreshes quarterly against the latest NIST AI RMF and Google Cloud MLOps publications. Owned jointly by the Pharos MLOps lead and the client ML platform owner.

Weights flex by workload. Payment and fraud models lift serving latency to 35 percent and drift detection to 30 percent because false positives hit checkout conversion and missed drift costs real money. Recommendation and ranking models lift drift detection to 30 percent because catalog and inventory changes are constant and silent. Forecasting and demand models lift retraining discipline to 30 percent because seasonality and supplier changes require disciplined trigger-based retraining. Same template, weighted to the workload.

  1. January 2026 Pass rate 84 percent across 12 MLOps handovers. The three engagements that failed all tripped the governance dimension: two had incomplete lineage records from the training phase and one lacked a documented on-call runbook. Remediation took 5-9 days per engagement.
  2. February 2026 Pass rate 79 percent across 14 handovers. A FinTech engagement failed serving latency after the first production traffic exposed p99 regressions not seen in shadow mode. We held promotion, rebuilt the serving layer on a quantized model and promoted 11 days later with p99 inside the SLO band.
MLOps reference diagrams

How MLOps operations look in practice

Three reference diagrams from our own runbook. They describe how a drift alert turns into action, how a model version travels from registry to user traffic, and how a 99.5% SLO translates into alert timing across a 30 day window.

Drift detection decision flow

From production batch to on-call page, in four gates.

Drift detection decision flow Decision flow chart. A production batch enters the left node. It flows to a drift-score node that runs Kolmogorov-Smirnov and Population Stability Index tests against a reference window. Based on the score: low drift continues monitoring, moderate drift triggers a retraining queue, high drift triggers automatic rollback plus an on-call page. Each branch is labeled with its threshold. Production inference batch Drift score KS + PSI vs reference window score < 0.10 0.10 - 0.25 score > 0.25 Continue monitoring log to dashboard, no action Queue retraining next scheduled CI window Rollback + page on-call previous model within 5 min Thresholds tuned per model class - KS statistic for continuous features, PSI for categorical. Retraining queue fires at 0.10 to give the batch pipeline a window before accuracy degrades visibly.
Each production inference batch is compared to a reference window. When the drift score crosses a gate, the next action is determined by severity, not by engineer attention.

Model registry and rollback topology

Four stages between a trained model and user traffic.

Model registry and rollback topology Layered topology diagram. Left column shows a model registry with four versions (v1.0 through v1.3) tagged by status. Middle column shows three deployment lanes: staging, shadow traffic, production. Arrows flow from registry to staging to shadow to production. A red rollback arrow loops from production back to any registry version, bypassing shadow. Each lane has a small annotation of its traffic share. Model registry MLflow / S3 artifacts v1.0 archived v1.1 rollback target v1.2 (prod) active - 100% traffic v1.3 (shadow) candidate - 0% traffic Staging smoke tests + evaluation 0% user traffic Shadow live traffic mirror compare predictions, no user impact Production user-facing traffic 100% on active version Rollback path within 5 min, no redeploy Promote arrows (solid blue): normal forward path. Rollback arrow (dashed red): emergency reversion to any registry version. Shadow runs continuously against production traffic.
Every deployed model version remains callable for at least 30 days after retirement, so rollback never requires a redeploy. Shadow traffic validates new versions against live load before promotion.

SLO error budget burn, 30 day window

How the 0.5% error budget translates into alert timing.

SLO error budget burn, 30 day window Line chart. X axis: 30 days. Y axis: error budget consumed, 0 to 100 percent. A budget line climbs linearly from 0 to 100 over 30 days (ideal burn). The actual burn curve starts flat for 12 days, then accelerates due to a drift-induced error spike, crossing the 50% warning threshold at day 18 and the 90% critical threshold at day 26. Warning and critical horizontal threshold lines are shown. A vertical dashed marker highlights the incident point on day 14. 50% warning 90% critical 0% 25% 50% 75% 100% day 0 day 10 day 20 day 30 ideal burn - 3.33% per day drift incident, day 14 warning fires day 18 - 12 days remaining critical fires day 26 - 4 days remaining ideal burn actual burn after drift 99.5% SLO = 3.6h budget per 30 days
A 99.5% availability SLO gives you 3.6 hours of budget per 30 day window. The warning alert fires when you have burned 50% with more than 15 days remaining - enough lead time to act before on-call is paged.
MLOps operational cost estimator

Estimate the monthly cost of running ML in production

The numbers below are a rough baseline - your real cost depends on model architecture, traffic patterns, cloud region and how aggressive you are about retraining. Use this as a first pass, then ask your cloud provider for a detailed quote. Rates reflect AWS / Azure / GCP list prices as of March 2026.

How many distinct ML models serving user traffic.
Total predictions served per 24 hours.
How often you retrain to counter drift.
Each provider has different compute + storage rates.

Estimated monthly cost

Serving compute
$0
Drift monitoring
$0
Retraining compute
$0
Storage + registry
$0
On-call + ops engineer hours
$0
Total monthly TCO
$0

Baseline assumes managed-service list prices. On-prem path assumes a 1-engineer-FTE overhead per 10 models in production. Figures exclude data storage for raw training datasets and do not account for volume discounts or spot-instance savings. Talk to us for a concrete engagement quote.

Production post-mortem

What we caught when the drift alert stayed quiet

In March 2025 a retail client recommender model logged clean metrics for 11 days while quietly losing 9 percentage points of click-through rate. Our drift monitor was watching feature distributions and prediction distributions with a KL threshold of 0.5 and neither signal crossed. What shifted was the catalog: roughly 12 percent of SKUs were quietly deprecated by the merchandising team, so prediction scores on affected items stayed plausible but the downstream business outcome degraded. Pure feature-level drift would not see it. We only caught it because the weekly business-KPI eval job compared CTR against a 30-day rolling baseline and paged on-call at minus 4 percentage points.[11] Root cause: single-signal drift detection misses catalog-level changes that feel invisible to the model. We now require three-signal confirmation (feature plus prediction plus business KPI) before promoting a model, and a KPI alarm fires independently even when upstream signals are clean.

Three-signal drift requirement added to the MLOps readiness checklist. Business KPI alarm thresholds tightened from minus 5 pp to minus 2 pp on revenue-critical models. Three subsequent catalog-change incidents were caught within 72 hours of the upstream event.

Operations principle
Every MLOps engagement establishes five artefacts before we consider it production-ready: an SLO, a drift monitor, a rollback path, a retraining trigger, and a documented on-call owner. No exceptions. Last reviewed: June 2026. Editorial policy.
Important
MLOps reduces the risk of model failures, it does not eliminate them. We are transparent about residual risk during scoping and include incident playbooks in every engagement.

Published record

Published Pharos research

Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.

Platforms We Work With

Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms

16+ partners

Our 16 technology partners include:

  • Consensys
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About Founder and CTO

Dmytro Nasyrov

Dmytro Nasyrov

Founder and CTO Pharos Production

Ask the founder a question

I design and build reliable software solutions – from lightweight apps to high-load distributed systems and blockchain platforms.

PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.

  • 13 years in architecture of great software solutions tailored to customer needs for startups and enterprises

  • 23 years of practical enterprise customized software production experience

  • Lecturer at the National Kyiv Polytechnic University

  • Doctor of Philosophy in Artificial Intelligence

  • Master’s degree in Computer Science, completed with excellence

  • Master’s degree in Electronics and precision mechanics engineering

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$14,000 - $30,000
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Full model development, API layer, cloud deployment and MLOps with monitoring.

$40,000 - $90,000
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Enterprise AI platform

Multi-model architecture, custom data infrastructure, compliance and hybrid or on-prem delivery.

$85,000 - $200,000

Prices vary based on project scope, complexity, timeline and requirements. Contact us for a personalized estimate.

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187+ technologies

Technologies, tools and frameworks we use

Our engineers work with 187+ technologies across blockchain, backend, frontend, mobile and DevOps - chosen for production reliability and performance.

Frameworks

Backend Frameworks 8

Spring Boot
Spring Boot
Erlang OTP
Erlang OTP
NodeJS
NodeJS
Phoenix
Phoenix
NestJS
NestJS
Django
FastAPI
Express.js

Front End Frameworks 8

React
React
Next.JS
Next.JS
Svelte
Svelte
Angular
Angular
Vue.js
Remix
Astro
Nuxt.js

Mobile Apps Frameworks 10

iOS
iOS
Android
Android
Flutter
Flutter
React Native
React Native
Capacitors
Capacitors
Ionic
Ionic
Swift
Swift
Kotlin
Kotlin
Java
Java
Dart

AI and Machine Learning

LLM Providers 8

OpenAI GPT
Anthropic Claude
Google Gemini
Meta Llama
Mistral AI
Cohere
Ollama
xAI Grok

AI Frameworks 15

LangChain
LangGraph
CrewAI
AutoGen
Hugging Face
PyTorch
TensorFlow
scikit-learn
LlamaIndex
Keras
XGBoost
LightGBM
OpenCV
spaCy
ONNX Runtime

Vector Databases 7

Pinecone
Weaviate
Qdrant
Chroma
pgvector
Milvus
FAISS

MLOps and Infrastructure 11

MLflow
Weights & Biases
DVC
Kubeflow
AWS SageMaker
Azure ML
Google Vertex AI
NVIDIA Triton
Airflow
Ray Serve
vLLM

AI Agent Tools 4

OpenAI Agents SDK
Claude MCP
Semantic Kernel
Haystack

Blockchains

Private and Public Blockchains 33

Ethereum
Ethereum
TON
TON
Corda
Corda
Tron
Tron
Hedera
Hedera
Stellar
Stellar
Consensys GoQuorum
Consensys GoQuorum
Solana
Solana
Arbitrum
Arbitrum
Binance Smart Chain (BSC)
Binance Smart Chain (BSC)
Sei
Sei
Celo
Celo
Hyperledger
Hyperledger
MultiversX
MultiversX
IOTA
IOTA
Polkadot
Polkadot
Aptos
Aptos
Neo
Neo
Flow
Flow
Algorand
Algorand
Avalanche
Avalanche
EOS
EOS
Optimism
Optimism
Polygon
Polygon
Cosmos
Cosmos
Sui
Sui
Tezos
Tezos
Ontology
Ontology
Fantom
Fantom
NEAR Protocol
NEAR Protocol
VeChain
VeChain
Base
Base
IPFS
IPFS

Cloud Blockchain Solutions 4

Amazon Managed Blockchain
Amazon Managed Blockchain
Amazon QLDB
Amazon QLDB
IBM Blockchain
IBM Blockchain
Oracle Blockchain
Oracle Blockchain

DevOps

DevOps Tools 15

Kubernetes
Kubernetes
Terraform
Terraform
Docker
Docker
Istio
Istio
Prometheus
Prometheus
Grafana
Grafana
Jenkins
Jenkins
ArgoCD
ArgoCD
Ansible
Ansible
GitHub Actions
GitLab CI
Pulumi
Datadog
New Relic
Vault

Clouds

Clouds 6

Amazon Web Services
Amazon Web Services
Azure
Azure
Google Cloud
Google Cloud
Cloudflare
Vercel
DigitalOcean

Databases

Databases 15

PostgreSQL
PostgreSQL
MySQL MariaDB
MySQL MariaDB
Redis
Redis
Cassandra
Cassandra
Neo4J
Neo4J
MongoDB
MongoDB
Elasticsearch
Elasticsearch
Solr
Solr
Ignite
Ignite
ClickHouse
TimescaleDB
DynamoDB
Supabase
CockroachDB
ScyllaDB

Brokers

Event and Message Brokers 7

Kafka
Kafka
RabbitMQ
RabbitMQ
Flink
Flink
Apache Pulsar
Amazon SQS
Amazon SNS
NATS

Tests

Test Automation Tools 6

Postman
Postman
Appium
Appium
Cucumber
Cucumber
Selenium
Selenium
JMeter
JMeter
Cypress
Cypress

Programming

Programming Languages 18

Solidity
Solidity
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An approach to the development cycle

The Pharos Delivery Framework divides every project into 2-week sprints. After each sprint there is a retrospective of the work done, planning for the next sprint, a report of the work done and a plan for the next sprint. This methodology is why agile projects are 3x more likely to succeed than waterfall (Standish Group CHAOS Report, 2024).

  1. Team Assembly

    Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.

  2. MVP

    We’ll design, build, and launch your MVP, ensuring it meets the core requirements of your software solution.

  3. Production

    We’ll create a complete software solution that is custom-made to meet your exact specifications.

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    Continuous Support

    Our company will be right there with you, keeping your software solution running smoothly, fixing issues, and rolling out updates.

MLOps FAQ

Last updated: Reviewed by: Dmytro Nasyrov (Founder and CTO)

Quick answers to common questions about custom software development, pricing, process and technology.

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    Rarely. Most MLOps engagements layer operational discipline on top of what the client already runs.

    Full platform replacements happen only when the existing stack fundamentally cannot support the operational requirements.

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    We measure feature drift, prediction drift and business KPI drift, and we alert on the combination. Single-metric drift alarms create noise.

    Three-signal confirmation reduces false pages while catching real regressions.

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    Retraining is triggered by drift thresholds and measurable KPI movement, not by a schedule. Scheduled retraining burns money without evidence.

    Trigger-based retraining is cheaper and more accurate once set up correctly.

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    Yes. MLOps and data engineering overlap.

    We pair with the data team and stay in our lane around model deployment, monitoring and retraining. We do not rebuild data pipelines unless they are blocking.

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    We decline when there is no operational owner on the client side, when the client wants us to pick a tooling stack before we understand the constraints, or when the ML footprint is too small to justify an MLOps investment at all.

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    Typical Pharos MLOps engagements fall into three tiers. Stabilization sprints (6-10 weeks, $35k-$90k) stand up SLOs, drift monitoring and a rollback path for an existing ML stack. Platform builds (3-6 months, $120k-$320k) deliver a full MLOps platform: model registry, automated retraining, observability and governance. Ongoing operations (monthly retainer, $8k-$28k) cover on-call, incident response, drift alert triage and quarterly retraining discipline. These are realistic 2025 numbers for our engagement book. Gartner forecasts $644B in genAI spending in 2025 and MLOps is increasingly the line item that determines whether that spending survives audit.

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    The honest answer is: use what your data already lives in. Cross-cloud data egress, IAM surface area and team familiarity usually dominate the decision more than feature parity. We operate on all three: AWS SageMaker when the data estate is S3+Redshift+Glue and teams are AWS-native, GCP Vertex AI when BigQuery is the warehouse and the team wants the strongest serving-to-BigQuery pipeline, Azure ML when the enterprise already runs on M365+Azure AD and governance needs to live inside Purview. Google Cloud’s MLOps reference architecture and AWS SageMaker MLOps docs both converge on the same maturity ladder - the platform choice rarely changes the discipline.

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    We implement a four-layer governance model: model registry with approver workflow, audit logs on every inference request, PII redaction gates on training data and a retraining decision log that a compliance auditor can read. For regulated environments we align to NIST AI RMF with its Govern-Map-Measure-Manage loop and OWASP ML Security Top 10 for adversarial robustness. HIPAA engagements add PHI tokenization before training data leaves the clinical boundary. GDPR engagements add a data-subject-rights path that can traverse the feature store back to source records. SOC 2 engagements add access reviews and change-management evidence. We publish the playbook; we do not ship opaque governance theater.

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    We measure four metrics and publish them monthly. (1) Model incident rate - number of drift events caught before business impact vs caught after, trending to near-zero after-impact by month 6. (2) Retraining efficiency - cost per retraining cycle and time from trigger to production, typically dropping 60-80% after the pipeline is automated. (3) Inference cost per thousand predictions - frequently halved in the first quarter by right-sizing serving infrastructure and pruning unused models. (4) Audit readiness - mean time to answer a compliance question, targeting under 4 hours with a registry-backed audit log. O’Reilly’s Machine Learning Design Patterns formalizes these as the production-ML feedback loops that determine whether an ML program survives its second year.

The Pharos takeaway on MLOps

MLOps is the right investment when the model footprint is large enough that operational discipline cannot fit in one senior engineer head, when the business depends on models staying accurate in production, or when regulated workloads require verifiable audit trails. It is the wrong investment when the ML footprint is a single model that changes once a quarter, when no operational owner exists on the client side, or when the scope assumes a platform choice before discovery. We tell clients which case they are in during discovery, even when the answer is "your existing observability stack plus one eval job is enough".[8] Teams that operate ML with discipline measurably outperform teams that ship models and hope, which is exactly what the NIST AI RMF, Google Cloud MLOps reference and O Reilly adoption research converge on.

Book a 30-minute MLOps readiness call
Dmytro Nasyrov, Founder and CTO at Pharos Production
Dmytro Nasyrov Founder & CTO Let’s work together!

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