Machine Learning Solutions

A machine learning model that performs well in a notebook has produced no value yet. Value appears when a prediction reaches a decision, at the moment the decision is made, in a form the person or system making it can act on, and keeps doing so as the underlying data shifts.

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10+Years of Industry Expertise 200+Talented Developers 90%Customer Satisfaction Rate

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What Clients Say About Our Machine Learning Solutions Services

Independent reviews from Clutch and DesignRush. Every review below is shown in full and links back to its source platform so you can confirm it yourself.

4.9/5.0 12 reviews View all reviews on Clutch

5.0/5.0 6 verified reviews View all reviews on DesignRush

5.0

May 2026

“Their balance between technical expertise and product thinking stood out.”

AgileTech Viet Nam developed an AI-powered analytics platform for an AI visibility and GEO solutions company. The platform featured real-time reporting dashboards and data processing pipelines. AgileTech Viet Nam’s work improved the client’s data processing efficiency and the accuracy of AI-generated insights. The platform was user-friendly and supported the client’s first large-scale deployments. AgileTech Viet Nam’s team was communicative, adaptive, and technically proficient.

Improved data-processing efficiency and AI insight accuracy

5.0

Nov 2024

“We saw huge cost savings and optimized stock levels, so we consider AgileTech a partner in our company transformation.”

AgileTech created a working Supply Chain Management (SCM) system that has helped our supermarket chain simplify operations across inventory management, demand forecasting and logistics. They integrated our existing systems, automated inventory and logistics and gave us robust demand forecasting with AI, we saw huge cost savings and optimized stock levels. And they continued to deliver on quality post launch with regular updates and training sessions, so we consider AgileTech as a partner in our company transformation.

AI-driven demand forecasting across inventory and logistics

5.0

Jun 2025

“Their proactive communication, flexibility, and technical expertise stood out throughout the project.”

AgileTech Viet Nam used React Native to develop an iOS and Android app for a real estate platform. The team integrated the app with the client’s backend and implemented property search features. AgileTech Viet Nam’s high-quality work resulted in a 30% increase in user registrations and over 1,000 app downloads within the first month. The team delivered on time, provided regular updates, and responded to feedback. Their flexibility and proactive communication impressed the client.

30% more sign-ups and 1,000+ downloads in the first month

5.0

May 2026

“They followed a structured Agile approach with clearly defined milestones.”

AgileTech Viet Nam developed a web-based logistics management system for an automotive parts supplier. The team integrated the platform with third-party providers and built a real-time tracking dashboard. AgileTech Viet Nam’s work reduced manual processing by 55%, improved real-time shipment visibility, and increased operational efficiency across multiple departments. The team followed a structured Agile approach, communicated consistently, delivered on time, and was flexible.

Manual processing reduced by 55% with real-time shipment visibility

5.0

May 2026

“They were not just executing tasks but were genuinely invested in the product’s success.”

AgileTech Viet Nam developed an MVP for a digital solutions provider’s marketplace platform. They created the UI/UX design, developed the front- and backend, and integrated payment systems. AgileTech Viet Nam successfully launched the MVP on time, allowing the client to onboard 500 users within two months. The platform was stable with minimal issues. Moreover, the team was collaborative, flexible, quick to adapt to changing priorities, and genuinely invested in the project’s success.

MVP launched on time, 500 users onboarded within two months

5.0

May 2026

“Their ability to handle both high-level architecture and hands-on implementation is rare.”

An IT services company hired AgileTech Viet Nam to re-architect their enterprise SaaS platform. The team conducted a technical audit, designed a new architecture, migrated legacy data, and added API gateways. Thanks to AgileTech Viet Nam’s work, the client saw improvements in system scalability, platform uptime, and deployment time. The team followed the Agile methodology, communicated effectively, and was responsive to the client’s feedback. AgileTech Viet Nam helped the client make better decisions.

Improved scalability, uptime, and deployment time on an enterprise SaaS

5.0

May 2026

“They were flexible in accommodating evolving business needs while still keeping the project on track.”

AgileTech Viet Nam developed a digital platform for a real estate firm. They designed the UI/UX, implemented advanced search and filtering functionalities, integrated dashboards, and automated reporting tools. The platform improved operational efficiency, business performance, lead conversion rates, and customer responsiveness. AgileTech Viet Nam implemented a well-structured project management approach, meeting timelines and responding to feedback with flexibility. Their tailored solutions stood out.

Improved efficiency, business performance, and lead conversion

5.0

Mar 2026

“The team demonstrated strong professionalism, reliability, and a proactive approach throughout the project.”

AgileTech Viet Nam developed a scalable engineering workflow system for an ICT company. The goal was to streamline internal processes, improve cross-team visibility, and enhance coordination. The system significantly improved operational efficiency by reducing manual processes and increasing transparency across teams for the client. AgileTech Viet Nam set clear milestones and provided regular updates to ensure everyone remained in sync. Their professionalism and reliability stood out.

Scalable engineering workflow system with fewer manual processes

5.0

Nov 2024

“The platform has greatly improved our operational efficiency, and we wholeheartedly recommend AgileTech.”

Working with AgileTech has been an amazing experience, especially when it comes to creating a personalized e-learning program for our IT services business. We were pleasantly surprised by how adaptable and scalable the platform developed by the AgileTech Team was. Their proficiency with user-friendly course administration and community participation technologies enabled us to provide a thorough instructional resource catered to our customers’ particular requirements. The platform has greatly improved our operational efficiency, and we are amazed by its capacity to manage heavy traffic with ease and offer comprehensive reporting and analytics. The platform remained successful and user-friendly because of the team’s dedication to continuous support and their quick response to criticism. For any organization looking to create innovative and reliable digital solutions, we wholeheartedly recommend AgileTech as a partner.

Personalized e-learning platform that handles heavy traffic

5.0

Nov 2024

“They used a potent tech stack including PHP, React, and AWS to guarantee perfect scalability and zero downtime.”

AgileTech provided a superb Learning Management System (LMS) that creatively and precisely addressed the intricate needs of our organization. Within a scalable, completely customizable platform, the LMS made it possible to create comprehensive courses, administer student tests, engage the community, and obtain real-time data insights. AgileTech’s modular and agile approach made sure that every component, from the user-friendly course builder to the sophisticated analytics and communication tools, was customized to meet our demands despite the difficulties of developing such a feature-rich system. In order to guarantee perfect scalability and zero downtime, even with high user concurrency, AgileTech used a potent tech stack that included PHP, React, and AWS. Their post-launch assistance, which included frequent training sessions and upgrades, gave our staff the means to fully use the LMS’s potential. This project was a testament to AgileTech’s technical skill, rapid deployment, and dedication, making them an outstanding choice for educational technology development.

Feature-rich LMS with course builder, analytics, and comms

5.0

May 2026

“Their professionalism, responsiveness, and ability to execute efficiently made them feel like an operational partner.”

AgileTech Viet Nam optimized a workflow for a consulting firm. The team improved lead management processes and streamlined internal communication systems across multiple departments. AgileTech Viet Nam’s work improved the client’s operations, customer communication process, and overall workflow efficiency. The team was strategic and reliable. Moreover, AgileTech Viet Nam worked closely with the client to understand their business objectives and recommend solutions.

4.5

Apr 2021

“They are incredible to work with and are truly passionate about technology and our concept.”

AgileTech Viet Nam provides mobile app development services for a social media startup. They are building the app using Node.js and Flutter. With the ongoing partnership, AgileTech Viet Nam’s passion has impressed the client so far. The team is truly into technology, and their concept makes the client feel confident about the project’s continuous success.

4.0

Jun 2019

“I was impressed with the flow of the project.”

AgileTech Viet Nam developed a mobile app from scratch. After helping with scoping and objective formation, AgileTech created design mockups and developed the full iOS and Android solutions. Valuable user data is being gathered in ongoing beta tests, which will soon lead to growth and expansion opportunities. AgileTech Viet Nam provided consistent communication and established a smooth workflow. Issues were raised proactively, allowing fixes to come earlier in the process.

5.0

Nov 2024

“AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store.”

AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store, incorporating data analytics that we could easily share with our customers. The scope of work included mock-up creation, UI/UX design, interactive prototyping, app development, rigorous testing, and setting up a secure production environment on AWS, culminating in the apps release on both iOS and Android platforms.

5.0

Nov 2024

“Since we launched our new site we’ve seen a significant increase in traffic and sales, which proves their work is effective.”

We’ve had an amazing experience with AgileTech for our eCommerce platform. The team worked with us to build an user friendly online store that met all our business needs. Their eCommerce development expertise was evident from the start as they shared valuable insights on how to maximize sales. The AgileTech team are not only talented but also care about their clients success. Since we launched our new site we’ve seen a significant increase in traffic and sales which proves their work is effective.

5.0

Nov 2024

“Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly.”

Working with AgileTech has been a game changer for us. We wanted a modern and responsive website and AgileTech delivered more than we expected. The team was super collaborative and involved us in every step of the process. Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly. The AgileTech team members are not only talented professionals but also great communicators, so we could share our vision and ideas easily. They were so committed to our success and the end result has increased our user engagement and satisfaction so much.

Explore all 16 client testimonials

Benefits of Machine Learning Solutions

Explore the benefits AgileTech Vietnam delivers for every machine learning solutions engagement.

Decisions at a Volume People Cannot Reach

Some judgements are individually simple but occur too often for human attention: scoring every transaction, ranking every item for every visitor, triaging every incoming request. Machine learning is well suited to this class, not because it exceeds human judgement on any single case, but because it applies a consistent standard at a volume where humans cannot.

Patterns Across More Variables Than a Rule Set Can Hold

Hand-written rules work until the number of interacting factors exceeds what anyone can maintain, at which point the rule set becomes a source of errors nobody dares to change. A model learns the interactions from historical outcomes and can be retrained as they shift, though it trades away the plain readability that made rules attractive.

Forecasts Attached to the Decisions They Inform

A forecast is only useful if it arrives before the commitment it should influence and at the granularity that commitment is made. We scope forecasting backwards from the decision, its timing, and its granularity, which frequently reveals that a simpler statistical method serves the need and machine learning is not required.

Our Machine Learning Process

Six phases from problem framing to a monitored production model, with an explicit decision point before any modelling begins.

Problem Framing and Feasibility

We establish which decision the prediction serves, what is done today, and what an improvement is worth. We also check the prerequisites: enough historical examples, labels that reflect the real outcome, and no target leakage. If those are absent, or if a rule or a statistical method would serve as well, we say so before any modelling is proposed.

Data Preparation and Feature Engineering

Training data is assembled reproducibly rather than by hand, and features are defined so they are computed identically in training and in production. Mismatch between the two is among the most common reasons a model that validated well performs poorly once deployed.

Baseline and Model Development

We establish a baseline first, whether that is current human performance or a simple heuristic, so any model has something honest to be measured against. Models are then developed and compared on a held-out set using a metric chosen for the actual cost of each error type, not for whichever number looks best.

Evaluation Against Business Impact

Technical performance is translated into operational terms: how many false positives your team can absorb, what a missed case costs, where the decision threshold should sit. A model with excellent aggregate accuracy can still be unusable if its errors fall in the wrong place, and this is where that is caught.

Deployment and Integration

The model is deployed into the system that consumes it, whether as a service, a batch process, or an embedded component, with the latency, availability, and fallback behaviour the use case requires. What happens when the model is unavailable or returns low confidence is designed rather than left undefined.

Monitoring, Retraining, and Governance

Prediction distributions, input drift, and outcome quality are monitored, with retraining triggers defined. Every model degrades as the world moves away from its training data; the difference is whether that is detected by monitoring or by a complaint.

AgileTech Vietnam: Your Machine Learning Partner

The reasons global companies trust AgileTech Vietnam to deliver this service, from deep expertise to uncompromising quality and security.

We Will Tell You When Not to Use ML

A meaningful share of problems presented as machine learning are better solved by fixing a process, writing a rule, or applying a standard statistical method. Recommending that costs us the engagement and saves you a system that needs retraining forever.

Built for Production, Not for a Notebook

Deployment, latency, fallback behaviour, and monitoring are scoped from the start. Models that validate well and never reach production are the most common outcome in this field, and the causes are almost always engineering rather than modelling.

Honest Evaluation Against a Baseline

We establish what current performance actually is before claiming an improvement over it. A model presented without a baseline is presented without meaning, however impressive the headline metric.

Error Costs Weighed Properly

False positives and false negatives rarely cost the same, and the threshold that balances them is a business decision rather than a technical default. We surface that trade-off in operational terms so you can set it deliberately.

Data Engineering in the Same Practice

Most machine learning problems are data problems first. Because the same practice engineers your data layer, the reproducible datasets and consistent feature computation that models depend on are within scope rather than a dependency on another supplier.

Monitoring and Retraining Included

A model handed over without drift monitoring will silently degrade until someone notices a bad outcome. Monitoring, retraining triggers, and the runbook for acting on them are part of the delivery.

10+ years delivering software, data, and AI engineering for start-ups, SMEs, and enterprises.
200+ talented developers across engineering, data, cloud, and QA disciplines.
300+ projects completed with 90% customer satisfaction rate.
Refined Quality Management practices verified by ISO 9001:2015
Powerful Security Management practices supported by ISO 27001:2013
Named Top App Developer in Vietnam by Clutch.co

Technologies We Work With

The modelling, training, and deployment tooling our engineers use to take a model from feasibility through to a monitored production system.

Modelling

scikit-learn
XGBoost
LightGBM
PyTorch
TensorFlow

Data & Features

Python
Pandas
Apache Spark
Feast

MLOps

MLflow
Weights & Biases
Kubeflow
Docker
Kubernetes

Serving & Monitoring

FastAPI
Seldon
Evidently
Prometheus

Who can Benefit from AgileTech's Machine Learning Solutions?

Companies Making High-Volume Repetitive Judgements

Transaction scoring, request triage, content ranking, and eligibility pre-checks share a shape: each individual case is straightforward, but the volume means either a person is overloaded or a rule set has grown beyond anyone's ability to maintain it safely.

Companies Forecasting Demand, Risk, or Behaviour

Forecasting is valuable when a real commitment depends on it: stock to order, capacity to staff, credit to extend, or intervention to prioritise. It is much less valuable when the forecast is produced and then not acted upon.

Companies With Rule Sets That Have Become Unmaintainable

A common position is a scoring or routing rule set accumulated over years, where nobody now understands the interactions and changing one rule produces unexpected effects elsewhere.

Best Practices for Choosing your Machine Learning Partner

Six practical steps to evaluate and select the right partner for your project.

Ask when they would recommend against machine learning

A partner who cannot describe the situations where a rule, a process fix, or a statistical method is the better answer either has not encountered them or will not tell you. Both are expensive.

Require a baseline before any accuracy claim

An accuracy figure without the current performance it improves on, and without the dataset it was measured against, carries no information. Insist on both.

Confirm deployment and monitoring are in scope

Ask specifically who deploys the model, what happens when it is unavailable, what drift monitoring is included, and what triggers retraining. Engagements that stop at a validated model routinely produce nothing usable.

Discuss error costs, not just accuracy

False positives and false negatives have different consequences in your operation. A partner who has not asked which is worse has not understood the problem well enough to set a threshold.

Establish explainability requirements early

If you must be able to justify an individual decision to a regulator, a customer, or an auditor, that constrains which models are usable. Raise it at the start, because it is not something that can be retrofitted.

Check the data foundation is addressed

Ask how training data will be assembled reproducibly and how features will be kept consistent between training and production. A partner who treats data preparation as a preliminary rather than the bulk of the work is underestimating the project.

Flexible Engagement Models

Choose the engagement model that fits your project size, budget, and timeline. All models include dedicated communication channels and transparent progress tracking.

Frequently Asked Questions

How is machine learning different from your analytics and BI service?

Analytics and business intelligence measure and explain what has happened so a person can decide what to do. Machine learning predicts what has not happened yet, or automates a decision at a volume no person could handle. If your question is why did this change, that belongs on the analytics page. If it is what will happen next, or decide this automatically for every case, it belongs here. Most organisations get more value from doing the first well before attempting the second.

How is this different from your generative AI service?

This page covers predictive machine learning on your own structured data: forecasting, classification, scoring, ranking, and anomaly detection. Generative AI covers language and content: assistants, retrieval over documents, drafting, and extraction from unstructured text. They use different techniques, need different data, and fail in different ways, which is why they are separate pages.

Do we have enough data for machine learning?

It depends on how varied the problem is and how strong the signal is, not on a universal row count. What matters more is whether you have historical examples with labels that reflect the real outcome, whether the conditions that produced them still hold, and whether there is any leakage of the answer into the inputs. We assess this in the feasibility phase and will tell you if the answer is no.

How long does a machine learning project take?

The modelling is usually a small share of the elapsed time. Data preparation, deployment, and monitoring dominate, and their duration depends on the state of your data platform and the system the prediction must reach. We scope after feasibility rather than quoting a standard duration, because a figure given before profiling the data would be a guess.

What accuracy can you achieve?

We do not quote accuracy figures in advance, and we would treat any supplier who does with caution. Achievable performance depends entirely on your data, your problem, and the baseline being improved on, and an accuracy number stated without those is unmeasurable. What we commit to is establishing an honest baseline, reporting performance against it on a held-out set, and telling you plainly if the improvement does not justify the system.

What happens when the model degrades over time?

Every model degrades as the world moves away from its training data. We monitor input drift, prediction distributions, and outcome quality, define the thresholds that trigger retraining, and hand over the runbook. Detecting degradation through monitoring rather than through a complaint is the difference between a maintained system and an abandoned one.

Can you explain individual predictions?

To varying degrees, depending on the model. Simpler models are directly interpretable; complex ones require attribution techniques that indicate influence rather than provide a definitive reason. If you must justify a decision to a regulator, a customer, or an auditor, tell us at the start, because it constrains the model choice and cannot be added afterwards.

Do we need data engineering first?

Usually yes, at least for the data the model consumes. Machine learning needs reproducible datasets and features computed identically in training and production, and neither survives being assembled by hand. Our data engineering page covers that work; how much is required depends on the state of your current platform.

Do you build recommendation engines?

Yes. Recommendation is a ranking problem and is well suited to machine learning where you have sufficient interaction history. For retail specifically, our recommendation engines page under the retail practice covers the applied case, including the catalogue and behavioural data it depends on.

How do we get started?

Describe a decision your organisation makes repeatedly where being right more often would be worth something measurable. We will assess feasibility, including whether the data supports it and whether a simpler method would serve, and tell you honestly if machine learning is not the right answer.

Trusted and Recognized

Independent directories, certification bodies, and award programs that have assessed our work. Each badge links to its source or names the standard behind it, so every claim on this page can be checked at first hand.

Top Software Development Company - GoodFirms

TechBehemoths Awards - Mobile App Development

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