Agile is important, but so are hybrid / cloud-agnostic solutions, multiskill, T-shaped
capabilities, and results-based delivery.
Azure, AWS, GCP, Heroku and IBM Cloud connected solutions from an Azure-certified AI Engineer & Data Scientist
Full Stack Data Science Apps
IDC predicts demand for technical expertise to develop, implement, and manage AI apps is expected to grow at a CAGR of 18.4%. And that can't be just a messy python script no-one understands.
From Python Notebooks hooked to cloud and front-ends built with react.js, Streamlit or Dash and hosted on Heroku to ensure a full stack data science experience
Automated Data Ingestion
Data ingestion has gone way beyond importing data from a SQL database or a csv file. ce.tech adopt DataOps continuous integration and continuous delivery (CI / CD) practises to ensure data pipelines don't break the moment an app is deployed.
Architected solutions with automation and data drift in mind
Kaizen-infused Design Thinking
Forward planning in AI plans for plenty of iteration. ce.tech invests time at the start in brainstorming the problem context and potential solutions before delivering a project lifecycle roadmap. kaizen then kicks-in, with progress monitoring.
ROI-focus, UX and workflow-driven design and implementation coupled with continual improvement
We Integrate With Your Ecosystem
People, processes, and tools are the three cornerstones of any best practice IT framework - the same goes for productionizing an AI application stack.
From the outset, we identify go-to stakeholders and MVP requirements to speed up (and secure) API connection to your ecosystem.
And whether the AI solution is served by a thick client application (local install or Jupyter notebook) or a thin client (web browser, Google Colab, React UI) we work with key data storage (Redshift, BigQuery, NoSQL databases etc.) and compute (EC2 instances, Azure VMs, Apache Spark etc.) to serve the underlying AI deployment.