Announcing our newest client: Global consulting leader Accenture

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Since we launched Udacity’s Enterprise business in 2017, a growing number of companies have enrolled their employees in Udacity’s Nanodegree programs, indicating that leading organizations are increasingly investing in Udacity’s Nanodegree programs to upskill their own employees while helping to close the talent gap in the labor market. We’re proud to have partnered with Fortune 500 companies to help them transform their workforces. Today, we’re thrilled to announce our latest partnership with the worldwide consulting leader Accenture. 

Learning with Udacity supports Accenture’s mission to help solve their clients’ toughest challenges. Through this pilot program, selected Accenture employees will have the opportunity to enroll in one of five Udacity Nanodegree programs in pioneering technology subjects such as Self-Driving Cars, Deep Learning, Deep Reinforcement Learning, Machine Learning, and Computer Vision.

When I met with Udacity graduates at Accenture last month, we spoke about the changing nature of industries worldwide. Transportation, for instance, is undergoing multiple parallel changes with the advent of electric and self-driving cars, as well as new ridesharing models. Lifelong learning is a crucial tool that enables students to catch up to the latest in these fields. By becoming expert learners in subjects such as self-driving cars and machine learning, students will be able to move in tandem with these innovations, remaining on the pulse, and transition into new capabilities as experts in their fields.

Andrew Smith, Managing Director and Head of Industry X.0 at Accenture, completed Udacity’s Deep Learning Nanodegree program earlier this year. “Continuous learning is important in every job. But in consulting it is vital to stay ahead, especially as the speed of technological advancement accelerates every day,” he said. “Udacity’s programs teach the exact tech skills we need at Accenture. They offer a great learning experience that can be flexibly integrated into your schedule regardless of location and course hours.”

By launching this pilot program with Accenture, Udacity is taking another step towards our mission to power careers through tech education. A warm welcome to all the new learners at Accenture. We look forward to watching your continued progress and seeing what you will build. Thank you for learning with Udacity!

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Foster a Learning Environment in Your Enterprise

Find out how to transform your workforce for the digital age.

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The most successful, fastest-growing companies are differentiated by one thing: they’ve transformed how their people learn and lead. This high-performing, maturing organizations encourage experimentation, iteration, and transparency to ensure new ideas are tested and outcomes are shared. According to a LinkedIn 2018 survey, employees are hungry for personal development opportunities at work: 94 percent of employees say they would stay at a company longer if it invested in their career but the number one reason employees feel they aren’t learning and upskilling is because they don’t have the time. 



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Five Questions To Consider While Selecting A Talent Transformation Partner

According to EY, 52 percent of Fortune 500 companies have either been acquired, gone bankrupt, or simply shut down as a result of digital disruption since 2000. The pace and volume of digital transformation have snowballed into a disruptive force with the advent of artificial intelligence (AI) and automation, resulting in repercussions even in workforce management. Reskilling employees, therefore, has become an inescapable reality for enterprises. 

A 2019 CIO Survey conducted by Gartner states that the number of enterprises applying AI grew 270 percent over the past four years – and tripled in just the past year. However, the survey also states that organizations that are leveraging AI for varied applications struggle with acute technology skill gap issues. Thus, what we are witnessing today is a massive demand from enterprises for upskilling solutions and programs.

Here are five key questions to consider while selecting an upskilling partner:



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Navigating an Enterprise Level Digital Transformation

Digital transformation isn’t a clean and simple process. There is no one singular path when it comes to implementing digital transformation within a company. No one knows this better than Chris Hsu, who was the EVP and GM of HPE software business. He was also the Chief Operating Officer of Hewlett Packard Enterprise. After HPE sold the software business to Micro Focus, he became the Chief Operating Officer of Micro Focus. Chris joined our COO, Lalit Singh to discuss how companies can lead effective digital transformations in our latest Thought Leader Webinar Series.

Digital transformation marks a radical rethinking of how an organization needs to use  technology, people and processes to fundamentally change business performance, Chris Hsu stresses. “Frankly, in today’s day and age, I think that if you are living in old systems and processes, you will be disrupted.”

Fearful of being outflanked by more nimble competitors, companies are seeking to accelerate innovation, experimenting with new digital services and capabilities to augment existing offerings or to slide into adjacent markets. One of the first things companies should do in embarking on a digital transformation is answer this critical question: What business outcomes do you want to achieve?

“It starts with the business outcomes and the new business models you’re going after and working backwards from there,” says Chris Hsu. “There are a number of perspectives to consider including technology, data, and more than anything the people and operational side of the business.”

Companies can follow some key digital transformational tips to affect the kind of change they desire, including:

  • Focus on a clear set of objectives. Whether you’re transforming an existing model or starting from scratch, leaders must reach a consensus on the best path to pursue.
  • Adopt agile execution. Encourage risk taking, enabling even lower-level employees to make decisions, fail fast and learn.
  • Instill focus. Business leaders must recognize it  is a marathon, not a sprint. And in order to present and to be effective over a long period of time, leaders must be mentally and physically fit so they can focus.

While emerging tech and revamped processes are crucial, having the right skills on staff is essential to any digital transformation.

Software engineers, cloud computing specialists and product managers remain key roles for companies seeking to roll out new products and services. DevOps leaders galvanize software development by merging development with operations, enabling companies to continuously iterate software to speed delivery.

Data scientists and data architects are also in high demand, as companies seek to glean insights out of vast troves of data, and transformations lean increasingly on machine learning and artificial intelligence.

Companies can lag in digital transformations for several reasons, a few being poor leadership, disconnects between IT and the business, lagging employee engagement and substandard operations. But, according to Chris Hsu, “I think digital transformation, if you’re doing it correctly, should impact all parts of the organization. Digital transformation has the ability to flatten an organization, and to really start to streamline and remove those manual administrative processes that no one really loves doing, and creates the capacity for more customer-facing interactions,  for more innovation, and fundamentally more building of things.”

This is one webinar, you don’t want to miss. View it here.

Why Gamification and Education go Together

Bing Gordon, CPO at venture capital firm Kleiner Perkins Caufield & Byers, has made smart investments in the leaders of the new video game industry, Ngmoco (acquired by Japanese mobile game firm DeNA for up to $403 million) and famed game developer, Zynga. And in his first video game career, he spent more than 25 years at Electronic Arts, working with game developers to produce some of the best video games ever made. He sat down with our own Sebastian Thrun discussing how today’s best thinkers apply key principles to make decisions and how education and gamification create a bond between the creator and user.

Gamification is everywhere: in to-do list apps Todoist and Asana, encouraging you to get more done; in Apple Watch, rewarding you for achieving fitness goals; in Fortune 500 companies’ strategies to boost employee engagement and schools’ plans to improve education.

Everything wants you to move more, get more done—and then reward you for it. You’re supposed to get caught up in the fun of the game, not the tedium of the chore.

“We behave in certain ways because we are motivated by the expected outcome of that behavior,” Bing explains.  Whether or not you find valence in your reward is a bit more subjective. It’s a matter of what motivates you. If you take pleasure in achievements (or simply that feeling of accomplishment associated with crossing something off your list), then chances are, gamification works for you. “Motivation trumps everything. Make your users feel like heroes,” he reiterates.

Bing also reflects about his time working with Jeff Bezos when he had joined Amazon’s board. He discusses key management principles he admires from the Amazon leader, Jeff Bezos, notably his top three: customer first, innovation always and long-term over short-term.  “Jeff is really the inventor of inventions. Throughout the years he’s learned to listen and develop systems to do that effectively. He doesn’t measure people on outputs. It’s all about inputs—i.e., what’s under your control, what you add to the system, on what date and in what quality.”

You can watch the interview in it’s entirety here

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

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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.