Machine Learning Consulting and Development
Our machine learning consulting starts with the decision a model should support, then checks whether your data can carry it. When it can, we build the model and the pipeline around it, and send its predictions into the systems your teams already use.
Machine learning model
Forecasts and flags
APIs
- ERP
- Data warehouse
- Dashboards
WHERE PREDICTIONS LAND
What Is Machine Learning?
Machine learning is software that learns patterns from historical records, such as sales, orders, or sensor readings, and uses them to predict, classify, or flag new cases. Machine learning consulting helps a company find the decisions a model can improve, then builds and runs that model.
Machine learning does well at categorizing, clustering, price predictions, and demand forecasting. It is also sensitive to bad data and to small volumes of data. Large language models are close to the opposite. They handle messy text well and need no training data of your own.
Machine learning vs. generative AI
Machine learning predicts a number or a category from your own history. Generative AI reads and writes text, images, or code with a model someone else trained. Many systems use both, with an AI agent calling a machine learning model as one of its tools.
WHEN IT FITS
Signs Machine Learning Fits the Problem
Machine learning earns its cost when a decision repeats often and the history behind it is large and clean. These signs show up first.
Forecasts live in a spreadsheet
Planners build forecasts by hand from last year's numbers, and the plan misses every time demand shifts.
The same judgment call repeats all day
Staff sort, score, or route the same kind of record thousands of times, following patterns nobody wrote down.
Problems surface after the damage
Fraud, equipment faults, or bad orders get caught at month-end review, long after a model watching the data could have flagged them.
Years of history nobody uses
Sales, order, and sensor records pile up, but nobody has turned them into predictions the business acts on.
A model never left the notebook
A promising model has no data pipeline, no monitoring, and no way into the systems where decisions happen.
Hand-written rules keep growing
Every exception adds another if-then rule, and the rule set now breaks as often as it helps.
OUR APPROACH
How We Approach Machine Learning
We start from the decision, not the algorithm, and we'll tell you when a simpler tool will do the job. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Model-first project
- Algorithm picked before the problem
- Accuracy checked only in a notebook
- No path into production systems
- Nobody watching the model after launch
Decision-first project
Start with the decision
We define the decision the model supports, how often it happens, and what a wrong prediction costs, before any data work starts.
Check the data first
Machine learning is sensitive to bad data and small volumes of data, so we check quality and volume before anyone promises an accuracy number.
Use the simplest tool that works
Some problems need a trained model. Others need a language model, a BI query, or a plain rule, and we recommend the one that fits.
Built into your systems
Predictions reach the ERP, CRM, or dashboard where people act on them, through APIs. Whatever we build, you own.
MODEL TYPES
Common Types of Machine Learning Models
Most business machine learning falls into a handful of model types. The right one depends on the question you're asking and the data you have.
| MODEL TYPE | WHAT IT DOES | WHEN IT FITS |
|---|---|---|
| Forecasting | Predicts a future value from past values | Demand, sales, inventory, or staffing plans |
| Classification | Assigns each record to a category | Routing tickets, scoring leads, sorting documents |
| Regression | Estimates a number from many inputs | Pricing, lead times, and cost estimates |
| Clustering | Groups similar records without labels | Customer segments and product families |
| Anomaly detection | Flags records that don't fit the pattern | Fraud, equipment faults, and data errors |
| Recommendation | Suggests items based on past behavior | Product search and cross-sell |
| Computer vision | Reads images and video | Photo tagging, inspections, and content moderation |
How we choose the model type
We start with the question and the data. A forecast needs enough history to show seasonal patterns, and a classifier needs labeled examples. Where the data is thin or mostly text, a language model is often the better tool.
WHAT WE BUILD
Machine Learning Development Services
Our machine learning development services cover the model, the data pipeline that feeds it, and the connection that puts its predictions to work.
Forecasting and demand prediction
Models that forecast demand, sales, or inventory from your own history, with the forecast delivered to the planning tools and AI-powered analytics your teams already use.
Anomaly detection
Models that watch transactions, orders, or sensor readings and flag the ones that don't fit the pattern, so a person reviews them before they turn into losses.
Classification and scoring
Models that sort records into categories or score them, such as routing support tickets or ranking leads, with low-confidence cases sent to a person.
Recommendations and product search
Recommendation models that suggest products based on past behavior and rank search results for online shoppers.
Computer vision
Image and video recognition for tagging, inspection, and content moderation, using services such as Amazon Rekognition or hosted vision models on Amazon Bedrock.
Data pipelines for machine learning
The pipelines that clean, join, and refresh training data, built as part of our data engineering work on a data warehouse or data lake.
HOW IT WORKS
How a Machine Learning Project Runs
A machine learning project runs in five steps, from the business question to a monitored model in production. The data check can end the project early, before most of the cost is spent.
STEP 1
Define the decision
We name the decision, how often it happens, and what a wrong prediction costs.
STEP 2
Check the data
We test whether the history is large, clean, and labeled enough to learn from.
STEP 3
Build a baseline
We train a simple model first and compare it with how the decision is made today.
STEP 4
Deploy
The model goes into production behind an API, connected to the systems that use it.
STEP 5
Monitor and retrain
We track accuracy on new data and retrain the model when the pattern shifts.
Where MLOps fits
MLOps consulting covers the work that keeps a model useful after launch: versioning, automated retraining, accuracy monitoring, and rollback when a new version does worse. Models drift as markets and customers change. On AWS, Amazon SageMaker handles much of this work.
What your team gets at handoff
Whatever we build, you own, including the model, the training code, and the pipelines. Services we can provide at handoff include knowledge transfer and documentation on how to retrain the model and read its monitoring. Ongoing support is also available.
RESULTS
Results From Pre-Trained Models
Neither project below trained a custom model. Both put pre-trained models to work inside software JBS Dev built, on Amazon Bedrock for a PropTech startup and on AWS machine learning services for a healthcare support services company.
85-90%
Forecast accuracy on Amazon SageMaker
Manufacturing
Generative AI Sales Forecasting for a Manufacturer
Challenge: Traditional statistical forecasts kept falling short, causing excess inventory or missed sales.
Result: A generative AI time-series model on Amazon SageMaker reached 85 to 90% forecast accuracy.
Read the manufacturing forecasting case study10x
Former traffic, handled on serverless AWS
HR Technology
Scaling Fama Technologies' Screening Platform on AWS
Challenge: Fama's managed servers were expensive to scale, and every new server took manual work.
Result: A serverless AWS architecture handles at least ten times the earlier traffic and lowered infrastructure costs.
Read the Fama case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
TECHNOLOGY
Machine Learning Technologies
We choose tools for the problem. The machine learning technologies we work with include Amazon SageMaker and SageMaker Data Wrangler, Amazon Rekognition, Keras, TensorFlow, and Python data science libraries such as pandas and scikit-learn.
A trained model is often one tool among several. An AI agent can call a forecasting model when someone asks a what-if question. Our complimentary half-day SageMaker Data Wrangler Accelerator walks through feature engineering and a training job on sample data.
See the SageMaker Data Wrangler AcceleratorMODEL PIPELINE
Data warehouse
Amazon SageMaker
Deployed model
WHERE PREDICTIONS GO
AI agent
Calls the model as a tool
People
Review and act
COST AND TIMELINE
What Drives Machine Learning Cost and Timeline
Machine learning cost and timeline depend mostly on the state of your data and on how the predictions will be used. Six factors drive most estimates:
- 01Data quality and volume
- 02Labeling work
- 03Model complexity
- 04Integration with existing systems
- 05Accuracy and explainability requirements
- 06Monitoring and retraining after launch
How the timeline is set
Timelines depend on the data, and the assessment sets them. The data check comes first, because it decides whether a model can reach useful accuracy. A simple baseline comes before a complex model, so you see results early.
How cost is set
We don't quote a number before we understand the data and the decision. The assessment turns the factors above into an estimate for each stage. If a model won't pay for itself, or a BI query would do the job, we'll tell you.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose a Machine Learning Partner
A good partner will tell you when a model isn't the answer. Ask these four questions, and here is how we answer them.
- Will they tell you when you don't need machine learning?
- Yes. Some problems need a trained model. Others are better served by a language model, a BI query, or a simple rule, and we'll tell you plainly which one fits.
- Who builds the model?
- Senior engineers. Engagements are led by senior engineers and technical leaders who can make architecture, integration, and security decisions directly. That means fewer handoffs, faster decisions, and the people you meet in the first call doing the work.
- What happens after the model ships?
- Models drift as the business changes, so someone has to watch accuracy and retrain. We can provide ongoing support, or your own team can take over with knowledge transfer and documentation from us.
- Where is the team?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work. From the first call, you will know exactly who is building, deploying, and supporting your model.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Machine Learning FAQs
What do machine learning consulting services include?
They cover finding where a model can improve a decision, checking whether your data can support it, building and testing the model, deploying it behind an API, and monitoring it after launch. Ours send predictions to the ERP, CRM, or dashboards your teams already use.
What's the difference between machine learning and generative AI?
Machine learning learns patterns from your own historical data to predict a number or a category, such as next month's demand. Generative AI uses large language models, trained by someone else, to read and write text, images, or code. Many systems use both.
How much data do we need for machine learning?
It depends on the model type and the question. A forecast needs enough history to show seasonal patterns, and a classifier needs enough labeled examples of each category. Machine learning is sensitive to bad data and small volumes of data, so the assessment checks quality and volume before anyone promises an accuracy number.
How long does a machine learning project take?
It depends on the state of the data and on how the predictions will be used, and the assessment sets the timeline. A simple baseline model comes first, so you can compare it with today's process early. Connecting predictions to existing systems is planned from the start.
How much does machine learning development cost?
It depends on data quality and volume, labeling work, model complexity, integrations, accuracy and explainability requirements, and monitoring after launch. We don't quote a number before we understand the data. The assessment turns those factors into an estimate for each stage, and we tell you if a model won't pay for itself.
What is MLOps?
MLOps is the practice of running machine learning models in production. It covers versioning models and data, automating training and deployment, monitoring accuracy on new data, and retraining when patterns change. Without it, a model that tested well slowly gets worse as the business moves, and nobody notices until decisions suffer.
Which machine learning platforms do you use?
We choose tools for the problem. The technologies we work with include Amazon SageMaker and SageMaker Data Wrangler, Amazon Rekognition, Keras, TensorFlow, and Python libraries such as pandas and scikit-learn. Where a pre-trained service such as Amazon Rekognition or Amazon Comprehend already does the job, we use it instead of training a custom model.
Who owns the model and the data?
You do. Whatever we build, you own, including the model, the training code, and the pipelines. Your data stays yours, and models trained on it belong to you. Knowledge transfer and documentation are services we can provide at handoff, so your team can retrain and maintain the model.
