3 Lessons Learned in Building a Production AI Platform from Delta Bravo

3 Lessons Learned in Building a Production AI Platform from Delta Bravo

Modern Devs Charlotte hosts Delta Bravo team for presentation, live ML coding exercise

Delta Bravo‘s John McAliley and Rick Oppedisano presented to a packed house this week for Modern Devs Charlotte‘s monthly meetup at Wray Ward in uptown Charlotte.

“There’s a ton of interest in AI and Machine Learning,” says Oppedisano, “we want to help people understand where they can start and also learn from our journey. The lessons we’ve learned along the way could save them months of technology evaluation and development time and in some cases, thousands of dollars in system costs.”

Delta Bravo, AI for the Database

Start Small with Familiar Tech

The presentation started with some context of how and why the Delta Bravo AI platform was built. This helped articulate a clear AI use case and also defined the differences between data science, machine learning and artificial intelligence. “Determining if you have an AI use case at all is important,” says Oppedisano. “The ability to clarify that use case and establish the measurables behind it is the first step in the process.”

Once the use case is established, the next step is prototyping. The Delta Bravo team recommends to start small and with a technology you’re already comfortable with. “There are a ton of choices out there and it can be a bit overwhelming,” says McAliley. “There’s likely an option in a tech stack that you’re already working with. Start there to save time and focus on a small, controllable and quickly proven use case.”

Delta Bravo, AI for the Database

Sample Use Case, Live Machine Learning Code Exercise

McAliley followed up with a sample AI use case based on Image Recognition and a live Machine Learning code demo designed to show the differences in accuracy based on data quality and model construction.

A model was established based on metrics that were clearly and visibly defined. “Accuracy is synonymous with credibility,” explained McAliley. “This exercise shows how to get the highest degrees of accuracy, as well as where certain compromises exist and where you begin to get diminishing returns on your model.”

Delta Bravo, AI for the DatabaseThe team also shared Delta Bravo’s functional architecture and provide an example of how AI is used within the platform to recognize, quantify and predict an oncoming database performance problem. This helped the audience visualize the differences in and applications of data science (insight), machine learning (prediction) and artificial intelligence (recommended action).

Lessons Learned

Oppedisano and McAliley shared 3 Lessons Learned from their experience, ranging from technical choices to the importance of data. “The quality of the data you are putting into the model is at the heart of everything,” says Oppedisano. “It’s a classic garbage in, garbage out scenario.” The presentation covered some of the more efficient options available for data cleansing and grouping, as well as the importance of representing the data in a clear, visible manner. “In certain scenarios, the AI process presents the data more efficiently than a human,” says McAliley. “Being able to visualize the data itself, then drill down into insight, correlation and action is important.”

Oppedisano and McAliley shared a fourth “Bonus Lesson” about understanding and managing cost projection. “Machine learning requires a lot of processing,” says Oppedisano. “Its easy for cost to start becoming a factor.”

Delta Bravo, AI for the Database

Feedback and Discussion

“Attendee feedback was very positive,” says Oppedisano, “but the real impression we got was how much opportunity there is in this space.” Several potential use cases and solutions were discussed during and after the presentation, with a lively discussion between the attendees and presenters.

“There’s a lot of passion in the AI community at large,” says Oppedisano. “We want to be a part of bringing that passion and knowledge to the local technology community.”

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Delta Bravo Releases AI for Datacenter-wide Predictive Analytics

Delta Bravo Releases AI for Datacenter-wide Predictive Analytics

AI visualizes and projects data growth trends for precise capacity and cost planning in large enterprise and global deployments.

ROCK HILL, SC –(January 3, 2018) – Delta Bravo, the world’s first AI platform for Database Management, released new capabilities to analyze and predict data growth and cost trends at the Datacenter level.

This new feature will be available to all existing Delta Bravo customers on January 3, 2018 and included in all new subscriptions until March 31, 2018. From April 1, 2018 forward, this feature will only be available in Delta Bravo’s Enterprise service tier.

Delta Bravo, AI for the Database

AI Delivers New Precision around Cloud, Cost Planning

“This new capability empowers CIOs and Infrastructure Managers to spot trends in growth and capacity costs from the datacenter to the database,” says Delta Bravo CEO Rick Oppedisano. “At the highest level, they can spot data growth, cost and performance trends for their entire deployment. They can identify what applications and workloads are influencing performance and cost today, then drill into predictive analytics to quantify future costs and growth trends. Our AI identifies specific optimizations that can change these trends, saving money and extending the life of existing infrastructure.”

Companies evaluating cloud deployments on platforms like Microsoft Azure can also benefit. “Most people don’t know what a DTU is or how to quantify it,” says Oppedisano. “New terminology like this can slow cloud adoption down. Delta Bravo identifies trends driving DTU growth and can help predict the impact on cost as data grows. With this information, its easier to project cloud costs and identify which workloads would make the most financial sense to move to the cloud.”

Data Growth, Shortage of Experts Driving AI Use Case

Explosive data growth is driving unprecedented cost and risk in the datacenter, and there simply aren’t enough people to keep up. Oppedisano explains, “Integration of data into cloud applications, warehouses and other analytics platforms has created a huge maintenance need at the database. That’s left enterprise IT Service Operations teams – whose job it is to manage mission critical applications and infrastructure – struggling to catch up and keep their businesses running smoothly. Our vision of using machine learning to automate and scale Data Service Operations matches the needs of our customers, whether they’re startups, enterprises or large Managed Service Providers.”

About Delta Bravo

Delta Bravo is the world’s first Artificial Intelligence platform for Database Management. Delta Bravo handles the time-consuming work of database management, like trend analysis, performance tuning, security and compliance audits. Delta Bravo’s mission is to help technical resources resolve database issues 90% faster than conventional tools and save users hundreds of hours per year in maintenance time.

Delta Bravo uses data science to analyze massive volumes of database activity and turn them into actionable insights. Our AI Platform observes patterns in customer systems that help us understand how data flows, how it’s being maintained and the impact of its growth on surrounding applications and systems. From these observations, we make specific, actionable recommendations tailored to each individual customer.

Founded in 2016, Delta Bravo is backed by PGE Capital and is headquartered in Rock Hill, SC. For more information visit https://deltabravo.ai or follow Delta Bravo on Twitter @deltabravoapp.