1. AI Implementation Strategy

AI strategies that aligns to your business goals. Our experts have mastered the art of applying AI at scale and will build your custom AI strategy roadmap, proof of concepts, scalable AI infrastructure, and production-grade AI solution deployment.

Depending on your business priorities, we’ll use a unique model methodology to maximize value realization for your AI solution. Through use case selection, applicability and feasibility of implementation, and other important factors we’ll guide you towards a decision where AI rewards your time and effort investment.

Our experts, having spent time at some of the top AI hyperscalers and established AI technology firms, know exactly how to develop, test, and deploy the right technologies to deliver the best value for you.

With our AI Strategy Implementation services, we’ll help you realize and identify the highest value opportunities from implementing AI solutions into your business.

Our AI implementation strategy details:

• AI strategy roadmap – validate feasibility of using AI for identified set of use cases, analysis of data availability, and possibility of data monetization
• AI technology implementation – identify the right toolset for your AI initiatives, and establish methods and processes to achieve reliable MLOps and ModelOps

2. Data Engineering

Your data is your most valuable asset. With our extensive experience, and technical expertise with emerging technologies, we’ll build a well-defined and integrated approach to identifying, managing, and using your internal and external structured and unstructured data.

Enterprises understand that data is one of the most valuable assets they have. It’s the key driver for the implementation of AI solutions that provide predictable outcomes. To get that implementation right, you need the right quality and quantity of data—in the hands of our experts—to train your AI models, including:

• Identifying the right toolset for ingesting and processing your data
• Automating and implementing data pipelines for your models
• Identifying the need for synthetic data and create solutions for generating data

We understand the importance of data diversity, security, and privacy to ensure enterprise production-grade quality for your AI deployments.

With our AI Data Engineering services, we’ll help you realize and identify the highest value opportunities from implementing AI solutions into your business.

Our data engineering tools include:

• Data warehouse: BigQuery, PostgreSQL, Azure Synapse Analytics, Amazon Redshift
• Data quality: Great Expectations, Google Dataplex, AWS Glue
• Data analytics: Mode, Google Data Studio/Looker, Amazon Kinesis
• Data pipelines: Vertex AI pipelines, Cloud Dataflow, AWS Data pipelines
• Data pipelines schedulers: Airflow, Cloud Scheduler, Amazon Codepipeline

3. ML Operations

Discover automated processes that scale. Our experts can help you automate your models for continuous, repeatable processes and free up your resources for building and training—eliminating infrastructure setup, management, and maintenance concerns.

A key machine learning best practice is establishing scalable, repeatable, continuous, automated processes so productivity is spent on building and training models, not on infrastructure setup, management, and maintenance.

Our experts can automate the entire process of model development, training, testing, validation, and deployment for your business—identifying the right technology set and implementing a robust solution that effortlessly scales to your business requirements.

Our MLOps tools include:

• ML feature monitoring: Arize, Great expectations, Amazon Cloudwatch
• ML model monitoring: Arize, Vertex AI model monitoring, Sagemaker Model monitor

4. ML Model Development

The right model makes all the difference. With our significant experience in building, training, and deploying AI models, we know how to take the right data and produce accurate, usable results for lifetime value for your business.

When it comes to model development, establishing the value of invested effort is critical in your AI implementation journey. Your business goals may require simpler prediction models using established technologies, or emerging technology implementation and complex model development. Regardless, we take into account your full life cycle view to make sure the models we develop and deploy for your business provide value for years to come.

With our ML Model Development service, you’ll get accurate, usable results from your models—built, trained, tested, and deployed using best-in-class open source and commercial tools.

Our model development, training, testing, and deployment tools include:

• ML version control: DVC, Pachyderm
• Hyperparameter optimization: Oputna, SigOpt, Vertex AI, Amazon Sagemaker, Azure ML
• ML pipeline scheduler: Airflow, Kubeflow, Amazon Sagemaker pipelines, Azure ML pipelines
• ML training: Vertex AI, Amazon Sagemaker, Azure ML