Tag Archive for: Machine Learning

Ironside’s Take30 with a Data Scientist series was typically targeted towards business leaders, with topics focused on strategy, including use-case development advice, de-risking AI with Data Science-as-a-Service, and ways to overcome common barriers to AI adoption. We also covered technical concepts like Model Evaluation and Feature Store Development. On top of that, we took several deep dives into technology partners including IBM Watson Auto AI, AWS Sagemaker Studio, Snowflake and DataRobot. Finally, we had a couple industry spotlights where we explored common use cases in Higher Education and Insurance.

Several attendees have shared that these sessions bridge the gap between the technical world of Machine Learning and that of their business, which in turn has helped them to know how to bridge that gap within their own organizations. For technicians, it has helped them to understand how to talk to the business and draw out use cases and help the business adopt solutions. For the business leaders, it’s helped them know what to ask of the data science team or what to look for in building a team. 

Overcoming the Most Common Barriers to AI Adoption (2/25/21)

Because so many organizations are in the early stages of AI Adoption, this is likely the most important topic to CIOs and business leaders in the Data Science series. This session discusses the challenges with people, infrastructure, and data that every organization faces and offers sound advice on how to overcome them.

Is Data Science-as-a-Service Right for your Organization? (5/19/20)

AscendAI, Ironside’s Data Science-as-a-Service, provides many benefits to organizations that are in the early or mid-stages of AI Adoption. Learn more about Ironside’s offering and how it could reduce your time to ROI to as little as 12 weeks.

How Snowflake Breaks the Chains Holding Your Data Science Team Back (9/10/20)

We hosted a number of Technology related secession with Partners such as Snowflake. This session dove a bit deeper than Data Science Best Practices: Feature Stores. Other Technology related sessions include Watson Studio, AWS Sagemaker, and a data enrichment session with Precisely, titled More Data, More Insight: The Value of Data Enrichment for Analytics.

Data Science work requires infrastructure that is scalable, cost-effective, and with easy access to multiple data sources. Snowflake provides this and much more to a data science tech stack. It also integrates easily with other machine learning platforms like DataRobot, AWS, and Azure. Snowflake is particularly valuable for data sharing with external data sources.

Leveraging Data for Predicting Outcomes in Higher Ed (6/30/20)

We hosted an industry-related session sharing how Higher Education is leveraging machine learning in very creative ways; this ended up being one of our top attended sessions for the Take30 series. In this webinar, we reviewed some of the ways that higher ed is using machine learning such as enrollment management, space planning and student retention. We also discussed some of the use cases that are helping universities cope with the challenges and nuance of COVID-19. We also hosted another industry specific session on Insurance.


As we continue our Take30 with a Data Scientist series, we’ll continue to partner with experts in Machine Learning technology to offer demos and successful solutions as well as strategic sessions for business leaders. We also hope to spotlight some of our clients this year and the exciting AI driven applications we are developing for them in Retail, Insurance, Higher Ed, and Manufacturing. Coming up on May 20th, we will be hosting an industry focus for Banking. 

We’d love to have 1-on-1 conversations to discuss any challenges you may be facing with AI adoption. Please feel free to sign up for a spot with Pam Askar, our Director of Data Science.

So you’re thinking that 2021 is the year to infuse Artificial Intelligence/Machine Learning (AI/ML) into your business. You’ve read about the difference it’s making in other organizations. You want to beat — or keep pace with — your competitors. But where should you begin? 

Should you license AI/ML software? How do you find the right business problem to solve? And if you’re like most organizations, your data is imperfect. Should you focus there first? 

Ironside can help. We’re a data and analytics consulting firm with a track record of helping companies get started with AI.

Let’s start with four things we think every organization should consider on their AI journey. They’re not the only four things you need to know, but we know your time is valuable, so let’s start here:

  1. Develop an AI Use Case Catalog – One of the first places you’ll start is to develop a list  of possible business problems, opportunities or challenges that AI might improve. We assist our customers in building that list by talking to executives and functional area leaders and understanding the organization’s strategic goals, how they are measured and then considering challenges/pain and what information is missing that would improve decision making. The catalog of use cases should be enhanced with information from a thorough data analysis. Is there the data to support the use case? Is there enough of it? What’s the data quality? Can you forecast improvement of an important metric? What’s the return on investment? 
  2. Involve a variety of stakeholders. Executive sponsorship in some form is critical in funding and executing an AI project. But building a culture of AI across an organization starts by involving as many stakeholders as possible across functional areas. Even if the use cases surfaced by some stakeholders are not immediately pursued, people want to be included and to have a voice. Broader involvement will avoid roadblocks and seed a culture of AI. Organizational success will grow over time. 
  3. Start small – Rank the use case catalog and find one or two to test. Identify the relevant business sponsor and data and prepare a limited set of data, or features. Build a simple machine learning algorithm to see if there are results that show that AI could improve a desired outcome. If not, move on to the next use case in the catalog (see, that’s why we need a catalog). If the early results are promising, move on to building a more advanced machine learning model.  
  4. Limit your investment – For as low a cost as possible, get a model deployed and start using it in the business to begin to get the benefit. You’ll inevitably iterate on that model but expediting that process and limiting the investment — and the risk — is the goal. Now here’s where we answer the questions about hiring a data scientist or buying software. 

Sometimes the answer is yes but for many organizations the answer is “no.” They’re just not sophisticated enough. And big costly failures could sour your organization on pursuing AI and set you back years

Ascend AI 

So what should you do? One option to get started is Ascend AI, a data science as-a-service solution that Ironside developed. Ascend AI lowers the risk out of diving into AI on your own. It is  underpinned by a custom configured and scripted cloud-based architecture as well as our highly skilled data scientists. 

We bring the data scientists and engineers and the technology. You provide the data and the business problems. 

We start with your leading use cases or help you develop them in a use case catalog. Then we perform rapid viability assessments on the leading use cases selected and if signs are good we would then build out full machine learning algorithms. Finally, we could deploy and host and manage the algorithms. At any point, depending on customer preference and maturity, we would hand the IP back to our customers and help them develop AI competency in house. We’re not a black box. 

Of course there’s more to getting started with AI than these four points and data science as a service might not always be the answer. The thing to remember is that AI should be consumed in bite sized-chunks and is attainable to even the most technologically immature organizations.

Ironside is pleased to announce the release of a new packaged service AscentAI. For nearly 10 years, Ironside has offered data science expertise and advisory services to organizations who seek to establish AI within their enterprise. Now Ironside offers a powerful new service for organizations who are earlier in their AI journey.

AI is becoming more of a necessity for businesses to retain their competitive edge, keep internal costs low and manage risk. But getting started can be overwhelming. What technologies should we invest in? Should we hire data scientists and how many? What use cases should they work on? Where would they get started? How much will this cost us?  Can our current infrastructure support this? Is our data mature enough? Is our organization ready?

Ironside’s strong history of helping organizations get started on their AI journey allows us to understand common pitfalls and how to pivot around them, have a valuable point of view on AI/ML technology and infrastructure options, and provide a highly skilled data science team. We understand that many organizations can’t jump in feet first and need a way to quickly and easily prove value with rapid cost-effective sprints before they begin to think about hiring or large technology purchases. That is why we created AscentAI.

What is AscentAI?

AscentAI is a packaged service, delivered in progressive modules to allow you to scale up at your own pace.

Ascend AI's Progressive 4-Step Solution

Ironside provides the data science team, including solution architects, developers, experience designers, data engineers and of course experienced data scientists, and leverages their own infrastructure and AI IP that they’ve developed over the years. You provide your data and business subject matter experts who work closely with the Ironside team to develop a customized AI solution that delivers measurable results.

We bring the technology and expertise so you can focus on putting the results to work for your organization.

Who is AscentAI for?

AscentAI is right for any organization that says:

  • We need to test out AI use cases before investing in technology and people.
  • We want to build a business case to gain executive support for further AI funding.
  • We want to become an AI driven organization, but without investing in capital expenses or building an internal center of excellence.
  • We need a trusted AI partner, not an off-the-shelf solution.
  • We have unique business problems and use cases that don’t fit any solution on the market.

Get started, today!

If you want all the benefits of implementing artificial intelligence to analyze, action and manage your data, without any of the hassles and headaches, AscentAI delivers.

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Yesterday, Ironside’s partner Pitney Bowes announced that they are forming a new data practice built to accelerate businesses’ digital transformation initiatives. This practice will reach across the whole company to accomplish the goal of helping organizations “utilize data and analytics to deliver a superior customer experience, support product and service innovation, and optimize business processes” according to the announcement. We see this as a major benefit for our clients. Read more

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