Technology First has teamed up with Girl Scouts of Western Ohio for a CSA Cyber Challenge with support of the Ohio Cyber Range Institute!

Volunteers from Technology First’s membership will lead a series of “plugged” and “unplugged” cyber challenges to over 50 Girl Scouts grades 6-12. This event will take place on July 23rd at the University of Cincinnati’s 1819 Innovation Hub. Participating companies include 84.51, CareSource, Great American Insurance Group, Kettering Health, Kroger, P&G, and the University of Cincinnati.

If you know a Girl Scout interested in participating, they can register by Friday, July 9th!

Business Leaders and Community Stakeholders,

A successful and sustainable recovery for Ohio must include all Ohioans. Nearly 20% of Ohioans live in distressed areas—defined as areas that have not recovered from the last recession. The COVID-19 pandemic further disproportionally impacted these communities and their residents. It is critical that we support these areas of the state to help them and Ohio overall with post-pandemic economic recovery.

These communities are home to businesses with intellect that can benefit all of Ohio. JobsOhio is prioritizing efforts to empower the businesses with the resources they need to reach their potential. Since the start of 2020, we have worked on an inclusion strategy that focuses on investing in and driving job creation to Ohio’s distressed areas as well as providing the capital needed to grow the businesses in these communities and those owned by underserved populations.

One component of this strategy is the JobsOhio Inclusion Grant, which was created in July 2020 as part of JobsOhio’s COVID relief programming. The program puts funding of up to $50,000 for small and medium-sized businesses located in distressed areas of the state and owned by underrepresented population groups. As of today:

• 133 companies have closed deals or are currently in the pipeline.
• Over 4,287 jobs have been created or retained.
• Of these projects, 82% are in distressed communities, 23% are woman owned, 21% are minority owned, and 11% are veteran owned.

Some of the participating homegrown businesses include The Chef’s Garden, an agriculture company that pivoted its business model due to the pandemic, and MAKO Finished Products, a rural Ohio-based business that helps power a leading fitness brand. After a successful first year in providing $5 million in financial support to small and medium-sized businesses, the JobsOhio Board of Directors recently approved an increase of up to $8 million in 2021 due to the success and growing interest from local businesses.

Our efforts to ensure Ohio’s recovery is inclusive of all Ohio continue and we will share more in future updates.

Treg Gilstorf has been selected chief operating officer for Smart Data, a local IT services provider and Technology First Member.

Treg is Vice Chair of the Technology First Board and has 25+ years of experience in IT, strategic planning, process improvement, consulting, operations and project management. Prior to joining Smart Data, he spent eight years as chief information officer at Yaskawa Motoman, the Miamisburg-based robotics division of Yaskawa Electric Corp. Before that, he held leadership roles for companies throughout Southwest Ohio and Indiana — including Cincinnati-based Fifth Third Bank, Afidence and Luxottica; as well as Duke Energy’s operations in Indianapolis.

Congratulations on the new adventure, Treg!

Celebrate Interns in your organization (July 12-16, 2021)

DAYTON, Ohio (June 28, 2021) – The Southwestern Ohio Council for Higher Education (SOCHE) leads several initiatives to increase internships and close workforce gaps in Ohio. The Dayton Region recognizes the immense value of interns in workplaces and communities. Dayton Region Internship Appreciation Week encourages Southwestern Ohio companies to show their appreciation for interns at all levels. Companies can visit  https://bit.ly/3hL2ZvT for ideas on how to thank their interns.   

Several Mayors in the Miami Valley have issued proclamations to declare Intern Appreciation Week for their city from July 12 – 16, 2021.  Thanks much to the cities of Springfield, Beavercreek, Trotwood, and Centerville! Other cities are welcome to join!   

SOCHE’s member colleges and universities, representing nearly 200,000 students, participate in thousands of internships throughout the year. These 200,000 students, in addition to all the region’s high school students, are the future workforce. Hiring these students boosts the likelihood that they will remain in the area as full-time employees.

“We’re proud of the ever-increasing intern numbers across the region and we hope that employers and colleges and universities are equally proud of the progress,” said Cassie Barlow, President of SOCHE. “Every year we work to strengthen and evolve partnerships between the private sector and higher education to provide more internship opportunities in Ohio,” Barlow added.  

SOCHE’s internship program employs hundreds of students, high school through the post-doctoral level, in many positions to support Wright Patterson Air Force Base, the City of Dayton, and Montgomery County, as well as numerous small and mid-sized companies. Internship roles call for students majoring in various subjects, including science, technology, engineering, math, arts, business, social sciences, manufacturing, and humanities majors. 

Patty Buddelmeyer, Vice President of Development at SOCHE, added, “We’re excited to see more and more employers with interns in the Dayton region. Businesses are experiencing the value interns bring to workplaces. The rising internship numbers speak to the commitment of the local business community and colleges and universities to retain talented and engaged students in the region.”  

Formed in 1967, SOCHE is the trusted and recognized regional leader for higher collaboration, working with colleges and universities to transform their communities and economies through the education, employment, and engagement of nearly 200,000 students in southwest Ohio. SOCHE is home to SOCHEintern, the Aerospace Professional Development Center (APDC), and Defense Associated Graduate Student Innovators (DAGSI). For more information about SOCHE and all SOCHE initiatives, visit http://www.soche.org/.

TechCred_TechFirst_June.pdf

BY BEN PRESCOTT, AHEAD - This article originally appeared on AHEAD's i/o blog 

Recall the year 2013. The world has just topped four zettabytes of generated data (equal to four trillion gigabytes). The term “big data” is taking hold, creeping into every corner of the tech and business worlds.

From that point on, data volumes have grown exponentially. It’s estimated that the world produced more than 44 zettabytes of data by the end of 2020 (growing by a factor of 11 in just seven years). We’re now generating more data than we can effectively manage—and it’s a big problem.

Today’s organizations are searching for ways to harness, manage, and analyze these huge influxes of information. Many are turning to machine learning (ML) and deep learning (DL) models to analyze data and predict what could happen next. In fact, organizations across every industry use ML and DL today in various capacities.

Healthcare innovates pathology detection by leveraging neural networks and tracking patient patterns to predict their length of stay. The pharmaceutical industry uses ML and DL to identify causal factors of diseases for drug development and predict patient response to drug combinations. Manufacturing organizations identify product defects, predict machine malfunctions before they even happen to provide proactive maintenance, and use generative design methods to determine the best structural design for a product (such as a car frame). From a general business perspective, many organizations leverage ML to aid in forecasting business revenue, schedule resources for projects, and drive sales and marketing efficiency.

But building and training effective machine learning models is no easy task. Add the maintenance (operations) component and achieving true machine learning operations (MLOps) is difficult for most organizations.

That’s why a platform like Azure Machine Learning can be beneficial. Azure ML is a hosted service that enables and enhances an organization’s MLOps capabilities through four key benefits:

1.     Graphical interface

2.     Out-of-the-box machine learning models

3.     Single pane of glass view

4.     Direct integration with Git and Azure DevOps

Before we dive into the ins and outs of Azure MLOps, let’s define MLOps.

What is MLOps? 

MLOps blends DevOps methodologies and processes with the ML development process. There’s a lot that goes into the process of developing ML models, from collecting and cleaning data, to model training, validation, and deployment. But, while the goal is to deploy a model into production, monitoring and maintaining that model are just as, if not more, critical.

Within the DevOps umbrella are concepts known as Continuous Integration and Continuous Delivery (CI/CD), a way to shorten development and deployment cycles while maintaining quality code. While DevOps plays a critical role in core application development, MLOps puts a twist on CI/CD. Within MLOps, CI validates data quality and structure, and CD broadens focus to the full ML solution.

MLOps also introduces a new concept—pushing the deployed ML model back through the training and validation process, often referred to as Continuous Training (CI/CD/CT). This is a way of ensuring the model doesn’t become stale, having only “seen” aging data. It also continually evaluates and improves a model’s ability to predict. After all, the last thing you want is to make business decisions from a model that provides poor predictions or recommendations.

MLOps with Azure Machine Learning

One of the biggest struggles of adopting MLOps is piecing together the services needed to support the full data lifecycle. Data scientists and machine learning engineers are traditionally really good at building robust models, but the process of deployment and monitoring is usually done manually, or not at all.

Once a model is in production and consuming new data, how is it monitored and retrained? The retraining process quickly becomes manual—going back through the same steps performed during development before pushing the retrained model back into production. As soon as it’s back in production, it’s out-of-date again. As with any iterative process, this becomes time-consuming, expensive, and frustrating.

Azure ML supports MLOps by giving teams a platform to manage models and integrate model usage, output, and insights across the organization. It does this through the areas we mentioned earlier—a graphical interface, out-of-the-box algorithms, single pane of glass approach, and integration capabilities.

Graphical Interface

Azure ML provides both a graphical web interface (Azure ML Studio) as well as SDKs that (at the time of writing) support Python, R, and Azure CLI. This is important because some organizations prefer to work in their existing environments while leveraging the features and capabilities of Azure ML.

Out-of-The-Box Algorithms

Azure ML comes with many pre-built algorithms to help you get started quickly, including those for regression, text analysis, recommendation services, and computer vision. In addition, Azure ML Studio boasts “Automated ML,” a no-code solution to automatically find, train, and tune the best model for your data.

Single Pane of Glass

Azure ML takes a single pane of glass approach that provides capabilities in all areas of the MLOps lifecycle, while integrating with common DevOps services. Each capability within Azure ML can operate as an independent feature to help gradually grow MLOps maturity. Whether it’s data scientists making use of the notebooks and experimentation, infrastructure engineers managing the CPU or GPU-backed compute infrastructure, or security teams leveraging Azure ML’s model change tracking logs, everyone has a role to play in embracing an MLOps methodology. Azure ML helps make this transition easier by providing ready-to-be-consumed services.

Integration with Git and Azure DevOps

Integrating Azure ML with Git and Azure DevOps offers many automation opportunities that are otherwise not accessible:

• Enables the use of Azure DevOps Pipelines to automate the data ingestion process and perform data checks
• Provides the ability to automate the Continuous Training and Continuous Deployment aspects of the MLOps lifecycle, removing the need to manually retrain models as performance degrades
• Automates scaling of compute resources for model training, both up and out, and provides a one-stop-shop for managing backend compute needs
• Automates deployment of trained/retrained models to internal- and external-facing services on Azure Kubernetes Services, Azure App Services, Azure Container Instances, or Azure Virtual Machines, and removes the need to manually deploy new or retrained models

At the end of the day, it’s better to back your MLOps with tools that will get you there better and faster, and that’s just what the Azure ML platform does.

To learn more about how Azure ML can help your organization leverage machine learning, reach out to our team.

Mardi Humphreys, Change Agent, Integration Edge/RDSI

I’m a storyteller and I love data analytics. These two things may seem mutually exclusive, but bear with me. Data analysis is a process. So is storytelling. Data analysis inspects, cleanses, transforms, and/or models data. So does storytelling. You use data analytics to discover useful information, form conclusions, and support decision-making. So is… never mind; you know what goes here. Let me give you an example. Here’s the story of Goldilocks and the Three Bears as if they were all data analysts.

Goldilocks finds an empty-looking cabin in the woods. She peeps in the window. She doesn’t see anyone inside. She knocks on the door. No one answers. She turns the knob and finds the door unlocked. Given this data, she decides to enter the cabin. The data she’s missing? The cabin belongs to a family of three bears: Papa, Mama, and Baby. The Bear family goes out to pick fresh blueberries every morning.

Goldilocks gathers more data. There are three various-sized bowls of porridge on the dining room table. She tastes the contents of the biggest bowl. It burns her mouth. She tastes a spoonful from the medium-sized bowl. It’s cold. She tastes the porridge from the smallest bowl and finds it palatable. With this data, she discovered enough useful information to form a conclusion. She decides to eat the whole small bowl of porridge and leave the other two sitting.

Goldilocks continues being nosy, er, I mean, gathering data. She wanders into the living room and spies three chairs. Full of porridge, she decides sitting a spell is a wise choice. She tries out Papa Bear’s large chair. She determines it is too hard to sit on. Next, she tries Mama Bear’s medium-sized chair. She determines it is too soft to sit on. Sticking to her data-gathering process, she moves on to Baby Bear’s tiny chair and gives it a sit. It’s perfect! She’s so excited her experiment worked, she does a happy dance and promptly breaks the chair. (Side note - This is why we never test anything in production.)

All this porridge eating and chair breaking made Goldilocks sleepy. She again wanders around the cabin; this time looking for a bed in which to nap. Refining and iterating her process based on feedback, she is not surprised to find three various-sized beds on the second floor. Continuing her data gathering process, she plops onto the ginormous Bed #1. She about breaks her back because it’s as hard as steel. Sticking to her method, she moves on to the medium-sized Bed #2. After wallowing in the piles of blankets and pillows, she decides it’s too soft. She moves on to test the smallest, Bed #3. It must have met her search criteria, because she falls asleep in it.

Not long after, the owners of the cabin return from blueberry picking to find things are not as they left them. The data they first encounter is the dining room table with two bowls of half-eaten, Goldilocks-germ-infested porridge on it and one tiny bowl licked clean. This data leads them to believe someone’s been eating their porridge. This is a correct analysis of their collected data.

The Bear family goes to the living room to see if there is more data to collect. They find their three chairs not as they left them. The big hard chair and the medium-sized soft chair are salvageable, but inspection of the smallest chair deems it transformed beyond repair. This additional data leads them to conclude a hungry vandal entered their house and may still be there. Another correct analysis of the collected data.

Not finding anyone on the first floor, the Bear family angrily stomps upstairs to see if there is more data (or a full-bellied, chair-destroying vandal) to be collected. They find their three beds not as they left them. The biggest and medium-sized beds are now unmade and rumpled, but the covers on the smallest bed are rhythmically moving and snoring. This aggregated data leads the Bear family to the conclusion that the culprit who entered their cabin, ate their food, and broke their chair is now sleeping in their bed. Acting on their conclusion, the three angry bears roar and Goldilocks awakens.

What have we learned about data analytics from this story?

Goldilocks’ Data Analysis:
What insight did she gain? The cabin offered three sizes of food, chairs, and beds
What understanding did it build? The third option always suited her best
What decisions did it influence? Eat, sit, sleep

The Bears’ Data Analysis:
What insight did they gain? They never had to lock the cabin door before
What understanding did it build? If they leave the cabin door unlocked, they may get robbed
What decisions did it influence? Whether or not to lock the cabin door

Goldilocks’ Results:
How did she use her collected data? To ransack The Bears’ cabin
What innovation did she achieve? She learned not to make herself at home in a home that wasn’t hers

The Bears Results:
How did they use their collected data? To find the culprit who vandalized their home
What innovation did they achieve? They discovered Goldilocks made a better meal than porridge and blueberries

Okay, that last conclusion is pure speculation on my part, but you see what I’m getting at. Data analysis is rich inspiration for storytelling. You help your clients visualize their choices when you include storytelling in your design, development, and deliverable.

Kathy Vogler, Communications Manager, Expedient Technology Solutions

Early on in my career, I was told by a boss that my intuition was a gift and that I should always trust my guts in every decision I made.  That advice has really worked for me most of my life.  However, the new reality in a data-driven culture embraces the use of data in decision-making.

“If we have data, let’s look at data.  If all we have are opinions, let’s go with mine.” ~ Jim Barksdale; President and CEO of Netscape 1995 - 1999

Maybe my mind captures, cleans and curates meaningful data.  But the volume of our organizational data is much more than my job or my thoughts.  Our culture is supported by data-driven decision making and the data holds our teams accountable.

The literacy level of any team member to leverage the data at hand and turn that into appropriate decisions is key. Hence a systematic approach to analysis of the data and reporting that is understandable and actionable is of utmost importance.  The staggering amount of data that we store and analyse means that we need to meet often to review data findings, choosing what needs to be measured and what metrics will be implemented.  Our data wizards have created detailed metrics on our customers’ experience which helps our team deliver wow.

No Departmental Silos Allowed

Departments tend to focus on the data that affects them directly, and rightfully so.  But this sometimes creates a logjam and we become ignorant to the language of data that is being interpreted by each team. If we tie every team in an explicit and quantitative level, it helps us to understand if we have enough data for a reliable model to make intelligent decisions.  We need to evaluate uncertainty by testing and reevaluating the data collectively. It’s been said that promising ideas greatly outnumber practical ideas.  Proof of concepts will determine if the idea is viable in production.  The immediate goals directly affect each team member by saving time and avoiding rework with readily accessible knowledge at their fingertips.  Metrics should be universal and each team should take ownership of interpreting their data to help the literacy of the enterprise.

Measure what you Should, not what you Can

More data doesn’t guarantee better decisions, but it is always better to start with data.  Better decisions almost always begin with better informed teams.  And it’s our duty as team members to ask questions. And so I bring up skewed data …

¹ Some distributions of data, such as the bell curve or normal distribution, are symmetric. This means that the right and the left of the distribution are perfect mirror images of one another. Not every distribution of data is symmetric. Sets of data that are not symmetric are said to be asymmetric. The measure of how asymmetric a distribution can be is called skewness.

The mean, median and mode are all measures of the center of a set of data. The skewness of the data can be determined by how these quantities are related to one another.

One measure of skewness, called Pearson’s first coefficient of skewness, is to subtract the mean from the mode, and then divide this difference by the standard deviation of the data. The reason for dividing the difference is so that we have a dimensionless quantity. This explains why data skewed to the right has positive skewness. If the data set is skewed to the right, the mean is greater than the mode, and so subtracting the mode from the mean gives a positive number. A similar argument explains why data skewed to the left has negative skewness.

Pearson’s second coefficient of skewness is also used to measure the asymmetry of a data set. For this quantity, we subtract the mode from the median, multiply this number by three and then divide by the standard deviation.

   X = mean value 

   M_o = mode value

   S = standard deviation of the sample data

Skewed data arises quite naturally in various situations. Incomes are skewed to the right because even just a few individuals who earn millions of dollars can greatly affect the mean, and there are no negative incomes. Similarly, data involving the lifetime of a product, such as a brand of light bulb, are skewed to the right. Here the smallest that a lifetime can be is zero, and long-lasting light bulbs will impart a positive skewness to the data.

¹ Taylor, Courtney. "What Is Skewness in Statistics?" ThoughtCo, Aug. 25, 2020, thoughtco.com/what-is-skewness-in-statistics-3126242.

It’s important to take note of skewness while assessing your data since extreme data points are being considered. Take into consideration the extremes for current logic instead of focusing only on the average which provides a better picture of the future logic.  Flawed data analysis leads to flawed conclusions which often result in poor business decisions.

I do trust my instincts, but my reporting to others is much better with statistical data.

Uptime Solutions & Vertiv

It’s not an exaggeration to say the global pandemic created a new healthcare delivery model virtually overnight. Telehealth, previously a simmering patient engagement option, has now erupted.

The global telehealth market is expected to grow dramatically, reaching $266.8 billion by 2026 and showing a compound annual growth rate (CAGR) of 23.4% between 2018 and 2026. The biggest barrier to pre-pandemic adoption was behavioral inertia. Now due to COVID-19, momentum is building toward a marked transformation.

Understandably, most healthcare providers are struggling to keep pace. Existing IT systems, infrastructure, and security and privacy protocols already were stretched or outdated due to the proliferation of diversified healthcare systems. Telehealth adds another layer of complexity requiring new IT strategies and investments.

Read our white paper to learn more about the critical decisions that are looming for healthcare IT managers and chief information officers (CIOs).

Download the White Paper at the Vertiv Blog

Governor's Office of Workforce Transformation

The next round of TechCred opens on June 1^st and closes on June 30^th at 3:00 p.m.

Ohio businesses can visit TechCred.Ohio.gov to apply and help their employees earn a short-term, technology-focused credential at no cost. Not only can businesses upskill their current employees, but they can upskill those they plan to hire as long as they are on the payroll at the time of reimbursement.

More than 1,100 Ohio businesses have used TechCred, some having used it multiple times, creating the opportunity for 19,841 technology-focused credentials to be earned by Ohio employees.

The results from the April application period will be announced in the near future.

Visit TechCred.Ohio.gov to learn more!