If not, you should. Studying how software developers learn is key to understand how they perform in the workplace. Learning can affect how quickly they react to new stimuli, or how engaged they’ll be in solving business challenges. It can be a prime motivator in promoting high level performance, and its presence can indicate whether or not developers will stay with an organization long-term. (more…)
The open source learning movement began with software development – examples include Android and Mozilla Firefox – and the software is open to everyone to use, customize or modify as needed to meet their individual or organizational requirements. It’s also free.
The open source movement, which began in the 1980s, enabled programmers and users to learn about or be trained on software and explore new codes and innovations because the code for open source software is available for anyone to add to, change or just generally improve. It has not only changed the way we develop software today, it has contributed significantly to the rapid advance of technology.
Part of the reason open source has been so influential is because it allows participants to collaborate and fully leverage their experts. Open source has applications outside the technology realm as well. For instance, some of the core ideas associated with open-source technology have made their way into strategic knowledge sharing efforts in an organizational context as learning leaders adopt them for their own use.
You may have heard of open source learning? As transparency, decentralization, and collaboration become the new normal in many organizations, the idea that anyone can and should share their expertise has taken hold. Further, the idea that individuals and teams should be able to tap into and build on each other’s know-how neatly sums up the concept of open-source learning.
Here are three ways open source learning can benefit technical training:
1. Open source learning promotes diversity of thought. One key facet of open source learning that is ideally suited for technical training is there is no one-size-fits-all strategy or solution. Open source learning is inherently diverse because it embraces the idea that different people learn in different ways. Further, there is the underlying idea that each of those people likely has something valuable to contribute to the software development or the learning experience.
As in the best technical training offerings, a high level of customization is not only necessary, it’s expected. Plus, open source themed training isn’t just customized for learners, it’s customized for each organization’s unique business challenges and technology concerns.
Because anyone could conceivably contribute to open source code, it’s by nature interdisciplinary, and open source learning is as well. Ideally, technical talent in various roles will share the same classroom. That way, each person can bring their perspective to the learning environment. In this way their peers can absorb different goals and concerns from various departments. The overarching goals, however, remain the same. Common business goals provides needed structured so that learners can use each other’s experiences to explore different alternatives to reach that common goal.
2. Open source learning prioritizes collaboration. With open source learning as the structure, a lab intensive, hands on technical training classroom is the perfect environment for multiple perspectives to come together to share knowledge. Peer-to-peer collaboration encourages learners to think creatively as well as analytically with support from expert facilitators who have deep experience in the work world as well as in specific technologies. Engagement is often high in this type of technical training environment, and it promotes knowledge retention as well as learning application once learners are back in the workplace.
3. Technical training the open source way promotes innovation. Innovation is the lifeblood of technology and the foundation of any significant competitive edge for an organization.
In an open source style technical training environment, as learners master different technical concepts and skills, they develop multi-faceted, even divergent ways to think. This is often just what’s needed to identify new products, services or process efficiencies with which to game the market. Open source learning gives learners the freedom to create and manage interactive learning material, generating and sharing value that extends beyond the immediate training curriculum.
Open source learning also creates interactive, rightly timed opportunities for performance evaluation, and alternative assessments to gauge learner absorption of material and help the trainer shape the lesson to best meet learner’s needs in the moment. Deeper, more engaged learners often achieve notable training achievements in this environment.
Learning leaders should seek out technical training options that promote open-source style learning. It’s quite practical, and its principles align neatly with the best run technical training classrooms. For instance, with the right classroom-based technical training learners don’t need to absorb complicated theories or ideas that aren’t immediately relevant to their work or current business needs. This saves time and energy and enables learners to retain more of their knowledge and apply it more effectively on the job.
Open source learning helps learners to create value by sharing information, collaborating to achieve training objectives and enjoying the benefits of peer-to-peer learning and idea exchange to spark new, innovative solutions to training exercises and business challenges alike. Open source learning and a thematically aligned technical training classroom are both high touch and team-centric. Reminiscent of the ideal business environment, in this learning lab multiple stakeholders from different functions can learn, share, dream, create and work together to achieve common business goals.
The rise of e-commerce, the impact of social media, and the increasingly prominent role technology plays in business functions like marketing, finance, HR and learning have companies in every industry claiming they’re tech companies. The demand for tech savvy talent is correspondingly high. It’s also difficult to find.
According to the 2017 Global Information Security Workforce Study, some 1.8 million IT jobs could be open by 2022. That’s a 20 percent bump from the same study data two years earlier. Low supply and high demand have created a significant challenge for learning and development leaders. In the absence of market ready talent, they must help to prepare existing technical talent to fill the roles needed to meet marketplace and consumer needs. It’s a tough challenge, but not an un-surmountable one if they’re willing to collaborate.
Meet Workforce Demands with Training
Developing in house training isn’t always a viable option, as many of the most in demand tech skills focus on emerging technologies. Because this tech is so new, most organizations don’t have the expertise they need in house. Even if there are tech greats on staff, they may not be great teachers or facilitators. Thus, they can’t effectively share what they know.
The next best option to an in-house training solution is an external one. By collaborating with the right technical training vendor, organizations can efficiently upskill their workforce, and be confident they’re dialed into a strong source for the latest technical skill as well as the most effective learning delivery.
However, organizations with a robust learning function aren’t always willing to look externally for training support. Because they have resources and experts in learning design and delivery, it’s only natural to try to create their own technical curriculum. That way they can avoid potential issues around a lack of customization or metrics, but this can be a mistake. Expertise counts – and not just learning expertise.
Upskilling and Collaboration, One Hand Washes the Other
Technology advances so rapidly, most companies find it difficult to pivot and adapt their training curriculum and learning delivery methods fast enough to accommodate industry changes. If they collaborate with the right training partner, however, they can not only avoid delivering dated technical training, they can establish the exact metrics by which to gauge training success. In this way, they can ensure business impact.
But that means finding just the right training partner. The word partner is key. The ideal technical training partner will not only have expertise in technical subject matter, they’ll have skilled facilitators and trainers as well. For example, DevelopIntelligence recruits the top working practitioners in the industry to teach its classes.
These experts in their respective technologies – big data, AI, machine learning, and a mélange of the most in demand programming languages – bring training delivery skill and real world expertise into the learning environment. The learning environment is classroom-based and lab intensive. Learners don’t sit and absorb theory, they enjoy continuous, in-depth practical application exercises alongside their peers.
Filling the Skills Gap
Data around the exact nature of the technical skills gap can be conflicting. Some reports say the gap is sizeable, others say the real problem is not that technical professionals lack skills, they just aren’t building the right ones, specifically the skills their employers need. Either way you look at it, the skills gap is real, and the training industry is prepared to help fill it.
According to a recent data analysis from job posting site Indeed, the tech skill training industry has expanded, and there is a greater emphasis on speed. This is in response to the pace of technology change and marketplace demands, and the fact that more specialized, in demand technical roles are still tough to fill with conventional recruiting methods. “In particular, tech struggles to find software engineers and architects, system engineers, data engineers, specialized developers, and data scientists,” the study read.
The longer the demand for technical talent continues to outpace supply, the more U.S. companies will struggle to keep pace not only with their international counterparts, but with their customers and with their industries demands. By upskilling the current technical workforce, companies can not only prepare their businesses to thrive in a challenging marketplace, they can promote talent retention and engagement, and remove the angst, expense and missed opportunities that come with always being in hiring mode – and still failing to bring in the right talent.
No matter what industry an organization falls into, technology is an undeniably important facet of modern business. To effectively address the technical skills gap, training is a logical and winning strategy. However, it’s not always easy to identify the right technical training partner.
Technical training providers must be willing collaborate closely with organizations to customize offerings and ensure that training curriculums address current and projected technical needs. It’s critical that training be intimately aligned to the business. Without that connection, the potential for poor outcomes increases, and that defeats the purpose of partnering with skilled, well-positioned technical training providers.
There’s no benefit in being plugged into the latest technology and having the classroom skills to share that knowledge with learners if training is happening in a vacuum. A close partnership will ensure that an organization’s technical talent get the training they need to advance business concerns today and prepare for innovation tomorrow.
For Salesforce, one of the world’s top customer relationship management platforms, technical training is all in a day’s work. Its global, engineer-heavy workforce needs continual development in order to provide the latest, most effective products for the more than 150,000 companies that use its CRM platform.
Caitlin Mann, the company’s Director of Technology, Marketing & Product’s Learning, said the right training is goal-driven and customized where needed. Further, when created with the right vendor partner, training is easier to scale and easier for the workforce to consume how and when they need it. Tailored training programs have been proven to improve engineers’ performance, and elevate their productivity. Mann spoke with DevelopIntelligence to discuss how and why their relationship works.
What’s The Best Solution When You’re Trying to Accommodate Tech Talent?
It depends. What are the goals of your program(s)? Are you trying to foster a culture of continual learning? Are you trying to develop a program to help move a business initiative forward? Are you trying to move the Titanic and build a program around org transformation? In an organization like ours all three are key. The best learning solution depends on the goals that you’re trying to achieve for whatever initiative you’re working on.
Our team was founded on a ‘continual learning’ model nine years ago. Traditionally our course offerings have been open enrollment, in-person classes, based on a number of different technologies and methodologies that are relevant to our employees. DevelopIntelligence has been key in creating this ‘continual learning’ catalog with ever-changing topics while using a tailored approach. For context, we offer over 200 classes each year through our open enrollment catalog.
Our learning programs based on business initiatives depend on what our leaders are looking for at that time and what’s happening within our business. Most recently it’s been machine learning. It’s a huge topic within the industry right now. A number of tech companies are looking at how to not only enable machine learning within their features, but how to create programs that allow engineers to learn and get hands-on with these new technologies.
Machine learning has been a huge shift in our partnership with DevelopIntelligence. We developed a five-day, hands-on class focused on the fundamentals of machine learning. We then used Salesforce-specific examples so our engineers could get hands-on and run some of these data sets through our own tools to see how it would impact the projects they’re working on.
We wanted a massive amount of people to go through the program. We had to look at a solution that was going to fit everyone’s needs, from how they prefer to learn to where they are located. We partnered with DevelopIntelligence to create the five-day class, then we built some Trails around that with Trailhead, our fun online learning platform. It became a blended learning solution that included in-person training plus online learning for the basics.
Now where blended learning solutions become key is when you’re building transformational programs. For us this means times when we want to focus on a shift in behavior or skills across the entire organization and complete it on a deadline; it’s like moving the Titanic on a dime. These are not one-and-done initiatives. These are programs that are part of a larger change management shift that are happening within the org. When these opportunities come along, it’s important we partner closely with our leaders to ensure that whatever our learning solution is, it meets the needs of the business and our learners.
The framework for our transformational programs include Trailhead content and resources, followed by hands-on classes tailored to the business needs, continual touch points with additional resources (docs, vidoes, research etc.), and ensuring they are working on applicable projects during the day so they practice what they’re learning. We then put a lot of the retention work back on the managers, asking them to make sure employees not only understand the content but ensuring it’s impacting their daily work and they’re practicing it every day. If people are not continually working on their craft, skill or mindset, it’s going to go away. Blended learning plus continuous practice is absolutely key.
Does That Require Customization?
If an Organization Decides to Go the Customized Route, Are There Certain Things They Need From a Vendor?
Absolutely. We always look for our vendors to be experts in the field we ask them to develop content on. We don’t want to just outsource content and completely release all responsibility. I have a team of engineers, instructional designers, program managers and learning and development professionals. We’re not reaching out because we don’t know how to develop this content; it’s mostly to help us scale.
However, we want to hear from our vendors on the industry standard. How are other companies succeeding in these areas? What are some of the tripwires that we should consider? We scale year over year, but DevelopIntelligence has worked with a number of companies that may have met some of the same challenges already. We’d love to learn from them to make sure that we’re as successful as we can be.
When it comes to learning and development vendors, I’d much rather it be a partnership where we work closely together to achieve our business goals and to produce the best outcomes. Plus, the instructors teaching the content, I would much rather have one who has been a professional within that industry for years versus someone always doing training. We can teach our own engineers to do train the trainer.
That’s one positive when you work with the right third-party vendor, you find people who know how to teach, are passionate about teaching and passionate about the subject area. Internally it can be difficult to find the right person with presentation skills, charisma, who knows how to make information stick and has subject matter expertise. When you work with DevelopIntelligence you get all of those.
We’ve worked with some instructors for years now. They’ve truly started to understand our culture and get to know our engineers. It speeds up the process quite a bit when we’re developing new courses. It also creates a lot of trust. I trust that when we show DI our content, it’s safe in their hands. They’ve always been incredibly open to working with us on iterations when we ask for them. They’re incredibly agile in how they get content out to us that’s up to date, and they iterate on the curriculum, which is fantastic.
DI Has Been Running Some Mico Training Sessions For You. What Prompted Those?
One of the business opportunities we were focusing on was how to scale our ILT classes, Instructor-Led Training. We have engineers around the globe, and the company continues to grow. The problem statement was ‘how do we get learning programs out to people at different locations with the same budget?’ DI helped us scale our programs to what we now call “Technical Targeted Topics.”
Keep in mind Technical Targeted Topics is not our only mode of training. We still have plenty of our traditional ILT classes where instructors are live in one of our global offices. However, the learning organization needs to grow and change along with the tech industry. This is another way for us to scale and meet the growing demands of our organization.
We’d say so. Engineering teams have to be good at more than just coding. Technical skills are actually not a primary gauge for a team’s success in a business context. Instead, the ability to work with others, to do your individual part to cultivate and sustain a productive and positive work culture, and to promote and exhibit a continuous desire to learn are just a few factors that can impact success more than being purely tech savvy.
Think about it. You could have the most technically brilliant software engineer or the most skilled coder on the planet on your team. If they don’t know how to work with others, to communicate effectively, and they have no desire to learn those things, is that team likely to be 100 percent functional? Nope. It’s just the opposite.
The classroom is an excellent place to hone collaboration, communication and technical skills, providing that classroom is run a certain way. For instance, technical staff will need to actively solve problems where they have to interact with their peers. For adult learners, small seminar-like environments that emphasize both theoretical and practical project work often pick up where traditional higher education learning falls short because of the immediate applicability of the learning, and the opportunity for peers to share their experiences, what worked for them on the job and what critical factors or perspectives may be missing in a given scenario.
For new hires or recent graduates who may not have much on-the-job experience, it’s helpful to be immediately grounded in real world problem solving so they can more easily transfer what they learn in the classroom to situations they encounter in the workplace. Knowledge transfer – when fueled by support from one’s peers and an instructor who has practical expertise as well as keen facilitation skills – is essentially what effective teaming is all about.
According to an April 2018 NPR article, “peer support turns out to be part of the secret sauce for adult success…students simply won’t let each other fail. This is a component of adult college-going that mass online completion colleges have trouble replicating.”
Think of it another way. For machine learning, AI and some of the other emerging technologies, it can be helpful to engage with peers while learning technical skills simply because the technologies aren’t established. Machine learning and other technologies are still actively evolving. Their applications are not set in stone, so collaborative sessions where learners can bounce ideas off one another can create the stimulus needed to develop innovative ideas to current problems.
Again, that only happens if the classroom operates in a way that nurtures that kind of result. For instance, a technical training classroom should include a diverse group of learners, ideally from the same team. With guidance, their different perspectives will take purely technical content around software development or any other topic, and move it into business problem solving territory. Once they leave the classroom, those conversations, that camaraderie and that spirit of cooperation are likely to continue back in the office.
Further, each person will learn more about what their peers do and how their individual work overlaps and builds to achieve business objectives. The best technical classes are not just about learning one program language, for example. They’re about effective teaming, software architecture as a whole, not one particular domain or project, building software right, following best practices, and testing and future-proofing the work.
That’s what a DevelopIntelligence classroom is like. Our various training offerings and DeveloperAcademies are results-driven learning environments. After two decades of testing, experience and client success, we’ve perfected the ability to create a technical learning and development experience that enables IT and R&D organizations to learn quickly and effectively. Part of that is due to our policy of deliberately and thoughtfully customizing curriculum so that it’s tailored to each client’s talent, skills, business needs, and existing internal systems.
Based on client data, our DeveloperAcademy model delivers 130% more technical learning engagement than similarly run training programs. Better learning creates more productive staff, better retention rates, and a competitive edge in the ultra-competitive tech talent market.
So, a well-run technical training classroom is an incubator for a lot more than just technical skills. So much can go on: mentoring, brainstorming and ideation, relationship building, an opportunity to practice interpersonal and communication skills. All of these things lead to one critically important skill: the ability to work effectively on a team. It truly is one of an engineer’s most valuable skills. If you doubt it, carve out some time to listen to engineers who’ve managed to escape from dysfunctional teams.
According to Intersog, a Chicago-based custom software engineering and IT staffing company, “A diversified team with a culture of collaborative behaviors is needed to deliver great software.”Teamwork is a skill one can learn, and it’s a skill that every developer and engineer needs to succeed. The depth of knowledge and support system a team can provide coupled with the skills and their application in a real-world context are the foundation upon which effective and technically superior teams are built. All of that can happen in the best technical training classrooms.
The CTO, chief technology officer, is a strategic role. Meaning, the individual in this role is primarily concerned with creating the high-level technology vision for an organization as well as its execution, but not necessarily the day-to-day decision-making. That vision could include creating a positive perception about technology for stakeholders who are leery, mistrustful or who hesitate to make necessary tech investments for fear of wasting resources following fads.
This individual likely has a technology background. A tech-based academic degree is common, as is a fruitful career featuring various levels of work as a practitioner and manager. But once ascending the tech career ladder to this top position, the CTO’s focus changes from choosing and operating tech systems to directing R&D, building innovation models and ensuring their organizations produce the best – technology-driven – solutions for customers. (more…)
If AI is a broad umbrella that includes the likes of sci-fi movies, the development of robots, and all sorts of technology that fuels legacy companies and startups, then machine learning is one of the metal tongs (perhaps the strongest) that holds the AI umbrella up and open.
So, what is machine learning offering us today? And what could it offer us soon? Let’s explore the potential for ML technologies.
Intro to machine learning
Machine learning is the process of machines sorting through large amounts of data, looking for patterns that can’t be seen by the human eye. A theory for decades, the application of machine learning requires two major components: machines that can handle the amount of processing necessary, plus a lot (a lot!) of gathered, cleaned data.
Thanks to cloud computing, we finally have both. With cloud computing, we can speed through data processing. With cloud storage, we can collect huge amounts of data to actually sort through. Before all this, machines had to be explicitly programmed to accomplish a specific task. Now, however, computers can learn to find patterns, and perhaps act on them, without such programming. The more data, the more precise machine learning can be.
Current examples of machine learning
Unless you are a complete luddite, machine learning has already entered folds of your life. Choosing a Netflix title based on prompted recommendations? Browsing similar titles for your Kindle based on the book you just finished? These recommendations are actually tailor-made for you. (In the recent past, they relied on an elementary version of “if you liked x, you may like y”, culled from a list that was put together manually.)
Today, companies have developed proprietary algorithms that machine learnings train, or look for patterns, on, using your data combined with the data of millions of other customers. This is why your Netflix may be chock full of action flicks and superhero movies and your partner’s queue leans heavily on crime drama and period pieces.
But machine learning is doing more than just serving up entertainment. Credit companies and banks are getting more sophisticated with credit scores. Traditionally, credit companies relied on a long-established pattern of credit history, debt and loan amounts, and timely payments. This meant if you weren’t able to pay off a loan from over a decade ago, even if you’re all paid up now, your credit score likely still reflects that story. This made it very difficult to change your credit score over time – in fact, time often felt like the only way to improve your credit score.
Now, however, machine learning is changing how credit bureaus like Equifax determine your score. Instead of looking at your past payments, data from the very near past – like, the last few months – can actually better predict what you may do in the future. Data analysis from machine learning means that history doesn’t decide; data can predict your credit-worthiness based on current trends.
What the future holds for machine learning
Machine learning is just getting started. When we think of the future for machine learning, an example we also hear about are those elusive self-driving cars, also known as autonomous vehicles.
In this case, machine learning is able to understand how to respond to particular traffic situations based on reviewing millions of examples: videos of car crashes compared to accident-free traffic, how human-driven cars respond to traffic signs or signals, and watching how, where, and when pedestrians cross streets.
Machine learning is beginning to affect how we see images and videos – computers are using neural networks to cull thousands of images from the internet to fill in blanks in your own pictures.
Take, for instance, the photo you snapped on your holiday in London. You have a perfect shot of Big Ben, except for a pesky pedestrian sneaking by along a wall. You are able to remove the person from your image, but you may wonder how to fill the space on the wall that walker left behind. Adobe Photoshop and other image editors rely on an almost-standard API to cull other images of walls (that specific wall, perhaps, as well as other walls that look similar) and randomize it so that it looks natural and organic.
This type of machine learning is advancing rapidly and it could soon be as easy as an app on our phones. Imagine how this can affect the veracity of a video – is the person actually doing what the video shows?
Problems with machine learning
We are at a pivotal point where we can see a lot of potential for machine learning, but we can also see a lot of potential problems. Solutions are harder to grasp as the technology forges forward.
The future of machine learning is inevitable; the question is more when? Predictions indicate that nearly every kind of AI will include machine learning, no matter the size or use. Plus, as cloud computing grows and the world amasses infinite data, machines will be able to learn continuously, on limitless data, instead of on specific data sets. Once connected to the internet, there is a constant stream of emerging information and content.
This future comes with challenges. First, hardware vendors will necessarily have to make their computers and servers stronger and speedier to cope with these increased demands.
As for experts in AI, it seems there will be a steep and sudden shortage in the professional manpower who can cope with what AI will be able to day. Behind the private and pricey walls of Amazon, Google, Apple, Uber, and Facebook, most small- and medium-sized businesses (SMBs) actually aren’t stepping more than a toe or two into the world of machine learning. While this is due in part to a lack of money or resources, the lack of expert knowledge is actually the biggest reason that SMBs aren’t deeper into ML. But, as ML technologies normalize, they’ll cost less and become a lot more accessible. If your company doesn’t have experts who knows how you could be using ML to help your business, you’re missing out.
On a global level, machine learning provides some cause for concern. There’s the idea that we’ll all be replaced in our jobs by specific machines or robots – which may or may not come to fruition.
More immediately and troubling, however, is the idea that imaging can be faked. This trick is certainly impressive for an amateur photographer, but it begs an important question: how much longer can we truly believe everything that we see? Perhaps seeing is believing has a limited window as a standard truthbearer in our society.
As it goes in technology, as soon as we all adapt a new term, there will assuredly be another one ready to take its place. As we embrace cloud technology, migrating functions and software for organization, AI potential, timeliness, and flexibility, we are now encountering yet another buzzword: serverless.
Serverless and the cloud may sound similar, both floating off in some distant place, existing beyond your company’s cool server room. But are the cloud and serverless the same? Not quite. This article explores how serverless technology relates to the cloud, as well as, and more importantly, whether you have to adapt a serverless culture.
What is serverless?
Serverless is shorthand for two terms: serverless architecture and serverless computing.
Once we get past the name, serverless is a way of building and deploying software and apps on cloud computers. For all your developers and engineers who are tired of coping with server and infrastructure issues because they’d rather be coding, serverless could well be the answer.
Serverless architecture is the foundation of serverless computing. Generally, three types of software services can function well on serverless architecture: function-as-a-service (FaaS), backend-as-a-service (BaaS), and databases.
Serverless code, then, relies on serverless architecture to develop stand-alone apps or microservices without provisioning servers, as is required in traditional (server-necessary) coding. Of course, serverless coding can also be used in tandem with traditional coding. An app or software that runs on serverless code is triggered by events and its overall execution is managed by the cloud provider. Pricing varies but is generally based on the number of executions (as opposed to a pre-purchased compute capacity that other cloud services you use may rely on).
As for the name itself: calling something “serverless” is a bit of a misnomer because serverless anything isn’t possible. Serverless software and apps still rely on a server, it’s just not one that you maintain in-house. Instead, your cloud provider, such as Google, AWS, Azure, or IBM, acts as your server and your server manager, allocating your machine resources.
The cloud vs. serverless
While the cloud and serverless are certainly related, there’s a better reason why we are hearing about serverless technologies ad nauseum. Because cloud leaders like AWS, Google, Azure, and IBM are investing heavily in serverless (and that’s a ton of money, to be sure).
Just as these companies spearheaded a global effort to convince companies their apps and data can perform and store better in the cloud, they are now encouraging serverless coding and serverless architecture so that you continue to use their cloud services.
Is everything serverless? Will everything be serverless soon? In short, no and no.
The longer answer is that serverless architecture and serverless computing are good for simple applications. In serverless coding, your cloud provider takes care of the server-side infrastructure, freeing up your developers to focus on your business goals.
Your developers may already be working on serverless code – or they want to be. That’s because it frees them from the headache of maintaining infrastructure. They can dispense with annoying things like provisioning a server, ensuring its functionality, creating test environments, and maintaining server uptime, which means they are focused primarily on actual developing.
As long as the functionality is appropriate, serverless can provide the following benefits:
- Efficient use of resources
- Rapid testing and deployment, as multiple environments are a breeze to set up
- Reduced cost (server maintenance, team support, etc.)
- Focus on coding – may result in increased productivity around business goals
- Familiar programming languages and environment
- Increased scalability
Traditional code isn’t going anywhere (yet)
While focusing on your core business is always a good goal, the reality is that serverless isn’t a silver bullet for your coding or your infrastructure.
Depending on your business, it’s likely that some products and apps require more complex functions. For these, serverless may be the wrong move. Traditional coding still offers many benefits, despite still requiring fixed resources that require provisioning, states, and human maintenance. Networking is easier because everything lives within your usual environment. And, let’s face it: unless you’re a brand-new startup, you probably already have the servers and tech staff to support traditional coding and architecture.
Computationally, serverless has strict limits. Most cloud providers price serverless options based on time: how many seconds or minutes does an execution take? Unfortunately, the more complex your execution, the more likely you’re go past the maximum time allowed, which hovers around 300 seconds (five minutes). With a traditional environment, however, there is no timeout limit. Your servers are dedicated to your executions, no matter how long they take or how many external databases they have to reference. This can make activities like testing and external call up harder or impossible to accomplish.
From a business perspective, you have to decide what you value more: only paying for what you use (caveat emptor), with decreased opex costs. Or, perhaps control is tantamount, as you are skeptical of the trust and security risk factors that come with using a third party. Plus, not all developers work the same. While some devs want to use cutting-edge technology that allows them to focus on front-end logic, others prefer the control and holistic access that traditional architecture and coding provides.
Using only traditional training does technical engineers and software developers a grave disservice for one very important reason: Things move too fast for traditional training methods to be effective.
Kyle Gabhart, a training and technology consultant for Gabhart Enterprises, said too often, software engineering follows a classical engineering model in how it approaches problems. That works if you’re building a physical bridge over a section of water, for example, because the river won’t get any wider. All of the variables are essentially fixed. The engineer knows up front that it’s a footbridge or for a train, and he or she can do all of the data collection, get things figured out, and then build it.
Software engineering is a different animal. The variables are constantly changing. Gabhart said the analogy would be, you’re half way through building a footbridge and the product owner says “hey, if you wouldn’t mind, can you add one quick little feature, and make it so that a train could go over the bridge as well?” In a normal engineering context that would be absurd. But in a software context it happens all the time.
“They’re like, it’s just a couple more fields, or it’s just a few more pages or entries in the database. Can’t you just make this one little change?” he said. “But the software environment is fundamentally different, and the way that people deal with it is different. We need a methodology and an approach that is sufficiently flexible to embrace the rate of change that has become the norm within the technology sector. Agile is really well suited for absorbing and embracing that type of change.”
Of course, agile technology means more than just flexible software development. It refers to flexibility in expectations around business outcomes as well as flexibility in the business need technologists are aiming to solve. It’s essentially a discovery process because in many environments there are a lot of unknowns and uncertainty. Agile helps to navigate that uncertainty, which is appropriate when the ultimate goal is to continuously innovate.
In order to promote agile or out of the box thinking and behavior in software development Gabhart said experiential training is best. There are some things one can learn in a classic academic setting, but it’s best to learn in a hands-on lab situation where technical talent can apply new concepts, see how they work, adapt and “rinse and repeat” over time.
“One thing that is often overlooked is the importance of having all the different roles involved in the training all the way through,” he said. “There’s a tendency for people to group up in their tribe whether that’s testers, developers, project managers or what have you, and agile more so than other approaches lends itself to having the entire team collaborating together, cross training.”
He said yes, participants will have different perspectives, and some elements of the training will resonate more readily with certain team members, but the value is in the collaboration because agile is not just about technology. It’s a collaboration and a communication activity that uncovers and clarifies a business need as well as the solution for that need. Diverse technical and non-technical perspectives facilitate that process and clarify the vision for the ultimate product or service under construction.
Roles will vary from client to client and situation to situation, but Gabhart said the ideal classroom might contain the product owner, scrum master, a few business analysts, the technical team, developers, a few testers and QA folks. That’s not what usually happens, however.
“Normally, you sit down and do a class with developers, then you sit down and have a class with testers, then you have a workshop with the project management team, and it’s like no no no. Everybody’s working on the same project,” he explained. “Let’s get whoever is going to be involved collaborating together; we want to mimic that in the training.”
In class, the team gets a scenario and with an instructor’s guidance, walks through all the paces. It’s not ‘okay, developers let’s drill into some random set of requirements and figure out how to build software from them.’ It’s ‘lets present a business problem, walk through the paces and have each stakeholder, from business analyst to product owner to tester, do their thing, supplying requirements, sharing stories, and receiving valuable feedback at critical points along the way.’
That’s the ideal classroom setting. Unfortunately, Gabhart said learning leaders aren’t receptive to the idea of a truly collaborative training environment.
“They should be, but their perception is, “it’s a three-day training. I don’t know that I can free up that person’s time, and aren’t ya’ll just going to be talking about a bunch of techy stuff anyway?” It’s a huge challenge,” he said. “I’ve seen it work well, yet time and time again we don’t have time, or they’re not available to do that. Or, I’ll set it up and get the non-technical folks for like half a day on day one. Then they bail and aren’t involved in the rest of the class.”
In the ideal training scenario, Gabhart said there likely will be times when the non-technical people zone out with the cartoon Peanuts teacher’s waa waa waa ringing in their ears as the technical people do their thing. But their presence is still vital to provide feedback at key points on, “did we hit the mark? Are the requirements that we defined consistent with your intent? Now we’ve built a prototype. Is that consistent with what you were expecting?”
Feedback, collaboration, having the non-technologists hear technology terminology in a different way, these things help to ensure the technology solutions developed actually solve business problems. “In the absence of solving a business need, technology literally has zero value,” Gabhart said. “You only have value to the extent that the technology enables the business to somehow be faster, better or cheaper.
“Remember, agile is intended for those contexts where there are a lot of unknowns, a lot of uncertainty, either in the tech, in the requirements, in the outcome, or all three. Because there’s uncertainty we have to iterate multiple times. We need feedback. Otherwise technical talent won’t know if they’re on target or not.”
Technology is changing how we design training, and it should. Unfortunately, many instructional designers are not producing the learning programs and products that today’s technical talent needs. Not because they don’t want to, but because many companies don’t support their efforts to advance their work technologically or financially.
That’s a mistake. Technology has already changed learning design. Those who don’t acknowledge this appropriately are doing their organizations – and their technical talent – a disservice.
Bob Mosher, chief learning evangelist for Apply Synergies, a learning and performance solutions company, said we can now embed technology in training in ways we never could before. E-learning, for instance, has been around in some for or another, but it always sat in an LMS or outside of the technology or whatever subject matter it was created to support. That’s no longer the case.
“Now I don’t have to leave the CRM or ERP software, or cognitively leave my workflow,” Mosher explained. “I get pop ups, pushes, hints, lessons when I need them, while I’m staring at what I’m doing. These things guide me through steps; they take over my machine, they watch me perform and tell me when and where I go wrong. Technology has allowed us to make all of those things more adaptive.”
Of course, not all learning design affected by technology is adaptive, but before adaptive learning came on the scene, training was more pull than push, which can be problematic. If you don’t know what you don’t know, you may proceed blindly thinking that, “oh, I’m doing great,” when you’re really not. Mosher said adaptive learning technologies that monitor learner behavior and quiz and train based on an individual’s answers and tactics, can be extremely powerful.
But – there’s almost always a but – many instructional designers are struggling with this because they’re more familiar with event-based training design. Designing training for the workflow is very different animal.
The Classroom Is Now a Learning Lab
“It’s funny, for years we’ve been talking about personalized learning, but we’ve misunderstood it thinking we have to design the personalized experience for every learner,” Mosher said. “But how do I design something personalized for you? I can give you the building blocks, but in the end, no one can personalize better than the learners themselves. Designing training for the workflow is a very different animal.”
In other words, new and emerging technologies are brilliant because they enable learners to customize the learning experience and adapt it to the work they do every day. But it’s one thing to have these authoring technologies and environments; it’s something else for an instructional designer to make the necessary shift and use them well.
Further, learning leaders will have to use the classroom differently, leveraging the different tools at their disposal appropriately. “If I know I have this embedded technology in IT, that these pop ups are going to guide people through, say, filling out a CRM, why spend an hour of class teaching them those things? I can skip that,” Mosher said. “Then my class becomes more about trying those things out.”
That means learning strategies that promote peer learning, labs and experiential learning move to the forefront, with adaptive training technology as the perfect complement. Antiquated and frankly ineffective technical training methods filled with clicking, learning by repetition through menus, and procedural drilling should be retired post haste in favor of context-rich learning fare.
Then instructors can move beyond the sage-on-the-stage role, and act as knowledge resources and performance support partners, while developers and engineers write code and metaphorically get their hands dirty. “If I have tools that help me with the procedures when I’m not in class, in labs I can do scenarios, problem solving, use cases, have people bounce ideas and help me troubleshoot when I screw up,” Mosher said. “I’m not taking a lesson to memorize menus.”
Learning Leaders, Act Now
Learning leaders who want to adapt to technology changes in training design must first secure appropriate budget. Basically, you can’t use cool technology for training unless you actually buy said cool technology. Budgetary allocations and experimentation must be done, and instructional designers have to have the time and latitude to upgrade their skills as well because workflow learning is a new way of looking at design.
“Everyone wants agile instructional design, but they want to do it the old way,” Moshers said. “You’re not going to get apples from oranges. Leadership has to loosen the rope a little bit so instructional designers (IDs) can change from the old way of designing to the new way.
“IT’s been agile for how long now? Yet we still ask IDs to design in a waterfall, ADDIE methodology. That’s four versions behind. Leadership has to understand that to get to the next platform, there’s always a learning curve. There’s an investment that you don’t get a return on right away – that’s what an investment is.”
For learning leaders who want to get caught up quickly and efficiently, Mosher said it can be advantageous to use a vendor. They’re often on target with the latest instructional design approaches and have made the most up to date training technology investments. But leadership must communicate with instructional designers to avoid resistance.
“Good vendors aren’t trying to put anybody out of a job, or call your baby ugly,” he explained. “It’s more like, look. You’ve done great work and will continue to do great work, but you’re behind. You deserve to be caught up.”
The relationship should be a partnership where vendor and client work closely together. “Right,” Mosher said. “If you choose the right vendor.”