General

1. What is xpresso.ai?

The xpresso.ai platform enables a comprehensive approach towards building enterprise artificial intelligence (AI) solutions. With its five-stage process-based cognitive journey (figure below), xpresso.ai delivers AI solutions in a timely and robust manner using a reproducible methodology. The enterprise AI journey starts with use case discovery, and continues through data intake to data preparation, and to cognitive modelling that leads to actionable insights.

2. How does xpresso.ai help data scientists build cognitive solutions?

xpresso.ai provides scalable, reliable, easy-to-use, automated tool kits and accelerators to build useful complex AI solutions with our MLOps environment and microservices repository. For each phase of the journey, xpresso.ai provides accelerators that are built for specific cognitive goals and enterprise-tested with our customers. Depending on the cognitive maturity of the enterprise, xpresso.ai leverages appropriate accelerators to manage the AI/ML model development and production deployment. xpresso.ai is optimized for AI-based analysis and operation-wide environments, from account, packaging, source versioning, data management, and application development to deployment, operations, and monitoring. It also enables additional data engineering capabilities to manage Big Data.

3. What differentiates xpresso.ai?

With the long history of delivering production-grade ML services at Abzooba, we’ve learned that there can be many pitfalls in operating production ML-based systems. Understanding these challenges, we designed xpresso.ai to manage the entire AI/ML application development life cycle using a scalable MLOps architecture. MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). Practicing MLOps means that we advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management. Data scientists can implement and train an ML model with predictive performance on a dataset, given relevant training data for their use case. However, the real challenge is not building an ML model; the challenge is building an integrated ML system and continuously operating it in production. xpresso.ai focuses on enabling these enterprise capabilities to transform the individual analysts’ research into production-capable solutions.

4. How do I get started with xpresso.ai?

To get started with xpresso.ai, we encourage all users to start with a business use case and let us help you productionize your machine learning models on any infrastructure in a 6-week timeframe. - 1 week to Discover, where we will define the business use case, identify the data sources, and design the data science algorithm and framework requirements. - 2 weeks later, you should be ready for Experimentation, where we help you set up the infrastructure, design the solution and the workflow, and bring in data from multiple sources. - The next 3 weeks will be used to Deploy your model to production, where you can train multiple models in parallel and track and compare them. You can then choose the best model and we will help you deploy that in production and set up post-deployment monitoring.

5. Can I run xpresso.ai on premise?

Yes, xpresso is available to be installed on any on-premises infrastructure. We have automated scripts that can help you install it on any Linux-based virtual machines.

6. Does xpresso support installation on cloud services?

Yes, xpresso can be installed on all the major cloud services — GCP, Azure, AWS.

7. How does the xpresso.ai pricing work?

We have flexible pricing for various degrees of use. You can refer to our Pricing Page for further details.

8. Is Python supported in xpresso.ai?

We support all major languages used in machine learning development including Python. xpresso.ai provides out-of-the-box support for Python 3.7 and all later versions. Developers can use older versions of Python with some manual changes to their project files. Please contact the xpresso support team for help.

9. Does xpresso.ai support R?

We do not currently support R, but support will be included in a later release.

10. Does xpresso have GPU support?

Yes, GPUs can be used for deployment of components and pipelines and consequently for running experiments on training pipelines. Please contact the xpresso team for configuring GPUs in your existing installation.