Cloud Pak for Data: The Developer’s journey in a data and AI platform
Do you remember when the IBM Deep Blue computer became the first machine to beat the reigning chess champion Garry Kasparov in a six-game match? That was considered AI a couple of decades ago, when scientists viewed chess as a meter stick for AI because it was “a game that required strategy, foresight, logic—all sorts of qualities that make up human intelligence.”
Since then, advancements in AI have spanned across varied industries and functions. Increases in capabilities for computer processing, power, and storage have contributed vastly to AI development, benefiting businesses and economies through increased productivity, automation, and innovation. Today, although worldwide investments in AI continue to rise, the job of building AI is still challenging.
A recent Global Developer Population and Demographic Study shows 23 million developers worldwide in 2018 growing to 27.7 million in five years. According to the study, 54 percent of them developing in cloud-native environments more than 50 percent of the time and over 70 percent of them are expected to use AI or machine learning (ML) within the next 12 months. That’s 13 million developing in cloud native environments and 17 million of them developing AI or ML solutions.
These ML developers and data scientists are expected to transform their businesses and optimize their solutions by unlocking the hidden insights within their organization’s data quickly, but they are getting stuck in experimentation. The four key challenges holding them back from achieving their goals are:
- Managing data and quality
- Collaborative model development and evaluation
- Keeping models fresh in production
- Trusting the model suggestions sufficiently to operationalize and infuse AI throughout their applications.
IBM Cloud Pak for Data offers the foundation to solve these four key challenges and open up this development landscape to allow developers to be productive in a short amount of time. Cloud Pak for Data is an open, extensible cloud-native platform based on Red Hat OpenShift with pre-integrated data management, governance, data science and analytics capabilities.
This unified end-to-end platform delivers these data and AI capabilities as container-based microservices that help to power new and existing enterprise applications to run on cloud or on-premises. The platform makes it easy to implement data-driven processes and operations and, more particularly, to operationalize the development of ML models and their deployment.
Let’s go through the key steps:
Prepare data with DataOps: Enable self-service access to data, automate data discovery and classification, and integrate data governance and quality regardless of where your data resides to turbocharge your AI/ML applications with a business-ready data-pipeline.
Build and train at scale: Increase productivity both visually and programmatically while developing algorithms and training models with a choice between code or no-code tools. Use rich capabilities to fine tune models and automate the feedback loop to continually adapt to changing conditions and become smarter over time.
Run with ML Ops: Enable easy model deployment, model re-training and ongoing model management with versioning support to feed and manage ML models in your production apps on an ongoing basis.
Manage with trust: Measure model accuracy, detect and automatically mitigate bias at both build and runtime to ensure fair outcomes, and explore the factors that influenced an AI outcome.
Ready to get started? Explore the resources below to start your Data and AI journey on Cloud Pak for Data.
- Cloud Pak for Data: Developer Hub: A hub for tutorials, videos, and self-paced hands-on learning series to accelerate and improve your productivity to build anywhere, extend, deploy, and infuse AI into your applications. The code patterns provide building blocks of curated packages of code in GitHub repos including documentation and assets that address different industry verticals and use cases to create specific solutions quickly.
- APIs and SDKs: The Cloud Pak for Data platform functionality is also accessible through open-extensible APIs and SDKs (in different languages like Python, Node, Go, Java, and more) so you can access and integrate on the platform with focus on governance, ML, Ops, and uinfusing AI into your applications.
- Open source and toolkits: The Cloud Pak for Data platform is open by design and built on a cloud-native architecture including containerized workloads, microservices, and multicloud provisioning. The platform includes open source tools such as Jupyter Notebooks and RStudi, open source software such as Apache Spak as a service, Python (Anaconda) and R, and open source databases like MongoDB, PostgreSQL, Apache Hadoop, and more. The platform also includes visual coding aids such as Data Refinery (dplyr) and Neutral Network Modeler (Tensorflow, Keras, Caffe, PyTorch). Cloud Pak for Data supports the major GIT frameworks - Github, GitLab—so you can collaborate and import/export the projects you're working on.
Cloud Pak For Data V2.5 introduces an open source governance service built into the platform to easily locate and access approved open source packages used across your enterprise, submit requests for additional packages, and review vulnerabilities to assess risk.
To help advance the theory and practice of responsible and trustworthy AI -
- Check out the AI Fairness 360 Open Source Toolkit, a rich open source library to allow model builders to investigate and fix bias in models.
- Use and contribute to the AI Explainability 360 Open Source Toolkit, the state-of-the-art algorithms that support the interpretability and explainability of ML models.
A journey of a thousand miles starts with a single step. Visit the Developer Hub and Cloud Pak for Data community for developer resources or try out Cloud Pak for Data at no cost. For more product details, visit our website or schedule a consultation.