Artificial intelligence has moved from experimentation to production. Machine learning models now drive decisions across industries. Generative AI creates content, code, and insights at scale.

But getting from proof-of-concept to production is where most organizations struggle. Data quality issues. Model governance gaps. Integration challenges. Cost overruns.

The right partner bridges this gap. They bring expertise in Amazon SageMaker, Bedrock, and the full AWS AI stack. They know how to build systems that work—not just demos that impress.

The State of AI and GenAI on AWS in 2026

AWS’s AI portfolio has expanded dramatically. Amazon Bedrock provides access to leading foundation models through a single API. SageMaker supports the full ML lifecycle. Agentic AI capabilities are now available through services like Amazon Q Developer and Amazon Nova Act.

Organizations are scaling GenAI from pilots to enterprise-wide integration. Custom frameworks handle advanced data models. Agentic AI workflows tackle end-to-end tasks autonomously. Responsible AI governance is gaining prominence as regulations tighten.

1. Avenga 

As an AWS consulting partner, Avenga helps organizations stop treating AI like a science project. The firm builds systems that actually make it to production and stay there.

The team uses SageMaker to automate the entire ML lifecycle. Data preparation. Model training. Deployment. Monitoring. Everything connected. Models don’t drift into irrelevance because MLOps keeps them current. AutoML accelerates development so teams don’t reinvent the wheel.

Foundation model integration through Bedrock enables GenAI applications without building everything from scratch. Avenga’s R&D center validates trends before clients invest. Compliance frameworks satisfy regulators without slowing innovation.

AI and ML expertise:

• Builds automated ML pipelines with SageMaker MLOps practices
• Integrates foundation models through Amazon Bedrock
• Implements responsible AI governance frameworks
• Accelerates development with AutoML capabilities

*Organizations seeking production-ready AI systems find a practical partner in Avenga. The firm combines technical depth with a compliance focus.

2. SoftServe

SoftServe holds Premier Tier Partner status with deep AI and ML capabilities. AWS named Ruslan Kusov its Top Ambassador of the Year 2025, recognizing SoftServe’s leadership in cloud innovation and AI.

SoftServe’s AIDeeQ suite brings GenAI and agentic AI into software development workflows. AI agents assist with coding, testing, deployment, and cloud environment management. This makes SoftServe one of the top AWS consulting companies for AI innovation at scale.

The firm helps organizations build and scale machine learning models using AWS AI and ML services. They tackle complex challenges, automate processes, and uncover new opportunities.

AI and ML expertise:

• Builds and scales machine learning models with AWS AI services
• Implements agentic AI in development workflows with AIDeeQ
• Automates testing, deployment, and cloud management
• Earned AWS Top Ambassador recognition for AI leadership

Organizations seeking AI innovation at scale choose SoftServe. The firm’s Premier Tier status and AWS recognition demonstrate capabilities.

3. EPAM

EPAM is a reliable AWS partner for AI and machine learning with 13+ years of AWS delivery experience. The company holds a Strategic Collaboration Agreement with AWS focused on generative AI and AI-native software delivery.

EPAM’s AI Factory multi-tenant blueprint enables rapid AI solution deployment. They helped a global oilfield services company build an ML-driven solution for oil well production optimization.

EPAM’s DIAL platform (available in AWS Marketplace) enables agentic workflow development using AWS Bedrock models. Their AI-DLC methodology compresses development cycles while maintaining quality.

AI and ML expertise:

• Deploys AI Factory blueprint for rapid ML solution delivery
• Builds agentic workflows with DIAL platform and AWS Bedrock
• Implements AI-DLC for faster development cycles
• Holds Strategic Collaboration Agreement with AWS for GenAI

Global enterprises choose EPAM for AI transformation. The firm’s engineering heritage and AWS partnership deliver production-grade AI systems.

Comparison Table: AI, ML, and GenAI Partners

Each partner approaches AI differently. Here’s how they compare.

Company	AI/ML Specialization	Production Readiness	GenAI Capabilities	Governance & Compliance
Avenga	SageMaker MLOps, AutoML	Automated ML pipelines	Bedrock integration, foundation models	EU AI Act compliance, responsible AI frameworks
SoftServe	Agentic AI, GenAI in development	AI-assisted coding, testing, and deployment	AIDeeq suite, GenAI for SDLC	AWS Top Ambassador recognition
EPAM	AI Factory, AI-DLC, DIAL platform	Multi-tenant blueprint for rapid deployment	Bedrock-based agentic workflows	DIAL platform governance

The table shows different AI approaches. Avenga focuses on production-ready systems with governance. SoftServe brings AI innovation recognized by AWS. EPAM delivers AI-native software development at scale.

FAQ

AI projects raise many questions. Here are answers to common ones.

1. What is the difference between Amazon SageMaker and Amazon Bedrock?

SageMaker is a platform for building, training, and deploying custom ML models. Bedrock provides access to pre-trained foundation models through a single API. SageMaker is for custom models. Bedrock is for foundation models.

2. How does Avenga approach AI and ML projects?

Avenga builds automated ML pipelines with SageMaker MLOps practices. They integrate foundation models through Bedrock and implement responsible AI governance frameworks. Their security-first approach ensures compliance with regulations like the EU AI Act.

3. What are the benefits of agentic AI on AWS?

Agentic AI systems perform end-to-end tasks autonomously. They make decisions and interact across multiple systems. Examples include research assistants, email triage, and data management automation. AWS services like Amazon Q Developer and Amazon Nova Act enable these capabilities.

4. What are the key challenges in deploying AI at scale?

Most organizations struggle with the same issues. Data that doesn’t work well together. Models that drift over time. Systems that don’t talk to each other. Bills that keep climbing.

EPAM’s DIAL platform tackles these problems with modular, open-source approaches. Teams can build without being locked in. Innovation keeps moving without governance getting in the way. Avenga focuses on ML pipelines that handle data preparation and model monitoring automatically.

5. How do I ensure responsible AI in my organization?

Set up governance frameworks. Keep humans in the loop. Conduct regular audits. AWS provides guardrails and compliance tools, but technology alone isn’t enough. Partners like Avenga embed compliance at the core of their solutions. This helps organizations meet standards like the EU AI Act without slowing down development.

Key AWS Services for AI, ML, and GenAI

Amazon SageMaker — Build, train, and deploy ML models at scale. Supports the full ML lifecycle from data preparation to model monitoring.

Amazon Bedrock — Access leading foundation models through a single API. Choice of models from AI21, Anthropic, Cohere, Meta, Mistral, and Stability AI. Supports model customization, RAG, and agentic workflows.

Amazon Q — GenAI-powered assistant for business and development tasks. Q Developer creates intelligent coding agents. Q Business handles enterprise workflows.

Amazon Nova — New foundation models from AWS. Nova Canvas creates images. Nova Premier handles complex tasks. Nova Sonics conducts natural conversations.

Agentic AI Services — Amazon Q Developer creates coding agents. Amazon Transform manages IT workloads. Amazon Nova Act manages web browser workflows.

Conclusions

ML projects fail when they stay in notebooks forever. The real value comes from putting models into production where they actually do work.

Avenga understands this. Their consultants build systems that keep models current. They integrate foundation models through Bedrock. They ensure compliance without slowing innovation down.

Different partners suit different AI needs. Evaluate each one against your specific requirements. The right partner moves AI from experiments to business results—that’s what the best AWS consulting services should deliver.