Platforms > Azure Machine Learning

Azure Machine Learning Consulting

Alphabyte offers Azure Machine Learning consulting. We help mid-market and enterprise organizations set up Azure Machine Learning, train models on their own data and put those models into production, connected to the systems and reports your team already uses.

We own the work from use case to monitoring, then document it and train your team so you can run it.

Success Stories

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What We Do

Azure machine learning consulting that takes models from first experiment to reliable use in production.

Azure Machine Learning is Microsoft's cloud service for building, training, deploying and monitoring machine learning models. It gives data scientists and engineers managed workspaces, compute, a model registry and tools such as AutoML, inside the same Azure security and access controls as the rest of your data. Running it reliably in production is where most teams need help.

Clients usually come to us with a model that works in a notebook but not in the business, or with a clear question and no one to build the answer. We set up the workspace, prepare the data, build and validate models, and connect predictions to your applications, warehouse and Power BI reports. For a plain-language overview of forecasting, read our guide from data to forecasts. The wider analytics work around a model sits with our AI-powered analytics service.

For some projects a custom model built through our Python development work fits better, and for language tasks we work with models such as Azure OpenAI and Claude.

01

Azure ML Implementation & Setup

Workspaces, compute, data stores and environments configured for governed model development.

02

Custom Machine Learning Model Development

Classification, regression, forecasting and anomaly detection models trained on your own data.

03

Automated Machine Learning (AutoML)

AutoML runs that compare model types and settings to find a strong baseline quickly.

04

MLOps & Model Deployment

Automated training, versioning and deployment to real-time or batch endpoints.

05

Predictive Analytics Integration

Model scores delivered to your applications, data warehouse and Power BI dashboards.

06

Azure Data Factory & ML Pipeline Integration

Data Factory pipelines that feed training data in and return predictions automatically.

07

AI Strategy & Use Case Identification

Machine learning opportunities ranked by value, with data readiness checked and a roadmap.

08

Model Monitoring & Ongoing Support

Drift detection, retraining, cost reviews and improvements that keep models accurate.

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Our Approach

  • We meet your stakeholders, review your data sources and agree on the business questions a model should answer. We check whether the data needed exists, how clean it is and how much history it holds. Discovery usually takes 2 to 3 weeks and ends with a short list of use cases worth building first.

  • We choose the approach for each use case and design the workspace, compute, pipelines and deployment pattern. You see how predictions will reach your team, which systems they touch and how success will be measured before any modelling starts. Where a simpler method fits better, we say so at this stage.

  • We extract and clean training data with Azure Data Factory and Azure Machine Learning data tools, then build the features that give each model its predictive inputs. The pipelines are automated and repeatable, so retraining later uses the same steps as the first build and does not depend on anyone repeating manual work.

  • Over about 4 to 8 weeks we train and compare models, using AutoML where it helps and custom Python code where it does not. Experiments and models are tracked in the registry. Each model is tested on data it has not seen and against thresholds agreed with your team before it is approved for use.

  • We deploy approved models to real-time or batch endpoints and connect the results to your applications, warehouse or Power BI reports. MLOps pipelines handle retraining and redeployment. Go-live happens with monitoring switched on and a rollback plan ready, so a problem with a new model version never reaches users unnoticed.

  • We document the model design, training pipelines, deployment settings, monitoring and retraining steps, then walk your data and engineering teams through each one. The goal is that your team can run and extend the environment without us. If you prefer, we can stay on to monitor models and handle retraining as your data changes.

Azure Machine Learning Success Stories

Artificial Intelligence

E-commerce companies are constantly seeking innovative ways to drive revenue growth. One of the most common ways to do this is by running marketing campaigns. However, determining which customers to target with campaigns is often time consuming, manual, and based on messy data.

Artificial IntelligenceRead More

E-Commerce

Annmarie Skin Care’s challenge is common and is experienced by e-commerce shops across the spectrum: while relying on 3rd party applications provides quick access to the functionality necessary to run its shop, it creates scattered environments of data that make it difficult to gather meaningful insights.

E-CommerceRead More

Wholesale Distribution

What AWD needed was 21st century answers to a complex 21st century question. Quarterly P&L reports simply did not make the cut. It left many questions unanswered – what is the net profit on each item or branch? Who are our top sales execs? Who are our top customers and how much profit are we generating from them?...

Wholesale DistributionRead More

Your Azure Machine Learning Consulting Team

Alphabyte is a data and AI consultancy, founded in 2015, and a Microsoft Solutions Partner. Our data engineers, developers and AI practitioners work on one team, so models are built on clean data and connected to the reports and systems where they are used. Our team has delivered machine learning and analytics work for clients in e-commerce, wholesale distribution, financial services and healthcare.

Our team holds Microsoft certifications including Azure Solutions Architect Expert and Azure Administrator Associate. See the full list on our certifications page. We have served 100+ organizations.

We work with mid-market and enterprise organizations. Related services: AI-powered analytics, AI consulting and data warehousing services.

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Frequently Asked Questions

  • Azure Machine Learning is Microsoft's cloud service for building, training, deploying and monitoring machine learning models. It provides managed workspaces, scalable compute, experiment tracking, a model registry, AutoML and endpoints for real-time or batch predictions. Because it runs in Azure, it uses the same identity, security and access controls as your other Azure data services.

  • Azure ML consulting covers the full path from a business question to a model in production. That includes choosing use cases, checking data readiness, setting up the workspace and compute, preparing data, training and validating models, deploying them to endpoints and building MLOps pipelines for retraining. At Alphabyte it also includes connecting predictions to your applications and reports, documentation and training for your team.

  • Most organizations bring in machine learning consulting when a prototype works but nobody can run it in production, when a data science team spends more time on infrastructure than on models, or when leadership wants forecasts or risk scores and no one has time to build them. It also helps when you are unsure whether your data can support a model at all.

  • Cost depends on the number of use cases, how ready and clean your data is, how many systems the predictions must reach and how much MLOps automation you need. Azure compute and storage are billed separately by Microsoft and depend on how often models train and run. We scope each project with you during discovery, often starting with one use case so you can judge the value before going further.

  • Azure Machine Learning is for building and running your own models on your own data, such as demand forecasts, churn scores or anomaly detection. Azure OpenAI gives access to large language models for tasks such as summarizing documents or answering questions in plain language. Many projects use both. We also work with other models such as Claude and recommend one based on the task and your data.

  • A first model in production usually takes about 8 to 14 weeks. Discovery takes 2 to 3 weeks, model development and validation about 4 to 8 weeks, and deployment and handover follow. The timeline depends mostly on data readiness. If training data has to be gathered from several systems or cleaned first, that work comes before modelling.

  • MLOps is the set of practices that keep machine learning models working after launch: automated training, versioning, testing, deployment and monitoring. Without it, models are retrained by hand, if at all, and slowly lose accuracy as data changes. You need it once a model supports real decisions. In Azure Machine Learning we build MLOps pipelines so retraining and redeployment run the same way every time.

  • Not always, but it helps. A model needs clean, consistent history, and many organizations only have that once their data sits in a data warehouse. If your sources disagree or history is missing, we usually fix that first, since a model trained on poor data gives poor predictions. We check this during discovery and tell you whether the data is ready or what needs to happen first.

  • Yes. We write model scores and forecasts back to your data warehouse or database, where Power BI reports can use them like any other field. This way predictions appear in the reports and tools your team already opens every day, instead of in a separate system that people have to remember to check.

  • A single in-house hire suits steady work once the platform and first models are in place. A consulting team suits the start, when you need data engineering, modelling, Azure setup and MLOps skills at the same time. Many clients use us to set up Azure Machine Learning and deliver the first models, then hire in-house, and we help with the handover and training.

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