AI is arguably the most impactful technology development of our generation and has quickly moved from conceptual to immediacy. Organizations are using AI right now to drive measurable business outcomes, innovation and differentiation. It is being used to optimize workforces, and streamline data and processes.
However, as organizations begin to expand the use of AI, we’ve noticed a common pain point. How do you govern your AI environment to maximize outcomes across your organization and avoid AI “silos” throughout the organization?
In our most recent webinar, Concurrency CTO Nathan Lasnoski was joined by Concurrency Data Scientist Swarmi Vankatesh, PhD, and Concurrency Technical Architect Jeff Lipkowitz to discuss how organizations can operationalize and govern their AI systems for successful operations, data analysis, models, and secure environments through Azure.
With every AI solution, a business need must be acknowledged, understood and driven towards. How is a business being structured now, and what pain points can be addressed? Through the Data Science Lifecycle, once a business need is found, data needs to be evaluated. What kind of data do you have? Where is said data stored? Does the data need to be transformed to match the model?
The transformation of data into a robust framework comes when the next stage occurs, modeling, and deploying the created model once it suits the needs of the organization and the needs of the data itself, eventually achieving user acceptance.
“It’s important to think about how an organization keeps the cycle going without any blocks,” noted Concurrency Data Scientist Swami Venkatesh. “At the same time, talk about how hands off can you be from the cycle once you have all of the pipelines and resources in place? You can’t get completely hands off, but you can get to a certain level of automation, and this is where machine learning can come into play.”
There is a lot of flexibility in the Azure architecture and environment for how data can be stored. Whether it’s on prem or in the cloud, data can be pulled from multiple sources and fed through the pipeline to storage using Azure AI. Setting up your pipelines is also contingent upon how you store your data. Concurrency Technical Architect Jeff Lipkowitz suggests holding data in data lakes, which then opens up the Azure ecosystem even more for various formats of data, whether structured, or unstructured.
Azure Machine Learning can be automated and empower the citizen and data scientist using the model created. Data sources are put into experiments, in which various models are tested and then selected to best fit what your organization needs to be done to the inputted data. Once a model is evaluated and selected, it then is deployed and continuously monitored to ensure all variants remain consistent and do not steer away from the main goal of the model.
If your organization is looking to implement AI solutions and govern the environment to maximize outcomes, Concurrency can help you every step of the way. To view the full webinar recording, click here.