How companies need to think about the cloud and digital transformation

Enterprises are at a critical juncture: adopt and adapt technology to meet the 24×7 demands of customers, employees, and partners—or risk becoming obsolete. One of the primary shifts in responding to such demands is cloud computing.

As part of our Udacity Thought Leader webinar series, our very own Lalit Singh, COO at Udacity, sat down with Rahul Tripathi, the CTO and VP of Customer Success at Nutanix to discuss the challenges facing companies looking to embrace the cloud while remaining relevant in today’s competitive market. The conversation anchored on how the cloud is the foundational enabler of digital transformation and offers the scale and speed needed for businesses to grow.

When it comes to transforming the infrastructure of a business, both modernization and automation play crucial roles. For organizations looking to bridge the digital skills gap and attract talent, it’s important to move from outdated to next-generation systems that facilitate innovation. Hear Rahul stress how technical skills and soft skills are critical for any business looking to stay competitive and transform into digital enterprises.

Watch as they discuss:

  • What a modern enterprise cloud strategy looks like
  • Why digital transformation requires the right blend of business, IT and soft skills
  • How organizations can take steps to bridge their hiring and training strategies

View webinar

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.

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.

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.

Answering “Yes” to Hard Questions About the SKills Gap, and The Future of Work

A recent article from the University of California’s Chief Innovation Officer, about the impact of disruptive technologies on jobs and skills, poses critical questions about how we connect learning to jobs—today, and in the future.

Future of Work

Everyone from politicians to policy makers, utopianists to university professors, innovators to investors, is talking about the future of work, the fourth industrial revolution, and the automation age. It’s hard to avoid these topics, and if you’re between the ages of, say, 16 and 80, you probably shouldn’t avoid them.

Our work lives are changing, and depending on how we manage the transition, this could either be a new golden age, or a serious shock to the system.

At Udacity, we’re engaged in helping lifelong learners across the globe empower themselves through learning, in order to build rewarding lives and careers. As such, we’re acutely aware of the looming changes—the theories around how it’s going to happen, and what it’s all going to mean.

We engage every day with innovators, educators, students, employees and thought leaders, to better understand what education needs to do, be, and represent as we move forward. We work with recruiters, hiring managers, entrepreneurs, and executives, to better forecast what skills will be needed, where the demand will be, and what career advancement will look like in the days, years, and decades to come. We collaborate with individuals, startups, and global corporations, to better understand how and where the work of the future will happen. In short, we spend a vast amount of time learning from anyone and everyone about what the future holds, and how we can best prepare our students to succeed.

We listen, we talk, we watch, we ask, and we read.

One article that recently impressed us for its ambitious scope, rich degree of insight, and clear-eyed understanding of where the world is heading, is a post by Christine Gulbranson, the Chief Innovation Officer for the University of California System. The article is entitled The Future of Work: The Impact of Disruptive Technologies on Jobs and Skills. Here is a sample of the wisdom Gulbranson shares in this provocative and timely piece:

“It’s not difficult to make some basic calculations about what skill sets will be needed in the future: automate predictable manual labor jobs and the skills demanded for such jobs decreases. More automated factories will increase the demand for hard skills in mechanical engineering, software architecture, coding, algorithms, data structures, data analysis/data science, and machine architecture/design. Increasing gene editing and robotic surgery will increase the demand for software engineers and mechanical engineers who also have medical skills. Move to IoT cities and policy makers and lawyers will need to understand coding, software architecture, economics, and more, on top of what they’re expected to know today.

Clearly with a rise of connected devices and infrastructure, machines, AI, spatial computing, blockchain, and autonomous vehicles, there comes an increase in demand for STEAM skills. However, sitting on top of hard skills is a deep and strong layer for cognitive, analytical, and soft skills. Employers won’t be looking for a degree that signifies what a candidate knows: they will be looking for someone who can learn, combine and analyze, problem-solve, create, and adjust.”

It’s that last sentence that especially resonated with us, because this echoes exactly what we hear directly from employers every single day. The pace of modern business and the rapid advance of technology have significantly altered the hiring landscape in such a way that characteristics such as agility, growth mindset, adaptability, creativity, and grit have emerged as the most important factors in predicting a successful hire.

That’s not to say that acquired skills don’t matter—they do!—but the ability to learn new skills and apply them has become just as important as the skills you already possess.

This is also not to say that educational pedigree doesn’t have a place any longer—it does—but what constitutes credible pedigree is changing rapidly. As we’ve learned in the years since first launching our Nanodegree programs, a Nanodegree credential fulfills a dual role. In addition to affirming your skills acquisition, earning a Nanodegree credential stands as evidence that you are a self-motivated problem-solver who possesses grit and determination.

Gulbranson’s article concludes on a sobering note of caution:

“Finally, as we already know today, if education can’t keep up with changing industry, then the skills gap will hinder technological advancement and adoption.”

She goes on to ask some powerful questions, such as:

  • Are students learning how to learn, handle high complexity, and be flexible?
  • Are they learning how to make the invisible visible, and how to make good decisions using data and analysis?
  • Are there solutions that don’t cost an arm and a leg and last four years when the industry needs a software engineer who is also a psychologist to create a product that detects the mood of drivers and auto-shuts off the car appropriately?

We’re proud to be part of a new generation of learning providers offering opportunities that represent a “yes” answer to all the above, and we’re grateful to innovators like Christine Gulbranson who are out there asking the hard questions, and providing the right answers.

Through your commitment to lifelong learning at your organization, you are helping build rewarding careers for employees, while creating an environment for innovation.

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Visit udacity.com/enterprise to discover how we can help your organization successfully navigate workforce transformation!

Udacity Artificial Intelligence and Data Industry Advisory Board

Udacity Artificial Intelligence and Data Industry Advisory Board

AI Advisory Board

As we look forward into a future we know will be shaped by the transformational impact of artificial intelligence and data technologies, we can clearly see the birth of a new knowledge ecosystem within which education, industry, and technology form a powerful partnership. That these three arenas will be interrelated goes without saying, but how they inform one another, and how these relationships take shape and evolve, remain open questions.

At Udacity, we recognize the singular role we occupy, existing as we do at the crossroads where education, industry, and technology meet. We are a learning provider that teaches AI and data skills, in partnership with industry, and as such, we see a unique opportunity—and feel a special obligation—to both facilitate and contribute to the global conversation around critical issues we face as we move into our AI and data-powered future.

We are very excited to have recently formed an Artificial Intelligence and Data Industry Advisory Board with the expressed goal of bringing together leading experts in the field to consider the opportunities that lay ahead, to address the challenges we face, and to answer the questions we must answer.

We believe that through combining experiences and skills, sharing insights and ideas, and producing solutions and strategies, we can lay out a plan for the future that is beneficial to all—a plan that nurtures and supports emerging generations of learners to master artificial intelligence and data skills, encourages and incentivizes industry to adopt beneficial AI and data practices, and guarantees a pipeline of highly skilled individuals who are committed to social good ideals, and the ethical adoption and implementation of transformational technologies.

Among the experts who have joined our board is Armen Pischdotchian, the Academic Tech Mentor at IBM. In his role, he mentors university faculty and students, and conducts enablement sessions—both in and outside of the company—pertaining to the IBM Watson Solution offerings. Here is Armen on why he wanted to be a part of the board:

“I strongly believe that the Advisory board, at its core, is addressing a gap that needs to be erased, and that is the space between industry and education. Udacity has the unique pedigree of listening to the needs of tech giants and startups and asking the question, what does your candidate need to be proficient so the firm will succeed?”

Armen is joined by an incredible roster of individuals who come to us from leading organizations such as Amazon, Google, NVIDIA, and more. It is with both gratitude and excitement that we introduce the inaugural members of the Udacity Artificial Intelligence and Data Industry Advisory Board:

  • Armen Pischdotchian, Academic Tech Mentor, IBM
  • Brad Klingenberg, VP of Data Science, Stitch Fix
  • Bryan Catanzaro, VP of Applied Deep Learning Research, NVIDIA
  • Cyrus Vahid, Principal Deep Learning Solutions Architect, Amazon
  • Dan Becker, Head of Kaggle Learn
  • Derek Steer, CEO, Mode
  • Jeff Feng, Product Lead, Data, Airbnb
  • Joe Spisak, Product Manager – Artificial Intelligence at Facebook
  • Jon Francis, VP of Customer Marketing Analytics & Optimization, Starbucks
  • Josh Gordon, Developer Advocate for TensorFlow, Google
  • Mike Tamir, Head of Data Science Uber ATG & Data Science Faculty member at University of California at Berkeley
  • Warren Barkley, GM, AI and Research, Microsoft

While each of these individuals brings to the board a wholly unique set of experiences and insights, they are united by a shared passion for learning, and for building a better future through the beneficial use of transformational technologies.

Our mission is to provide companies and their employees with meaningful opportunities to master valuable and in-demand skills. Jeff Feng is the Product Lead for Data at Airbnb, where he leads a team building machine learning infrastructure, data infrastructure, data visualization tools, and their experimentation platform. Here is Jeff on the passion that drives his participation:

“Shaping how people and machines make decisions with data is one of the most critical skills needed in the workforce over the next decade. Thus, providing learners with the practical knowledge needed to work with data is an area I am hugely passionate about.”

We look very forward to sharing more updates about the work of the board, and to furthering our engagement with the important issues and incredible opportunities before us. As we advance our efforts, we are thankful above all else to our board members for their spirit of generosity and goodwill, and for their commitment to the true ideals of education. Josh Gordon, Developer Advocate at Google, put it both perfectly and simply when he stated the following:

“Good teachers are hard to find. I’m grateful for those who helped me out over the years, and it’s always been important to me to give back.”

We are grateful to the members of the advisory board, and we are excited to transfer insights gleaned from their leadership to you, our students, for it is who are the emerging leaders that will define the future we are eagerly building towards.

For more information about how Udacity for Enterprise is helping companies transform their workforce, click here.