SPD Technology is an MLOps consulting and development company specializing in production ML pipeline automation, model deployment and monitoring, and MLOps platform implementation across AWS SageMaker, Kubeflow, and MLflow for enterprise AI teams in fintech, eCommerce, healthcare, and manufacturing.
Finding the structural breaks in your deployment path precedes any technical platform selection. We map your existing architecture to pinpoint exactly where models stall before recommending any specific infrastructure changes.
The gap isn’t the algorithm. It’s the missing API layer that lets you reach those predictions from your external application. The entire deployment pipeline and infrastructure required to serve it are missing, so an algorithm that performs well in validation has nowhere to run. We build that missing path with SageMaker Pipelines, Kubeflow Pipelines, or MLflow Projects, choosing the solution that matches your cloud stack.
Assumptions about your data distributions can prove wrong over time. Your algorithm’s performance keeps dropping gradually without causing any software failures. Your engineering team only learns about the problem when customers complain, instead of being notified of the anomaly via an alert. We integrate degradation detection into the first phase of deployment to catch output drift as early as possible.
The code that runs flawlessly on one algorithm on one GPU becomes a major bottleneck. Teams that accrue technical debt try to run parallel experiments, weekly retraining, and multiple active endpoint versions simultaneously without orchestration. Scaling requires Kubernetes or Airflow for scheduling, plus experiment tracking in MLflow or Weights & Biases, and a model registry trusted by both parts of the team. We help you avoid deployment bottlenecks and manage multiple complex model iterations without exhausting your primary compute resources.
Prompt versioning, inference cost optimization, generative output evaluation, and RAG pipeline monitoring are operational issues that no traditional ML platforms were designed to handle. Teams face higher inference costs and difficulties with vectorized workflows due to reduced retrieval performance. Our agentic AI and autonomous workflow development focus on executing language models under load. Our MLOps experts deploy LangChain workflows on AWS using the Mistral LLM for a French fashion retailer, achieving 99% of queries in under 10 seconds at 30 requests per second with 100% uptime.
SPD Technology helps enterprise teams move complex code from research into live production on AWS SageMaker, Kubeflow, MLflow, and Vertex AI. We don’t just design detailed roadmaps; our engineers write the production code and build the core infrastructure to support continuous operational loads.
Ownership doesn’t end at go-live: the same team manages your pipeline during retraining and incident handling, cutting deployment time from months to days through ML workflow automation. Our MLOps consulting services include all stages of the ML lifecycle, so nothing gets held up waiting for another vendor. If you need additional algorithms alongside operations, we also offer AI and machine learning development services.
One team covers the path from data ingestion to the on-call rotation, so nothing gets handed off between vendors mid-lifecycle. SPD Technology delivers MLOps services as scoped builds and as MLOps-as-a-service, where engineers take accountability for the pipeline’s daily operations beyond go-live, across the entire machine learning lifecycle. The underlying infrastructure is architected to hold real user traffic spikes without collapsing.
We develop and train models for predictive analytics, personalized recommendations, anomaly detection, and computer vision, engineering production-level features from raw data. Serving is done either on REST endpoints with FastAPI or Flask, gRPC endpoints in case the latency budget is tight, or on SageMaker Endpoints for managed serving using Seldon Core or BentoML on Kubernetes.
We write unit tests for feature transformation and integration tests for the serving API. We run automated testing, bias detection, and baseline validation on the frozen dataset after every merge. Our MLOps experts use shadow deployment to test the new version against live traffic before cutover, and we select the model version to retain using the champion/challenger approach.
Three types of drift require different tools: data drift for changes in input distributions, concept drift for shifts in the feature-to-label relationship when the model itself does not change, and prediction drift for changes in output. Model monitoring uses Evidently AI, SageMaker Model Monitor, or Prometheus with Grafana and applies its own thresholds. Alarms are sent to your existing on-call tooling, such as PagerDuty, OpsGenie, or Slack.
Orchestration happens via Apache Airflow, Kubeflow Pipelines, SageMaker Pipelines, or Prefect, and data versioning is handled via DVC and Delta Lake. The feature store uses Feast or SageMaker Feature Store to make sure the values read during training and serving are exactly the same. Data engineering handles ingestion, preprocessing, and validation to keep runs reproducible months later for a new engineer.
GPU cluster setup consists of AWS EC2 P-series, GCP A100, and Azure NCv3. For distributed model training, we use PyTorch DDP/Horovod. We tune hyperparameters with Optuna/Ray Tune, and spot instances reduce costs due to their interruption tolerance. Our MLOps specialists track experiments in MLflow/Weights & Biases to ensure model consistency across staging and production.
Prompt versioning with rollback, plus evaluation harnesses scored on BLEU, ROUGE, and LLM-as-judge. RAG pipeline monitoring watches retrieval quality, context faithfulness, and answer groundedness as separate signals. Inference cost drops through quantization, vLLM serving, and batching, while fine-tuning pipelines handle domain adaptation for our generative AI development and LLM application services.
Our MLOps consultancy includes production delivery: multi-model AI architecture, multi-year production ML collaboration, and cloud-native infrastructure built on an AWS Select Tier Services Partner platform. We also provide custom AI development for enterprise ML systems, with each architecture decision grounded in engineering realities.
Production pipelines run on AWS SageMaker, Kubeflow across GKE and EKS, MLflow for experiment tracking and registry, and custom Kubernetes serving when managed services don’t fit the workload. MLOps consulting in this case involves existing architectures ready to receive traffic, with multimodal architectures in which commercial LLMs and custom models are deployed together. Human-in-the-loop output review applies to outputs that have a business and regulatory impact.
Every engagement is scoped against three maturity levels. Level 1 automates training and leaves the deployment manual. At Level 2, deployment is automated, and tracking is centralized. Level 3 runs retraining, deployment, and monitoring in response to triggers. MLOps-as-a-service is priced based on your team’s current level, so the engagement starts where you are today. This targeted approach prevents you from paying for heavy infrastructure you don’t need yet, scaling only when necessary.
We continuously monitor after deployment and catch latency spikes and memory leaks. We guarantee production availability with 99%+ uptime for serving endpoints, automated rollback in case of regression in model performance, and drift alerts and SLAs, which our team practiced before go-live. Our incident management solution shortened the reaction time from 60+ minutes to less than 30 minutes for making a PR-worthy fix, with up to 70% of incidents fixed automatically, leaving only human verification.
No requests are required to receive updates regarding the current state of the pipeline, ongoing compute costs, and progress of the entire project. We provide technical documentation on the endpoint API, automated rollback policy, and alerting SLA routes before the system is implemented. Data science engineers and platform engineers share the same dashboards to stay fully aligned.
Continuous monitoring after deployment ensures latency spikes and memory leaks are caught before they affect external applications. Failed CI/CD builds and stream degradation issues are sorted within the predefined time frames. The same team responsible for the pipeline builds and patches the vulnerabilities when the system pings them.
MLOps is an operational discipline that runs for years. Going live is not the goal; the fifth year of uptime is. Our PitchBook partnership is in its tenth-plus year, and the AI trend detection it now runs processes roughly 50,000 articles a day across 3,000+ data sources — work that followed an earlier cloud migration and microservices build for the same client.
MLOps consulting services are scoped to your current level, with the next level as the deliverable. MLOps maturity drives sequencing, staffing, and cost. An engineer assesses your current ML infrastructure against the three levels and returns, in writing, the exact engineering steps required to reach the next tier.
Training and deployment are done entirely by hand, and monitoring involves occasionally checking a dashboard. This approach works for a basic proof of concept but collapses at scale because retraining is painfully slow and releases break production. We intervene by first automating the core training jobs, then standing up a central model registry in MLflow or SageMaker Model Registry to serve as your primary stability anchor.
Pipelines for training are executed in Kubeflow Pipelines, SageMaker Pipelines, or Airflow DAGs using centralized tracking, but manual deployment and configuration are still involved. Automated ML pipelines and CI/CD are implemented. Shadow deployment tests against production traffic are performed with automated rollbacks triggered by post-deployment metrics. The approval process is moved from an individual’s calendar to the pipeline.
All ML operations, from training to evaluation, deployment, and continuous monitoring, are done automatically. Predefined metric thresholds trigger retraining and further deployment of models in the champion/challenger framework. We define strict performance SLAs and integrate monitoring with incident management systems so that the on-call engineer is notified before degradation affects business outcomes.
Selecting the right MLOps services tooling depends on three variables: your cloud environment, your existing data infrastructure, and the total size of your endpoint fleet. Fleet size matters more than most teams expect, since MLOps tools that comfortably support three models tend to break at thirty. We operate as a platform-agnostic engineering team and have delivered pipelines across all major clouds.
We use TensorFlow, PyTorch, Scikit-learn, and Keras. For production LLM deployment, we configure Hugging Face Transformers. XGBoost and LightGBM take care of complex gradient-boosting operations, and FastAI makes the journey from research to production easier. Generative workloads require special orchestration, which LangChain and LlamaIndex provide.
As an AWS Select Tier Services Partner, we build ML solutions managed on SageMaker. We natively support Vertex AI on GCP and Azure ML on Azure. When data security requirements keep training data on-premises, we configure on-premises and air-gapped deployments using Kubernetes. A hybrid architecture connects on-premises training directly to cloud serving endpoints.
Our MLOps specialists use Git, DVC, and MLflow to track code and data. Weights & Biases is needed when experiment tracking must be deep and at massive scale. Fully AWS-native environments leverage SageMaker Model Registry. Enterprises that want data lineage first, with an immutable audit trail behind it, use Pachyderm.
Advanced workflows run seamlessly on Apache Airflow, Kubeflow, and Prefect. Argo Workflows ensures native Kubernetes execution, while Ray runs highly distributed Python workloads. Teams operating in multiple environments have ZenML to ensure a consistent pipeline abstraction across multiple orchestrators and cloud providers.
Endpoint reliability is ensured using TorchServe, TensorFlow Serving, and BentoML. We configure Seldon Core and Seldon Deploy in a Kubernetes-native way for uninterrupted A/B testing and canary deployments. Complex deployments use vLLM for high-throughput language processing and NVIDIA Triton for significant GPU execution acceleration across mixed-model enterprise fleets.
Data management for large enterprise datasets is done safely using Delta Lake, Apache Iceberg, and DVC. You get an open-source feature platform using Feast, SageMaker Feature Store for AWS environments, and Tecton for large-scale feature storage. Evidently AI and Whylogs help detect data quality issues during pipeline execution, preventing bad batches from being used during training.
To share their experiences
Alex Samano
Co-Founder & CEO, Mogami App
SPD Technology has done a great job of maintaining the lifeblood of their codes. They’re transparent with pricing models and deliver within budget. Their dedicated teams act as an extension of the partner’s company. Responsibility and a commitment long-term partnership are two hallmarks of their work.
Fabrice Forget
Chief Product Officer, PitchBook
We feel very lucky to have found SPD Technology as our partner. Over the last 10 years, they have totally surpassed our expectations, and day after day we have received incredible value from the team. Here is the secret sauce, just put together your best ideas in your requirements and there is nothing they can’t do!
John Gabbert
Founder and CEO, PitchBook Data
Customers are king at PitchBook and SPD Technology shares in this mission. For the last 13 years, SPD Technology has helped us scale product development and continuously deliver the product functionality our clients need to make smarter decisions.
Prasad Sridhar
Senior Engineering Manager eCommerce Solutions Company
SPD Technology’s work has been praised by the client for their consistency and high quality. Their communicative, responsive, and flexible project management ensures a positive collaboration. Ultimately, their professionalism and forward-thinking are impressive.
Shariq Ahmad
Head of Technology, Morningstar, Inc
All of their developers and technology staff are highly talented and very professional. Working with SPD felt like we were working with an internal team. They are always accessible any time of the day and very flexible in providing support to our users who are globally distributed.
Steve Carner
Founder CEO, Home Hub
The team at SPD Technology exceeds expectations. Their professional communication style makes them stand out, They’re a skilled group of detailed-oriented workers. Customers can expect a team that provides helpful suggestions to better their clients.
SPD Technology designs and develops transformative software solutions that drive innovation, new revenue streams, and market leadership.
Industry: Finance, Payments & Fintech 
Country: USA 
Team Size: 2 Industry: Energy Management Country: USA Team Size: 7 Industry: eCommerce & Retail Country: France Team Size: 6 Industry: Finance, Payments & Fintech Country: USA Team Size: 3 Regulatory scope, data sensitivity, and latency requirements vary heavily by sector, and each one changes the architecture underneath an otherwise identical model. We map your specific constraints before finalizing the core architecture.
Engineering teams deploy pipelines for complex risk modeling, fraud detection, and algorithmic trading, wrapping them in strict compliance layers. Our fintech software development and financial AI systems operate under rigorous audit governance. This infrastructure allows institutions to safely process millions of financial transactions daily. For example, Morningstar’s financial analytics pipeline optimization generated a 10x reduction in storage costs and slashed processing times from days to just 2–3 hours.
Operational AI handles large-scale demand forecasting, route optimization, and automated quality-control protocols across our construction software development work. Because field data arrives unstructured, our ingestion layers rigorously clean it before models process it during logistics and supply chain development. Proving this scale, the HaulHub multi-model AI ticketing platform operates reliably across 500+ contractors and 35+ state agencies every day. Resilient ML pipelines keep all operations running 24/7.
High-traffic eCommerce environments require inference architectures for recommendation engines and customer-facing LLMs that can scale instantly to meet spikes in consumer demand, such as on Black Friday. We built an advanced LangChain LLM pipeline for a major French fashion retailer that processes 99% of complex queries in under 10 seconds and runs entirely on autoscaling infrastructure. We tuned the underlying RAG retrieval layer to keep inference compute costs predictable even as total user volume multiplied.
Medical deployments require maximum data confidentiality, including expert data labeling and inference pathways built to meet compliance rules. The healthcare and life sciences AI platforms are carefully designed under strict HIPAA compliance. We have built an FSA/HSA payment system that uses an AI classifier for eligibility classification with >80% accuracy on 200,000+ synchronized healthcare products, fully incorporating deep HIPAA and SOC 2 remediation.
Predictive maintenance, visual defect detection, and inspection automation on imagery captured in the field, where one missed defect costs more than the pipeline that found the rest. Our manufacturing software development work pairs with computer vision development and ML model deployment to cover the pipeline end-to-end. The drone inspection pipeline we built has held 100% object detection accuracy across more than five years of continuous field operation.
Assortment planning, pricing models, and catalog enrichment at volume, where one bad classification multiplies across a million SKUs. Our consumer and retail solutions run on the same ingestion stack. An AI product categorization pipeline we built scraped and processed 300,000 products in 3 hours as part of a 1M+ product catalog build, using the ChatGPT API with image recognition. Automating the workflow eliminates the need for manual mapping as your catalog expands.
Our MLOps implementation spans six stages, managed end-to-end by a single dedicated engineering team. Structuring a highly reliable system demands establishing automated data flows, rigorous versioning, and strict validation gates before ever serving complex predictive models to real users.
Deliverables include a maturity assessment with Level 1/2/3 scoring, platform selection (SageMaker, Kubeflow, MLflow, or a proprietary platform tailored to your cloud environment), and a 90-day MLOps roadmap. At the MLOps consulting stage, two engineers will review your repository and on-call log and agree on the order of operations before any infrastructure setup.
We set up Apache Airflow or Prefect to manage orchestration strictly, requiring DVC or Delta Lake for data versioning. The implementation of Feast or SageMaker Feature Store allows managing shared features during both training and serving operations. Ingestion, validation, and transformation operations are implemented from the very beginning, making it possible to track which batch failed and where the source of the failure is located.
We integrate MLflow or Weights & Biases to track all experiments and use Optuna or Ray Tune to optimize hyperparameters efficiently. Validation of algorithms is strictly enforced, thus preventing them from progressing if they fail. Each algorithm run will retain its specific data version, parameters, and performance statistics.
The operations team configures the CI/CD pipeline using either GitHub Actions or GitLab CI. The system undergoes rigorous shadow deployment testing against live production traffic before any cutover. Should performance issues arise during testing, the automatic rollback system kicks in immediately, without any human intervention, whereas traffic promotion will always be approved explicitly by engineers.
We monitor predictions using Evidently AI or SageMaker Model Monitor in order to detect drift. Engineers develop Grafana dashboards to understand the health of our serving endpoint, connecting relevant alarms to PagerDuty or OpsGenie. Before launch, we set monitoring thresholds and tune them based on live traffic, ensuring the alert fires on the first alarm rather than the fifth.
Degradation in performance above some predetermined threshold will trigger the retraining process automatically. New models are tested as a champion/challenger against existing models, gradually moving up to handle production traffic. The system maintains the lineage of each algorithm release in MLflow or the SageMaker Model Registry.
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Answering common questions on production ML operations.
MLOps, or machine learning operations, brings together machine learning and DevOps principles to operationalize machine learning models in production through automation, versioning, testing, deployment, and observability of model artifacts. The key difference is duration and scale: a model in a notebook runs once for one user, while a production model runs reliably, serving user requests continuously without constant supervision from the data scientist.
An MLOps consultant analyzes exactly how your training, deployment, and monitoring workflows fail and provides a roadmap to implement MLOps solutions to close these gaps. This analysis includes aspects such as training pipeline reproducibility, CI/CD gating, drift-detection coverage, and alignment within your organization. Our unique MLOps consulting offering is that the same engineers who analyze your architecture will later write the production code.
It depends on your cloud environment, available tooling, and current operational maturity level. MLflow is a good fit for teams just starting with MLOps because it’s open source, provides essential tracking, and doesn’t require a Kubernetes cluster. Kubeflow provides Kubernetes-native pipeline orchestration but requires high platform engineering maturity. SageMaker is good for companies committed to AWS and looking for fully managed training, while Vertex AI does the same for GCP users.
Teams consistently struggle with unstructured data quality, silent model drift, complex deployments, strict regulatory compliance, and cross-team collaboration friction. These hurdles surface as severe operational costs. We work through them in production systems, proven by HaulHub’s multi-model routing architecture that reliably serves 500+ contractors and a LangChain chatbot that answers 99% of complex queries in under 10 seconds on highly elastic autoscaling infrastructure.
LLMOps is an MLOps specialization focused on solving specific challenges faced by large language models. Traditional guidelines can be applied, but there are mandatory aspects of prompt versioning, evaluation harnesses for generative output with no single ground truth, and complicated RAG pipeline monitoring where retrieval becomes inefficient without depending on the model itself. The technical stack includes libraries such as LangChain and LlamaIndex to coordinate processes, LangSmith and/or Weights & Biases to monitor them, and vLLM and/or AWS Bedrock to serve.
The initial audit and technical roadmap process takes 2–4 weeks. The Level 1 implementation for a team with simple production models takes 6–12 weeks. Level 2 implementation adds 8–16 weeks, largely depending on the current infrastructure setup. The full Level 3 automation takes 4–9 months. This depends more on data infrastructure readiness than on any other factor. Training data pipeline readiness can cut this time in half, and MLOps consulting starts from this point of analysis.
MLOps must meet many operational control standards in the finance and healthcare industries. However, standard MLOps solutions usually neglect them. We provide ML model lineage tracking, which traces any production model to its specific training dataset and code version. Our implementation creates immutable audit trails of the model deployment approvals. It includes explainability tools and a data residency protocol for model governance purposes. For example, we have provided an FSA/HSA payment solution within a HIPAA and SOC 2 compliance framework with 80%+ classification model accuracy.
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