Essential Roles for AI Success in Manufacturing

Essential Roles for AI Success in Manufacturing

Rick Oppedisano, president and CEO of Delta Bravo AI, shares key takeaways from his experience helping manufacturers make the most of the mountains of data they collect. He lists the three key roles he considers foundational to ensuring predictive analytics goes to work to optimize product assembly.

Delta Bravo awarded coveted SCRA Grant

Delta Bravo awarded coveted SCRA Grant

Summerville, SC

delta bravoDelta Bravo was chosen from over 100 select South Carolina startups to receive the SCRA’s prestigious Acceleration Grant. “We are excited to approve a $50,000 Acceleration Grant for Delta Bravo,” says SCRA Executive Director Bob Quinn. These grants are awarded to early-stage companies that are demonstrating fast growth and promise in their market. Delta Bravo’s product and leadership team are innovating with machine learning and artificial intelligence. We look forward to seeing their impact on South Carolina.”

SCRA is a state-chartered organization that fosters job creation and grows South Carolina’s Innovation Economy. Through SCRA’s Entrepreneurial Programs, early-stage companies are provided mentoring and grants and may be eligible for an investment from SC Launch, Inc. The program is open to qualified South Carolina companies in the Advanced Materials/Manufacturing, Information Technology and Life Sciences industries.

“We are honored and excited to receive this award,” says Delta Bravo CEO Rick Oppedisano. “SCRA’s choice validates our solution and continues to solidify our relationship with the state of South Carolina.”

###

About SCRA

Chartered in 1983 by the State of South Carolina as a public, non-profit corporation, SCRA fosters South Carolina’s Innovation Economy by supporting entrepreneurs, enabling academic research and its commercialization, and connecting industry to innovators.

 

Delta Bravo Presenting At Charlotte Bots and AI Meetup

Delta Bravo Presenting At Charlotte Bots and AI Meetup

Delta Bravo CTO John McAliley and CEO Rick Oppedisano are presenting “3 Lessons Learned in Developing a Production AI Platform” to the Charlotte Bots and AI meetup group on July 11, 2018.

“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.”

REGISTER TODAY

Lessons Learned from Developing a Production AI Platform

Wednesday, Jul 11, 2018, 6:00 PM

Industry Co-Working
1000 NC Music Factory Blvd. Suite C6 Charlotte, NC

12 Bots Attending

*** ATTENTION – Parking is FREE. You may park on the street if available or Deck #1 at AvidXchange. If there are any events, inform the guards that you are attending a Tech Meetup and they will direct you to Deck #1 *** Hello Everyone! Welcome to the July Charlotte Bots and AI meetup. This meetup our presenters from DeltaBravo.ai talking about “Les…

Check out this Meetup →


Feedback from Other Delta Bravo presentations:




This Is Your SQL Server on Machine Learning

This Is Your SQL Server on Machine Learning

delta bravo, sql server machine learning, database machine learning, ai

Applying Machine Learning models to database management turns the old paradigms upside down. Folks of a certain age remember the old “this is your brain on drugs” commercials from the 80s. For this post, we are going to borrow from this analogy to observe your SQL Server on Machine Learning.

What Is the Benefit of Applying Machine Learning Models to SQL Server?

Machine Learning enables you to:

  • Predict performance trends, capacity and potential security and/or compliance breaches
  • Correlate system spikes and/or anomalous behavior to specific events, actions and code
  • Model all possible fixes and identify the remediation that has the highest likelihood for success

The Power of Influence

It all starts with understanding what factors within the database itself influence each other. This varies with each use case and is influenced by business requirements, maintenance patterns and available system resources. Basically, databases are like people. Would you expect your doctor to prescribe the same medication for three random people just because they share the characteristic of being human?

Delta Bravo’s machine learning algorithms track the relationships between critical performance metrics for each SQL Server database. Here is a heatmap that shows, for this particular database, what metrics influence each other the most. High influence is reflected by a positive number and dark red tones, no influence is zero and gray tones. Negative influence is reflected by negative numbers and black tones.

delta bravo, machine learning, AI for the databaseTranslating Models into Action

For the sake of brevity (further detail is available in our whitepaper), we’re going to focus on the following Use case:

  • Identify a problematic system trend that has NOT reached a threshold*/been alerted on
  • Quantify the trend and verify that trend is going to continue into the future
  • Associate the trend with a specific event, measure impact of event
  • Identify root cause, quantify impact, identify specific action causing impact
  • Provide remediation recommendation

The work you are about to see was performed in 4 clicks (45 seconds) using the Delta Bravo UI. 

Let’s start with a quick view of the Delta Bravo System Health panel for SQL Server Instance DemoSQL-2.

We observe a problematic trend with this SQL Server Instance’s CPU. Is this trend temporary?  Seasonal? Let’s use Predictive Analytics to find out.

We see that the problematic trend is forecasted to continue, growing at a rate of nearly 90% over the next 14 days. However, our system thresholds* have not been hit yet. This means the system is acting in an anomalous fashion. Let’s identify the specific anomalies that are influencing this CPU trend.

delta bravo, predictive analytics for database, SQL Server

In the graphs above, the gray shadow is a machine learning algorithm that represents the “acceptable range” or baseline for system behavior associated with that metric. We see that, while no thresholds have been reached for these metrics, behavior is outside the scope of the baselined “norm.” Why?

By selecting one of the graphs, we’re able to zoom in for more detail. The Blue lines represent specific Events that influenced the rise in that metric.

delta bravo, SQL Server, machine learning

By selecting the line prior to the large red spike, we see that an Object was altered. This procedure impacted Query behavior adversely. We are able to see the code that was used to alter the Object, as well as the quantified impact this change had on Query performance.

delta bravo, machine learning, AI for the database

Using AI to Recommend and Implement a Fix

From here, the AI runs through a series of possible fixes and identifies which ones will have the highest likelihood of success and prioritizes their impact. In this case, the recommended fix is adding a series of Indexes.

delta bravo, database AI, SQL Server machine learning

Similar workflows are applied to Security, Capacity planning and other aspects of database management. We believe the use case is changing; its no longer about monitoring, daily care and feeding. Using Machine Learning and AI to manage large database deployments helps your best people scale where you need them most, and for your systems to run at peak efficiency and performance.

*Delta Bravo has the ability to set thresholds, but we feel this is a dated and reactive way to monitor/manage system behavior.

Delta Bravo Presenting At Charlotte Bots and AI Meetup

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.”

BOOK US FOR YOUR MEETUP.

Interested in topics like this and looking for qualified, professional and fun presenters? We’d be happy to help!  Click below to connect with us.