Top 10 AI Consulting Companies to Consider in 2027
Key Takeaways: AI consulting companies assist startups and enterprises in converting AI experiments into measurable business outcomes with the right
Azure Machine Learning & MLOps Services
When you need to train, manage, and govern your own models, Azure Machine Learning and Azure Databricks are the right tools. We build custom ML and deep learning models with reliable MLOps, and we’ll recommend Foundry agents when they’re a better fit.
Microsoft Solutions Partner (Data & AI) | Azure ML · Databricks · MLflow | Feature stores · endpoints · drift
500+
Customers
•
1000+
Projects
•
250+
Employees
•
26+
Years of Experience
•
Is Azure Machine Learning still relevant with agents and Foundry?
Yes, it is. Microsoft Foundry is most commonly used to build genAI apps and agents using existing pre-built models. It isn’t used for tasks or processes such as training a custom model or handling traditional ML tasks.
Microsoft Azure Machine Learning services and Azure Databricks continue to be important when it comes to training, managing, or tuning a custom model. Azure ML often best suits tasks like model tracking, monitoring, and deployment, whereas Databricks works best for enterprise-scale data and model training.
Azure Machine Learning, Databricks, or Microsoft Foundry?
A common enterprise setup comprises Databricks for data and model training, Azure ML for managing custom models, and Foundry for building AI agents and GenAI apps around them.
Azure Databricks
Typical Use Case:
There are numerous use cases of Databricks; some of the most common ones are Mosaic AI, Unity Catalog governance, Lakehouse-scale data engineering, and ML/GenAI training.
Well suited for:
Azure Databricks is the best choice when it comes to feature engineering, large-scale training, and data-first teams.
Azure Machine Learning
Typical Use Case:
AzureML use cases comprise AutoML, full MLOps, and custom model training.
Well suited for:
It is best suited for classical ML and DL with governed, repeatable pipelines.
Microsoft Foundry
Typical Use Case:
Their use cases include pre-built models, generative AI apps, and agents with proper governance.
Well suited for:
It works best for GenAI & agents – Foundry Models / Foundry IQ.
The Models We Build
AutoML & Custom Models
With the help of AutoML, businesses can speed up initial stages of model development, while custom models make sense when off-the-shelf options don’t fit right.
Deep Learning
When it comes to complex tasks such as image recognition or natural language processing, deep learning powers those apps efficiently.
Recommendation System
These models learn from customer behavior to suggest content, products, or the next best action.
Fraud & Anomaly Detection
Models can flag unusual patterns or suspicious activities, helping businesses detect potential fraud or other issues as they happen.
Forecasting
Organizations can use historical data to forecast demand, revenue, inventory needs, and capacity.
Predictive Models
From customer churn and credit risk to pricing and demand, these models assist businesses in making more informed, data-backed decisions.
The MLOps Lifecycle We Deliver
A model only turns out to be useful when it can work reliably in production. We develop the processes necessary to efficiently deploy, manage, monitor, and update models over time.
Prepare data and create reusable features with Fabric or Databricks, keeping training and production data consistent.
Train models, test different approaches, and track experiments with MLflow so results are easier to compare and reproduce.
Version models and manage approvals, access, and model history through MLflow and Unity Catalog.
Deploy models via online endpoints for real-time predictions or batch endpoints for large-scale scoring.
Track model performance, data quality, and drift, with alerts when something needs attention.
Set up automated retraining and CI/CD pipelines to keep models accurate as data and business needs change.
Azure Machine Learning, Databricks, or Microsoft Foundry?
A common enterprise setup comprises Databricks for data and model training, Azure ML for managing custom models, and Foundry for building AI agents and GenAI apps around them.
Work with data
Azure Databricks:
It handles large datasets, data processing, feature engineering, and model training.
Azure Machine Learning:
Azure Machine Learning services work with prepared data and features to create and train models.
Management of models
Azure Databricks:
Databricks uses Unity Catalog to seamlessly manage data, models, and access.
Azure Machine Learning:
It provides model registration, versioning, access control, and governance.
Build with GenAI
Azure Databricks:
Databricks supports fine-tuning, model serving, vector search, and GenAI workflows via Mosaic AI.
Azure Machine Learning:
AzureML connects with Foundry to bring custom models into GenAI and agent-based apps.
Put models into production
Azure Databricks:
Databricks deploys models using Mosaic AI Model Serving.
Azure Machine Learning:
It provides managed online and batch endpoints for model deployment.
Best fit
Azure Databricks:
Databricks is best suited when the main focus is on data, analytics, and large-scale training.
Azure Machine Learning:
It is a strong choice when the priority is model deployment, management, and MLOps.
Build Features Once, Use Them Every
Reusable Features
Create features once and reuse them across various models and teams, instead of recreating the same feature logic repeatedly.
Consistent Training and Serving
Use the same feature definitions during training and live predictions to avoid differences that can affect the model’s accuracy.
Fast, Real-Time Predictions
Keep frequently used features ready for quick access, with online serving and autoscaling for real-time workloads.
Keep AI Secure, Responsible, and Cost-Effective
Responsible AI & Governance
Support fairness, explainability, documentation, approvals, and secure access with tools such as Unity Catalog, Azure ML, Entra ID, and Key Vault.
Smarter Compute & Cost Management
Use the right compute resources, automatic scaling, and scheduled shutdowns to keep model training and deployment expenses under control.
Bring Custom Models into Real Business Work
Trained models only make a difference when they actually support workflows in real time and help streamline them. Custom models can be connected easily to Microsoft Foundry agents and GenAI apps through endpoints. For instance, a vision model can review any claims, or a forecasting model can support a planning assistant. This helps to bring traditional ML and agentic AI under one roof.
Where Predictive ML Creates a Difference
Dedicated AI Experts
Leverage historical data to predict future sales, demand, inventory, and other business needs.
Understand Customer Churn
Identify customers who may leave or show buying intent, helping teams take action at the right time.
Catch Fraud and Unusual Activity
Assess transactions and other real-time data to flag suspicious or unusual patterns.
Predict Equipment Problems
Use IoT and machine data to detect early signs of equipment issues and lower downtime.
Assess Credit and Risk
Develop explainable models for credit decisions and risk assessment, with the governance necessary for sensitive use cases.
Personalize Customer Experiences
Use customer behavior and preferences to suggest relevant products and content.
Ready to Put Your Models to Work?
Whether you’re starting with a new model or moving an existing one into production, get the right platform, deployment approach, and MLOps setup for your use case.
Client Success Stories
Scroll
Latest Blogs
AI in Supply Chain: Use Cases, Benefits and Implementation Guide in 2027
Key Takeaways: AI is transforming supply chains from reactive to proactive. AI supports intelligent warehousing and maintenance. With the help
Meta Muse for Business: AI Agents & Workflow Automation
Muse can already send an email, manage your calendar, shop online, and book travel or appointments. For organizations, Meta Muse
Frequently Asked Questions
If you want to train and operate your own models with full MLOps, use Azure Machine Learning services and Databricks. For pre-built models and agents, use Microsoft Foundry, although it helps to keep in mind that it doesn’t provide broad training compute or tabular AutoML. Most organizations use both; our experts help you decide as per your use case.
Databricks is a good fit for working with huge volumes of data and training models at scale. Azure ML, on the other hand, primarily focuses on the management, deployment, and monitoring of ML models. Most firms use both together—Databricks for data and training and Azure ML for MLOps and deployment.
We build Classical ML (classification, regression, clustering), forecasting, deep learning (computer vision, NLP), recommenders, and anomaly/fraud detection.
Of course, we do. We offer feature stores, MLflow tracking, model registry/Unity Catalog, CI/CD, and managed online and batch endpoints.
It helps you define features as reusable resources, materialize them on a schedule, and serve them with low latency online, thereby enhancing training or serving consistency. It turns out valuable once more than one model or team shares features.
Use online endpoints for fast, real-time predictions and batch endpoints for processing large volumes of data. AutoML models can also be deployed without writing extra scoring code.
Our team monitors data/prediction drift, data quality, performance, and responsible AI signals, with alerts and automated or scheduled retraining.
Yes, we expose models through endpoints so Foundry agents and genAI apps can call them.
Training and data stay within your Azure or Databricks environment. Security and access are managed via tools like Entra ID, RBAC, Key Vault, and Unity Catalog or Azure ML.
Yes, LLMs, or simply large language models, can be easily fine-tuned and deployed with the help of tools such as Databricks Mosaic AI. They are also connected to retrieval systems and AI agents, while Foundry models can be used when a ready-made model is the best fit.