top of page
Depositphotos_199947958_DS.jpg

AI/ML
 

AI Readiness Review

Comprehensive assessment of an organization's readiness to adopt and implement of AI 

Generative AI

Intelligence techniques and models that are designed to generate new data

Text Analytics

Extracting valuable insights & information from unstructured text data

    ML Operations

Streamline and automate the end-to-end machine learning (ML) lifecycle

AI Readiness Review

AI Readiness Review

Strategy

  • Evaluate the alignment of AI initiatives with overall business goals.

  • Assess the clarity and specificity of AI strategy and objectives.

  • Ensure that AI projects have a well-defined business case and ROI expectations.

Generative AI

Generative AI

Depositphotos_321314904_DS.jpg

  • Data preprocessing services to clean, format, and structure the training data.

  • Data augmentation techniques to increase the diversity and quality of the training dataset

  • Data labeling services to annotate training data, especially for supervised or conditional generative tasks.

  • Crowd-sourced or outsourced labeling services for larger datasets.

Text Analytics

Text Analytics

Depositphotos_501508032_DS.jpg

  • Data ingestion: Ingest a diverse and representative set of text data sources, including documents, emails, social media posts, customer reviews, and more.

  • Tokenization: Text preprocessing for tasks like tokenization, stemming, lemmatization, and stop-word removal to clean and prepare text data for analysis

ML Operations

ML Operations

Depositphotos_425549826_L.jpg

  • Set up CI pipelines to automate the testing, validation, and building of ML models whenever changes are pushed to the code repository.

  • Ensure that code changes are automatically tested against defined metrics and quality standards.

  • Implement CD pipelines to automate the deployment of ML models to production environments.

  • Deploy models seamlessly while monitoring their performance and health

bottom of page