SXSW: How Top Companies Create Training Data

Udacity had the unique opportunity to have two of our thought leaders on a panel discussion on training data for machine learning entitled AI-AI-Oh! during SXSW 2019. The discussion triggered an exchange of viewpoints among the expert panelists which ranged from how the data is being used in various industries, how much training data you need to apply machine learning, and practical tips for the audience to consider.

You can listen to the entirety of our panel discussion here.

The discussion started with the framing of machine learning. Machine learning (ML) is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to.

An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have similar musical taste.

Machine learning fuels all sorts of automated tasks and spans across multiple industries, from data security firms hunting down malware to finance professionals looking out for favorable trades. They’re designed to work like virtual personal assistants, and they work quite well.

Machine learning serves a mechanical function the same way a flashlight, a car, or a television does. When something is capable of “machine learning”, it means it’s performing a function with the data given to it, and gets progressively better at that function. It’s like if you had a flashlight that turned on whenever you said “it’s dark”, so it would recognize different phrases containing the word “dark”.

In machine learning projects, we need a training data set. It is the actual data set used to train the model for performing various actions.

ML relies heavily on data; without data, it is impossible for an “AI” to learn. It is the most crucial aspect that makes algorithm training possible. The panelists discuss three different types of training data including:

Client services data – data generated from customers. “At HubSpot, we gather user-generated training data for ML that informs everything from email send time optimization to audience targeting,” stated Hector Urdiales.

User generated data – data created by users on their own without being prompted.  “We train data based on patterns,” said Rob McGrorty.

Simulated data – sensor data that self-driving cars, for example, collect in the real world. “A test vehicle’s cameras might record video of pedestrians crossing the street at night. Software developers can use that video every time they update their self-driving software, to verify that the software still detects the pedestrians correctly,” explains David Silver.

Essentially, training data is the textbook that will teach your AI to do its assigned task, and will be used over and over again to fine-tune its predictions and improve its success rate. Your AI will use training data in several different ways, all with the aim of improving the accuracy of its predictions.

Quite simply, without training data there is no AI. The cleanliness, relevance and quality of your data has a direct impact on whether your AI will achieve its goals.

Be sure to listen to this informative panel discussion and learn more about training data and practical use cases.

Vinod Khosla on the Future of Tech

In our continued “Thought Leader” webinar series, Sebastian Thrun sat down with Vinod Khosla, the founder of Khosla Ventures, a firm focused on impactful technology investments in software, AI, robotics, 3D printing, healthcare and more. Previously Khosla was the founding CEO of Sun Microsystems, where he pioneered open systems and commercial RISC processors. Khosla is widely regarded as a leading thinker in Silicon Valley and this conversation provides a glimpse of why he’s so deserving of that label.

The conversation centered on key aspects including:

  • The next wave of tech breakthroughs
  • Key industries ripe for tech disruption
  • How the biggest risk is not taking any risk at all

Technology and new inventions have always shaped the human world, and have disrupted the way we live and work, and yet we are only at the beginning summed up Khosla. Innovation in the areas of food, digitization, robotics, artificial intelligence, as a few examples, have the potential to achieve food abundance, reshape cities, knit humanity, and enhance human capability exponentially.

The big needs in society, food, health, housing, transportation, financial services, entertainment and more are being and will even more so be reinvented by technology in an “increasingly more accessible to all” way. “We need to turbocharge our efforts to utilize technology to accelerate accessibility. Many of society’s GDP and business-related needs are being reinvented everyday in a truly innovative and non-institutional way,” discussed Vinod Khosla.

“You heavily invest in media technology and human health services. Give us your vision for what is going to happen there?” asked Sebastain Thrun.  “What’s not clear are timelines. But what is very clear is that any notion of medical expertise will be embodied in AI systems,” decalred Khosla.

Receiving medical advice from the comfort of your own home will be the norm in a world where leaving your house will be less necessary in general, as a result of robotic delivery of groceries, and robotic kitchens that reduce the need for going out to grab takeout. That means taking more cars off the road, presenting an opportunity to redesign cities.

In addition, for Khosla, medical schools should focus on recruiting future doctors with high Emotional Intelligence (EQ) because their future will be managing patients rather than determining medical interventions. He lamented that the current model of symptom-based diagnosis is the practice of medicine, not the science of medicine.  

Be sure to tune in to learn more about what Vinod Khosla states about Silicon Valley, other industries ripe for disruption and how the future belongs to those who aren’t afraid of the high probability of failure, and who take, bold and radical risks.
Listen now.

Bjarne Stroustrop Reflects on 40 years of C++

Earlier this month, Bjarne Stroustrup, creator of C++, managing director in the technology division of Morgan Stanley, and a visiting professor of computer science at Columbia University in the US sat down with our own David Silver, who leads the C++ Nanodegree Program at Udacity.

The conversation centered on the history and evolution of C++ (celebrating its 40th this year), glimpse into Bjarne’s many published books, and some surprising revelations he shares with the audience.

As a young researcher at Bell Labs, Bjarne wanted to study and build a distributed system. For that, he needed a language that could do low-level things well such as: fast drivers, schedulers, and memory management. And he needed something where he could abstract away from the low-level hardware because he wanted a distributed system. And, he also wanted to use shared memory. Some languages at the time were good at hardware, some were good at abstraction, but there were none

that could do both. So, he built one. And, that is how C++ was born.

“Design and programming are human activities; forget that and all is lost,” reflects Bjarne Stroustrup. “This quote comes from one of the early versions of The C++ Programming Language when I was describing how software is written. Code is written by people and the people are part of organizations. And, if you want good code, you have to organize, educate, and motivate your people in reasonable ways. People have to do good things in good ways so that it actually works.”

Today C++ is widely used by programmers and developers. For example, the signal processing used when recording in phones use C++. A large number of the apps are in C++. Some of the control systems – fuel injection, steering, breaks – could be in C++. Most cameras and communications systems utilize C++. C++ is the language of the self-driving car industry, and an almost mandatory requirement if you want a job working on autonomous vehicles. There are several benefits of using C++ for developing applications and many applications product based developed in this language only because of its features and security.

Without omitting critical details or getting bogged down in technicalities, Stroustrup presents his unique insights into the decisions that shaped C++. Every C++ programmer will benefit from Stroustrup’s explanations of the ‘why’s’ behind the language. You’ll hear first-hand Stroustrup’s resolute philosophy about how a programming language should work and what compromises are necessary to assure its success.

Be sure to tune in to this discussion.
And, if you’re excited to get started with C++ today, you should also check out Udacity’s new Nanodegree program, Become a C++ Developer.

Q&A with AT&T’s John Donovan

John Donovan, CEO of AT&T Communications sat down with Sebastian Thrun, Udacity’s Executive Chairman as part of Udacity’s ongoing Thought Leader Series to discuss how to lead through change, leverage your employees to turn invention into innovation, and provide teams the skills needed to build tomorrow’s companies.

John manages over 250,000 people. Which begs the question, what’s the key to running an organization that large? For John, it starts with knowing his values, knowing that you’re only as good as the people who work with you and having a relentless focus on training.

“What I noticed early on was how much invention was going on that wasn’t actually going into the innovation. The distinction is invention is the thought and construction and innovation is when it becomes usable to the end user, ” John reflects.

AT&T recognized early on that it needed workers who were trained and ready for the technologies that would drive tomorrow’s business — artificial intelligence, machine learning, data science, and more.

It was a math problem, according to AT&T Communications CEO John Donovan who recounted the challenge to Sebastian. The percentage of the company’s workforce that was highly technical needed to evolve from where it was a few years ago, about 50% who were already highly technical, to some number close to 85% or 90%, according to Donovan.

“You do the basic math,” said Donovan. “You can’t hire your way there and you can’t acquire yourself there. You really have to start with the workforce you have. How can we help our employees know jobs and skills are transforming?”

That’s the problem that many companies are facing as they look to evolve their businesses for a new era. If they don’t have the trained staff today, it’s not really clear how they will get to where they want to be for tomorrow.

Reimagining work requires you to be proactive about y

our workforce planning, according to Donovan. Pivoting the workforce means being able to identify new areas of growth and helping workers acquire the right skills.

AT&T lets employees access an HR system that shows a career path to a future job they may want and lets them plot their course to that job and then map the curriculum needed to get to that job.

“I have to establish competencies along the way just to stay in my current job,” Donovan said. “Then I can add a badge that certifies I’m competent in a new skill. I can add a nanodegree.”

Listen to the entirety of John and Sebastian’s conversation as they discuss:

John’s leadership style and how he deeply cares about relationships
His strategy for getting honest feedback from his peers
The catalysts for workforce transformation

LISTEN NOW

Educating Our Way Out of the Data Scientist Shortage

It’s no secret that employers are looking for data scientists. They have become the stars of the modern workforce – the most valuable employees.

Companies of all sizes have awoke to the fact that data science, by mining new insights from even decades of accumulated data sets, has the potential to drive efficiencies and increase productivity in ways never previously imagined. Simply put, it has the potential to transform businesses. From Zillow’s home price predictions to Amazon’s recommendation engines, applications of data science have become increasingly accurate, prevalent, and impactful on our everyday lives.

But while “data scientist” has been ranked the “No. 1 Job in America” for three years running now, according to careers website Glassdoor, there’s still a shortage of talent to fill the huge need of employers across every industry. In fact, according to a recent LinkedIn study, businesses across the nation need 151,717 more data scientists right now.

The need is nothing short of stunning.

This is why companies understand that they must increasingly invest in the education of their employees in order to compete in an ever-changing world. At the same time, employees need to recognize that traditional higher education just isn’t designed or equipped  to keep up with the breathtaking pace of technological developments and digital transformation that we see in business every single day. People may intuitively know that learning is a lifelong process. But the modern employees also needs to accept that that continually adding to their skill set is the best way they stay competitive in the job market.

Here’s the reality: Jobs are available. But organizations expect potential employees (and current ones) to have the skills to those critical jobs.

The advantage of this digital transformation is that it’s also changing how we think about education. And it truly can be the answer to solving the data scientist shortage within your company.

This ongoing process of learning can take place digitally and independently of location. E-learning can happen anywhere, anytime: at the workplace, at home, on the train, or in the coffee shop. The subject matter can even be adapted to the precise, tailored requirements of a company. This way, it has maximum added value for employees and employers. For example, last year the automobile company Audi launched its employee “data-camp” training focused on big data and artificial intelligence.

Even companies that specialize in data analysis have recognized their own crying need to create alternatives to the traditional training pathways. After all, they are on the front lines of the digital transformation, and their workers need to have cutting-edge skills.

For example, our customer Alteryx, which develops self-service data analysis software, offers a nanodegree that enables regular employees to become data specialists and to expand their own career opportunities. In this way, companies meet the need for data specialists, while employees sharpen their skill sets, receive additional qualifications and ultimately improve their career opportunities.

It becomes a win-win. Organizations benefit the improved effort of employees. The workers themselves expand their horizons.

Employees who have a background in computer science or mathematics – and interact with numbers, data and programming daily – are ideal candidates in terms of becoming data experts in the company. Udacity’s online course, with concrete sample projects and application examples, is usually enough to give employees the added education they need to take that next step within their own company.

But employees outside of traditional IT departments have opportunities to pursue what is known in the industry as  “Citizen Data Scientists.”The term describes employees who evaluate data but do not program the algorithms themselves. Instead, they use self-service tools. These tools enable the analysis and visualization of large amounts of data with preconfigured workflows. The advantage here is that employees usually know more about the context of the data and can bring that understanding directly into their own departments.

Data isn’t the future. It’s now. And it’s critical to every company in every industry.

Companies are looking everywhere for data scientists. They can be academically trained, educating through  internal further education programs, or this relatively new world of Citizen Data Scientists, It’s clear that businesses need all of them because we live in  a world where data is collected everywhere. It’s clear that companies need to invest in employee training to keep pace with digital transformation.

Faced with this dire shortage of talent, business leaders who want to make the most of data science can’t rely on half-measures and casual hiring processes. What they need is a strategic roadmap toward building data science skills internally and effectively upskilling their talented employees.

Stay tuned for new releases from Udacity Enterprise.

The Future of Work is All About Your Skills

The future of work won’t be about college degrees. It’ll be about skills.

That’s the new global reality shaping the job market. Highest performing organizations are reinvesting in their talent to fuel profits and business growth. By investing in training and development efforts, companies can enable their well-rounded employees to perfect their set of skills to succeed in their jobs.

The reality is that employers are looking for more than knowledge — they want skills,  top-tier tech companies such as Google, Apple, and IBM have gone public “offering well-paying jobs to those with nontraditional education.” For these and many other companies, a solid, skills-centered non-formal education is all that separates ambitious students from top-paying jobs. A formal education is no longer the best path to launching a successful career.

The skills gap is widening and companies are struggling to find the right talent. A recent Gartner research supports this premise by highlighting that companies need to shift from external hiring strategies towards their current workforces and apply risk mitigation strategies for critical talent shortages.  According to Gartner, most organizations are undergoing a digital transformation that directly impacts how they do business, yet 70 percent of employees have not mastered the skills they need for their jobs today, and 80 percent of employees do not have the skills needed for their current and future roles.

Re-skilling was also hot topic in this year’s Davos event. According to a World Economic Forum report released just ahead of the event, a total of 1.4 million US workers might lose their jobs over the next decade as a result of new technological changes and inadequate skills compete effectively. However, the report found, it will be possible to transition 95 percent of at-risk workers into positions that have similar skills and higher wages through re-skilling. The report further indicates that the rapid evolution of machines and algorithms in the workplace could create 133 million new roles in place of 75 million that will be displaced between now and 2022.

Too often, college degrees have been thought of as lifelong stamps of professional competency, perpetuating the notion that work — and the knowledge it requires — is static.  The shift to a skills-based economy enables individuals to compete for employment based on what they can do for a company. At the same time it gives companies a tremendous opportunity to more efficiently integrate continual learning into work routines and implement reskilling and upskilling initiatives.

Here at Udacity we are working with global companies to help them:

  • Launch an upskilling initiative across their company (communicate the mission, how employees can get involved, what is expected of them, duration and how success is measured)
  • Develop flexible learning journeys to help employees reach the next level and prepare for tomorrow
  • Encourage our stakeholder(s) to invest in frequent, regular communications about the employee experience
  • Work collectively to employ incentives, learning models, leadership communications, and other motivational campaigns to drive completion rate of upskilling programs.

Udacity for Enterprise provides tailored, end-to-end learning paths for your company and entire workforce. We’ll help companies choose the right learning path for their workforce and help their high-performing employees continue to gain the right skills to excel and innovate.

Let’s get started.

Bridging the AI Skills Gap Webinar Recap

Last week, we held our Bridging the AI Skills Gap webinar featuring Varun Ganapathi, head of our AI and Data Engineering and Mat Leonard, Product lead for our School of Artificial Intelligence.

The conversation centered on five key areas:

  1. AI vs Machine learning vs Deep learning
  2. How companies are using these technologies today?
  3. Skills gap and talent shortage
  4. Common use cases and outcomes
  5. How to overcome the skills gap

AI, machine learning, and deep learning are easily confused and overlap with each other. The panel did a good job of breaking down the definitions:

AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

“AI is any technology that enables a system to demonstrate human-like intelligence,” explained Varun Ganapathi. “Machine Learning (ML) is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”

Watch Webinar Recording

Today, different AI technologies are finding a place in various industries. For instance, Banking and Financial Services companies are using chatbots or virtual assistants to help customers with routine tasks like scheduling payments, automate most frequently asked questions. Predictive Analytics’ to reduce the risk of loan defaulters. Machine learning to identify patterns of transactions that might indicate fraudulent activity.

The expanding applications for AI continues to create a shortage of qualified workers in the field. AI is moving fast and enterprises need talent today. However, not just any talent. What once was a shortage of coding and software engineering expertise has now evolved into an overall shortage of skills in machine learning, robotics and algorithmic engineering.

Product Lead

“If you’re considering working in AI as a data scientist or machine learning engineer you need to find a good starting point, and it starts with knowing Python, C++, and learning mainstream deep learning libraries like TensorFlow or PyTorch,“ said Mat Leonard, Product Lead at Udacity’s School of Artificial Intelligence.

AI and machine learning are driving innovation and transformation. They are embedded in how we sift through large volumes of data and content and how we interact, connect, and buy today. They are the engines underlying many of our products and services.

Hear more from Varun and Mat about steps your organization can take to embrace AI and close the skills gap. Listen now.

How To Get Started In AI

Learning AI

Artificial intelligence (AI) enters our lives in many different ways. AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning. Deep learning is a branch of machine learning utilizing giant neural networks and massive data sets. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Artificial intelligence is growing exponentially in all the major sectors, including health, social media analysis, self-driving cars, language processing and others. The AlphaGo victory is just one of the signs of amazing things to happen. The understanding of artificial intelligence opens lots of opportunities.

If you’re considering working in AI as a data scientist or machine learning engineer but need to find a good starting point, here are a few things to consider in your learning journey:

  1. Get your mathematics strong. You should have some appreciation of the mathematical underpinnings, especially linear algebra and calculus. Specifically, you’ll need to be comfortable with matrix multiplication and partial derivatives.
  2. To get a development role on an AI team, be sure to have at least one to two years of software development and machine learning experience under your belt. This can include building your own projects or working at a company driving key projects such as image or text classification. A great place to look for machine learning projects is arXiv where researchers often publish their papers. You can sharpen your skills by implementing models and systems from papers that capture your interest.
  3. Learn Python. This handy programming language is the tool of choice for most machine learning engineers and data scientists.  Python’s syntax is relatively easy to pick up and it has a vibrant and helpful community. The language also has excellent documentation and tons of training resources. With tools such as Jupyter notebooks and libraries like Numpy and Pandas, Python has become the first choice for developing machine learning and deep learning applications.   Outside of machine learning, Python is useful for developing websites, videos games, and more. Udacity can get you coding in Python and building your first neural network in just three months!
  4. Learn mainstream deep learning libraries like TensorFlow or PyTorch. Most deep learning systems are built in either TensorFlow or PyTorch, Python frameworks that provide APIs for defining and training deep learning models. You’ll want to be experienced with at least one of these frameworks as most AI teams are using them for research or product development. You should also consider joining the machine learning community.  

The biggest area of AI research today seeks to enable computers to make inferences from complex data. Techniques to do this are termed machine learning (ML).  AI and ML are large and rapidly-developing fields. While it’s impossible to capture their full potential in a this blog post, we’d like to invite you to Mat’s workshop on Natural Language Processing at the Global AI Conference on January 24th from 2pm – 6pm PST in Santa Clara, California.

Turkcell Embraces Digital Transformation

Turkcell Graduates
Turkcell Graduates

Digital transformation has further raised the need for change of the telco business model. Traditional telcos are almost indistinguishable—same services, different day—resulting in stagnant growth. Customers are constantly shopping around for what’s next, thanks to competition from born-digital market entrants and a growing demand for new services and immersive experiences. In an age of unprecedented disruption where brands cater to customers, telcos must adapt quickly or risk losing even long-time loyalists.

Enter Turkcell. Turkcell is a mobile phone service provider based in Turkey that also operates around nearby countries, with a total of 50 million subscribers, making it the third largest in Europe. In addition, they are listed on the New York Stock Exchange.

The company has invested in building its own digital apps and services, reaching 110 downloads, 3 million of which are from outside of Turkey. The carrier’s current portfolio covers a communications platform dubbed BiP, music platform fizy, TV platform TV+, local search engine Yaani, secure login service Fast Login and digital payments company Paycell. The company has expanded its digital portfolio an embraced the needs of its consumers.

Turkcell needed to move rapidly in a market being transformed by digitalization and needed to make sure its employees were reskilled to handle the changes it was instituting on the technology side.

Turkcell Graduates
Turkcell Digital Masters Program

The company invested in the future of its workforce and created the Turkcell Digital Masters Program. Employed by Turkcell Academy and in partnership with Udacity, Turkcell Digital Masters trained employees in data analysis, machine learning, artificial intelligence, data entry, programming and business analysis. During the 9-month period, 1,088 Turkcell employees prepared a total of 4,878 projects, dedicating 10 hours a week to the program.

Just this past Friday, November 30, 2018 Turkcell held their graduation ceremony where they announced 751 new Udacity graduates from programs spanning from Data Foundations to Artificial Intelligence.

Udacity and Turkcell have been working together since 2017. The collaboration and passion has resulted in:

  • 1,500 applications to the Udacity Nanodegree program
  • 1,088 enrolled employees
  • 751 Udacity graduates (500 attended the in-person ceremony)
  • 4,878 total projects completed
  • 19 news articles reached a distribution of 2.3M people

We wanted to congratulate all the new graduates! Udacity is proud to be working with Turkcell to help them transform their workforce.

How to Be a Champion of Change – Forbes CMO Summit Takeaways

 

CMO Summit
Forbes CMO Summit 2018

I had the privilege of attending the Forbes CMO Summit 2018 a few weeks back. It was a veritable who’s who in the world of marketing, including (in no particular order) CMO’s from Hallmark, PepsiCo, Cadillac, Visa, Microsoft, Wendy’s, Ebay, Salesforce and more.

The overall theme was Champions of Change: CMOs at the Center of Business, Tech and Cultural Innovation. During my career in marketing, which if you must know spans several decades, the role of marketing has changed dramatically. Tools and concepts have changed, the audience is more sophisticated, with lofty expectations, and our organizations are now at the center of it all, owning everything from top of the funnel to revenue to up-sell, renewals and churn. But at the heart of it, we still need to connect one on one with people. Or as Forbes summed up the conference, “data is king, but the heart rules”.

Here are my top 5 takeaways that can be applied to every role in every organization:

1.) Be authentic – It’s easy to get caught up in the crazy that is our daily lives, both at work and home. It’s also very easy to fall into the spell of doing just enough, cutting corners, and in some cases, even being lazy (or as someone once put it, “having the minimum amount of flare”). But to truly be successful, productive (and happy), you really need to be present, be yourself, and be authentic – even if that scares you. Lindsey Foy, CMO of Hallmark, articulated this in her talk at the Summit, which is similar to her Ted talk here. It’s well worth the 17 minutes to watch.

2.) People first, not customers first – This is true for B2B, as well as the obvious B2C. In order to truly engage with buyers, you need to establish rapport and trust and then build on that relationship. Treat people as citizens of your company.

3.) Ask the question “how can I/we add value to that person’s life?”. – By doing this you are focusing on the needs of your customers. I can’t tell you how many sales calls I’ve been on where the person tells me what their solution/features/benefits are without asking me about my needs. But this is also true of marketing collateral I see (and sometimes create). In other words, too focused on what it is versus what value it will bring, not just to the company, but to the end users or consumers of the product or solution.  

4.) Live the brand. Become the story. – When I was at Oracle, we had a saying, “eat your own dog food.” Sounds trite, but for me it was meaningful. We used our own software to do our job. We had specific insight to its features, how to use it, what worked, and what didn’t work. We actually ended up creating a case study on it. Can’t get any more “live the brand, become the story” than that. But by doing this, it helped us develop  the right content and programs to attract them. And let’s face it, if you’re not working for a company you believe in, you’re probably not going to be happy.

5.) Create the right environment for the above to happen seamlessly – Above all else, as leaders, we need to create the kind of environment for 1-4 to exist and thrive.

It’s not often we take the time out of our busy schedule to participate in events like this. Turning the laptop off, putting away the phone, cancelling all meetings and truly being present. For me, this was a great reminder of why I went into marketing in the first place. These takeaways and concepts aren’t new. They are not groundbreaking. They are just reminders of the ways we might have strayed and how to get back on the right path (whatever that may be for you).

So to summarize – be authentic, value customers, and live your brand. Super easy, right?!? Now go forth and be a champion of change.

Written by: Christina Del Villar, Global Head of Marketing, Enterprise at Udacity

Christina is passionate about seeing companies transform, grow  and scale, leveraging technology. With over 20 years of executive-level growth marketing experience at Fortune 100 companies and over 10 startups, she has a successful history of building teams that execute innovative go-to-market roadmaps and strategy. Christina loves working with companies that are going through a growth phase and she has the experience and industry perspective needed to take growing businesses to the next level. Her role at Webgility put Christina in a unique position to impact the e-tail industry with powerful e-commerce solutions. Her most recent role at Udacity, involves shifting the company focus from a B2C model to a B2B model. Christina also enjoys traveling, participating in endurance events, and working with various nonprofits, including Team Ronald McDonald House and Best Buddies.