An engineering approach to consulting
We do not advise on paper - we build working ML solutions with CI/CD, monitoring, and production quality from the start.
8
years in cloud transformations
120
scalable cloud projects
50
international experts on the team
MLOps consulting services from Alpacked: Faster, cheaper, and more stable for your ML
We build reliable ML and LLM platforms that scale with your business: we automate ML processes, optimise model performance in production, and implement monitoring and stability control of their results.
In 30–60 days, we set up reliable and reproducible pipelines for training, validation, promotion, and deployment of models, based on the best open-source solutions.
Data control
Automatic validation and drift detection maintain model stability and prevent its degradation in production.
Savings: -40-70%
Thanks to ML infrastructure optimisation, processing pipelines, and model automation, you can significantly reduce costs without losing quality.
Faster iterations: 2–3×
Automated CI/CD for model training, testing, and promotion shortens the development cycle and speeds up the delivery of improvements to production.
MLOps consulting services
From recommendations to fully implemented ML solutions, we provide the path from idea to stable production.
MLOps infrastructure
AI infrastructure consulting
Audit of the current ML/LLM infrastructure and recommendations for stability, speed, and scaling.
AI infrastructure architecture
Design of production architecture for models: compute, data, networking, security, and platforms.
AI infrastructure setup services
Deployment of infrastructure: clusters, model environments, monitoring, access, and MLOps tools.
Private AI environment setup
Creation of an isolated environment (VPC) with access control, security policies, and full data confidentiality.
On-prem LLM deployment
Deployment of LLMs in local data centres or corporate infrastructure with compliance adherence.
Automation and ML operations
ML pipeline automation
Automation of ML processes from training to model retraining and their delivery to production.
ML CI/CD setup services
Building CI/CD processes for models with testing, validation, and safe release of new versions.
Model release management
Standardised management services of model releases with transparent versioning and controlled promotion.
Secure LLM platform setup
Building a secure platform for LLMs: authorisation, rate limiting, logging, and access control.
Data and drift monitoring
Monitoring of data changes and model stability with automatic response to quality deterioration.
Responsible & Secure AI
Responsible and explainable AI setup
Implementation of model transparency: explainability, decision audit, and ethical risk control.
ML governance and access control
Management of model policies: access, versioning, logging, and control of ML pipeline operation.
ML security and compliance audit
Assessment of the ML environment for security and regulatory compliance with recommendations for risk mitigation.
AI Governance, Security & Compliance
AI observability setup services
Real-time monitoring of model, services, and data performance services.
Model evaluation (eval stack)
Automated model evaluation services: accuracy, version comparison, batch eval, and qualitative metrics.
Technologies and platforms for
production-ready AI
We use proven ML technologies and modern platforms to build stable, scalable, and fast AI systems ready for real production workloads.
Take the first step toward reliable MLOps
If you want predictable, failure-free model performance - start with an MLOps audit.
Our proven methodology for your success
A clear path to MLOps maturity
A consistent maturity path: from ML process automation services to a full model management platform.
Transparency and observability
The eval stack, monitoring, and cost tracking are integrated into the system from day one.
ROI as a core principle of work
We measure all solutions and services by key metrics: performance, accuracy, speed, and savings.
Why work with Alpacked?
Guaranteed results in 30-60 days
We work in a timeboxed format and deliver production-ready MLOps/LLM solutions with measurable metrics, instead of endless hourly consulting.
Risk reversal
If performance, accuracy, or cost do not meet the agreed targets, we improve the solution for free. This MLOps-driven model provides maximum engineering transparency that large consultancies do not offer.
Engineering-first
Every project ends with a working system: pipelines, CI/CD, observability, autoscaling, and an eval stack - real production, not a presentation or recommendations.
Transparency from day one
Every solution includes dashboards for performance, cost, and quality. You see latency, token usage, drift, errors, and ROI in real time - no black boxes. Full MLOps visibility from the start.
MLOps solutions for different industries
We adapt MLOps processes to the needs of various sectors, ensuring stable models and transparent ML pipelines.
Fintech
Accurate scoring and fraud models supported by reliable MLOps practices and full regulatory compliance.
Healthcare
Secure ML systems with private infrastructure and strict access control built on healthcare-grade MLOps.
Logistics and supply chain
Demand forecasting, routing, and supply optimisation powered by stable MLOps pipelines.
Manufacturing
Predictive maintenance and quality control enhanced by continuous monitoring and production-ready MLOps.
E-commerce
Personalisation, search, and recommendations delivered with low latency and controlled cost through scalable MLOps setups.
Telecom
Load forecasting and real-time models backed by robust MLOps workflows.
SaaS solutions
Built-in ML features with auto-retraining and full observability enabled by SaaS-focused MLOps.
EdTech
Personalisation, content analysis, and learning analytics with guaranteed accuracy supported by education-oriented MLOps.
Our leadership team
Our approach is shaped by engineering practices, technical mastery, and solutions tested by the most demanding clients.
Dmytro Konstantynov
DevOps Team Lead, Co-Founder
A certified Cloud Architect and Kubernetes expert with deep experience in building DevOps teams and processes. Focuses on scaling, stability, and infrastructure automation that enable continuous product growth.
Yevhenii Hordashnyk
DevOps Consultant, Co-Founder
A specialist in Serverless, Docker, and AWS. One of the first engineers to implement AWS Managed Kubernetes in production. Able to optimise complex and unconventional systems, ensuring flexibility, reliability, and efficiency of cloud solutions.
10-30%
fewer support requests
100%
SOC 2 and GDPR compliance
<500 ms
latency with eval control
Our team’s certifications
Our work in action: Case studies
and success stories
Optimizing the Cloud Infrastructure for a Next-Generation NFT Marketplace
SEE FULL CASE STUDYNFT Company (NDA)
Team20-50 peoplePeriodNovember 2021 - June 2022LocationHong Kong
Staying Truly Agile and Cost-Efficient in the Cloud: The Mission Is Possible
SEE FULL CASE STUDYVR Company (NDA)
Team20-50 peoplePeriodJanuary 2020 - presentLocationTel Aviv, Israel
Avoiding Long Builds and Slow Deployments in DevOps: How We Helped A Startup Streamline Their CI/CD Processes
SEE FULL CASE STUDYStreamSer (NDA)
Team101-250 peoplePeriod2022 - presentLocationBoston, Massachusetts, United StatesOptimizing the Cloud Infrastructure for a Next-Generation NFT Marketplace
SEE FULL CASE STUDYNFT Company (NDA)
Team20-50 peoplePeriodNovember 2021 - June 2022LocationHong KongStaying Truly Agile and Cost-Efficient in the Cloud: The Mission Is Possible
SEE FULL CASE STUDYVR Company (NDA)
Team20-50 peoplePeriodJanuary 2020 - presentLocationTel Aviv, IsraelAvoiding Long Builds and Slow Deployments in DevOps: How We Helped A Startup Streamline Their CI/CD Processes
SEE FULL CASE STUDYStreamSer (NDA)
Team101-250 peoplePeriod2022 - presentLocationBoston, Massachusetts, United States
Helping a Top Offensive Security Provider Use DevOps to the Max
SEE FULL CASE STUDYSecurity Provider (NDA)
Team251-500 peoplePeriod2022 - presentLocationTempe, Arizona, United StatesOptimizing the Cloud Infrastructure for a Next-Generation NFT Marketplace
SEE FULL CASE STUDYNFT Company (NDA)
Team20-50 peoplePeriodNovember 2021 - June 2022LocationHong KongStaying Truly Agile and Cost-Efficient in the Cloud: The Mission Is Possible
SEE FULL CASE STUDYVR Company (NDA)
Team20-50 peoplePeriodJanuary 2020 - presentLocationTel Aviv, IsraelAvoiding Long Builds and Slow Deployments in DevOps: How We Helped A Startup Streamline Their CI/CD Processes
SEE FULL CASE STUDYStreamSer (NDA)
Team101-250 peoplePeriod2022 - presentLocationBoston, Massachusetts, United StatesHelping a Top Offensive Security Provider Use DevOps to the Max
SEE FULL CASE STUDYSecurity Provider (NDA)
Team251-500 peoplePeriod2022 - presentLocationTempe, Arizona, United StatesOptimizing the Cloud Infrastructure for a Next-Generation NFT Marketplace
SEE FULL CASE STUDYNFT Company (NDA)
Team20-50 peoplePeriodNovember 2021 - June 2022LocationHong KongStaying Truly Agile and Cost-Efficient in the Cloud: The Mission Is Possible
SEE FULL CASE STUDYVR Company (NDA)
Team20-50 peoplePeriodJanuary 2020 - presentLocationTel Aviv, IsraelHelping a Top Offensive Security Provider Use DevOps to the Max
SEE FULL CASE STUDYSecurity Provider (NDA)
Team251-500 peoplePeriod2022 - presentLocationTempe, Arizona, United StatesOptimizing the Cloud Infrastructure for a Next-Generation NFT Marketplace
SEE FULL CASE STUDYNFT Company (NDA)
Team20-50 peoplePeriodNovember 2021 - June 2022LocationHong KongStaying Truly Agile and Cost-Efficient in the Cloud: The Mission Is Possible
SEE FULL CASE STUDYVR Company (NDA)
Team20-50 peoplePeriodJanuary 2020 - presentLocationTel Aviv, Israel
Take your ML to the next level
Want to turn experiments into stable ML systems? We will help you do it right.
Useful materials from our blog on this topic
FAQ
Have other questions? Email us!
sales@alpacked.io
1. How can you tell that a company already needs MLOps?
When models move from the “lab” into production and begin influencing business processes, automation, monitoring, and stability become necessary. If the team deploys models manually or reacts to failures only after they occur, the moment for MLOps has come.
2. What are the signs that models are drifting or “breaking”?
Accuracy drop, changes in data distribution, an increase in support tickets, inconsistent responses, latency spikes, or suspiciously stable (too “flat”) predictions - all of these are early symptoms of drift.
3. Is it possible to implement MLOps without completely rebuilding the infrastructure?
Yes. MLOps services are integrated gradually: monitoring, CI/CD, model management, and training automation are added step by step. You do not need to rebuild the entire stack from scratch - only strengthen the weak points, which can be done with a tailored MLOps implementation service from Alpacked.
4. How to choose a stack: Kubeflow, MLflow, Vertex AI, or SageMaker?
It is better to start from the needs: scale, privacy, budget, and team.
- Kubeflow - for serious K8S environments
- MLflow - an easy start and flexibility
- Vertex/SageMaker - managed platforms with minimal infrastructure load
We choose the stack that fits you, not the other way around.
5. What if you have only Data Scientists but no ML engineers?
This is the most common situation. MLOps services takes over deployment, infrastructure, and monitoring, allowing Data Scientists to focus on models instead of firefighting.
6. How long does MLOps implementation take?
On average, 30-60 days is enough to automate key pipelines, set up monitoring, workflows, and the first governance processes.
7. Can model retraining be automated?
Yes. Models are trained on a schedule, when new data appears, or in case of drift. After eval tests, the new version is automatically promoted to production.
8. How to measure the effectiveness of MLOps for business?
Not only model accuracy:
- iteration speed;
- reduction of infrastructure costs;
- latency and stability;
- fewer support triggers;
- improvement in product metrics.
MLOps is about economic impact, not just ML.
9. How to ensure explainability and regulatory compliance?
Within a proper MLOps framework, model versions, audit logs, access control, private infrastructure, explainability algorithms (SHAP/LIME), and automated recording of model decisions form the foundation for fintech and healthcare compliance.
10. How to optimise GPU and inference costs?
In MLOps-driven environments, the key tools are autoscaling, quantisation, batching, LoRA, efficient runtimes (vLLM/TensorRT), spot instances, and cost dashboards. The right combination provides 40–70% savings.
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