ACD200 certification - Appian Certified Senior Developer? Updated: 2024

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The world is currently full of `hot’ stories about where AI is going to take us, and do to us, and the state we will be in by the time it has finished with us. It is true that it may gain some semblance of sentience at some time in the future, but it is far from that at the moment. And the argument now is very strong that the `A’ is AI should stand for 'Augmented’ not 'Artificial’. When looked at that way the obvious question for potential users then becomes `what do you want augmented – what help do you need to augment it, where in any process do you need it, and how do you make it happen?’

This subject was addressed at a recent London conference hosted by low-code/business process management vendor, Appian. CEO Matt Calkins side-stepped the hype and used his keynote address to set about outlining where AI is going to help in that all-important area of `making businesses run better’ over the next couple of years.  It was, therefore, low on scary content, but did address the underlying reality that business managers will need to be ready for the significant changes already barrelling down on them if they are not to be caught unawares and unready: 

We need to move from hype to reality. We've talked a lot about how AI can get us to an amazing place. Now we need to figure out how we actually go there: where are we actually trying to go with this technology? AI can take us to a human centric future. AI is going to be a partner instead of a substitute for people.

Calkins also sees the coming changes being more democratically available to businesses of all types and sizes rather than exclusively for large enterprises, a view he suggested is different from current conventional wisdom. And it's different from the view of the investment community as well, which has so far bet heavily on the big technology names because, as he put it, investors believe that they're going to use AI as a tool to gain advantage over all the rest of us: 

I think investors think Big Tech is going to find a way to use AI to get our data, take our money, put us in a position where we have to rent the best technology in our entire enterprise. But we have to move our conversation away from wild predictions and toward demonstrations of effectiveness of AI in the workplace.

Don’t waste your data

What Calkins sees is a need for users to understand that AI in the workplace is not a standalone technology, rather, it is symbiotic with - even dependent on - two other areas, data and process, without which it will not have any tangible effect. He sees it as a triangle of these three primary, interdependent and synergistic areas:

AI without data would be like a card trick with no cards. It is just re-shuffling cards, just re-shuffling data; taking what it's learned, putting it into a new configuration, and putting it back out to you.

Current models have it that AI is not only dependent on data, but also dependent on two data sources: the `past’ in the form of a training data set, and the data that comprises the ‘present’. AI then relates the two of those together.  Therefore, the value of the data presented is key to the value that comes out of any AI process. In Calkins’ view most data today is waste. For example, most data that's known to organisations is not utilized properly because it's not brought to bear at the moment of decision. The moment a customer calls a call center, that's when a business needs to know about that customer in order to react properly. If that does not happen, (within 10 seconds, in his opinion) then the data is effectively wasted.

He sees AI solving much of this, allowing data to be accessed in real time…in theory. In practice, this will depend on the effectiveness of the interface between AI and the rest of the business. This is an area Appian is focussing on, but Calkins has doubts about how many users are aware of it, or the value of it. Added to that is then the business need to understand how best to get to that value:

I'd say your data is maybe double as valuable as it was a couple of years ago. And most people don't realise that they haven't really talked about it. AI isn't 100% reliable. Instead, we are entering a long term period in which AI will write, but humans will edit, AI will propose, humans will decide. It's a team effort that's going to last a long time.

One of the keys here, so far as Calkins is concerned, is Appian’s development of its Data Fabric, which has clocked up 4 billion queries despite still being rolled out to all of the users of the software. The amount of effort put into developing and implementing Data Fabric has led Calkins to suggest that Appian has got close to becoming a Data Fabric company, specialising in the development and application of the technology above and beyond everything else. 

Doing AI without training

Its importance to the application of AI by the company can now be seen as some explanation for the effort that was made. That all hinges round Calkins’ enigmatic statement to the conference delegates:

We're not going to try to take your data; we want to facilitate the enterprise that you've already got.

This has to do with the way that current AI implementations tend to work in practice, where a user’s data can be at risk as a core part of the process, while it is being used. The Appian Data Fabric is at the heart of this because it connects the entire enterprise via what is, in effect, a virtual database layered across all the data sources that exist in that enterprise. This allows users you to run rapidly-built queries and get answers based on the latest live information from any data source connected to the Data Fabric. with full row-level security. 

Calkins was keen to stress that the provision of row level security plays a vital role in the implementation of AI for practical business applications, not least because the fear of many businesses that using AI services puts their data at risk is very real for them. After all, it is the way that most AI services now operate. Most of the services are only available via the mainstream public cloud service providers, so in order to train a Large Language Model service in its particular business and requirements, a user company first has to upload its data to the service.  

We believe that it is a non-starter to load your data, especially enterprise data, onto a public AI platform, just a non-starter.

His point here is that in order to train an AI system sitting on a public cloud service, a business has to feed it with its own real data, and that makes it difficult to definitively claim that it owns that data any more. As an operating model, it does mean they can then work well - questions can be sent, the context derived from the training data and an answer delivered - but a company may have difficulty claiming in  now 'owns’ its data, and it has to keep feeding in new data to the training process. 

One way round that is for a company to install an open source AI inside its firewall, training and using it effectively 'on-premise’. The downside here is that it then requires both a high level of expertise on the part of staff, and a high level of ongoing, dedicated investment to keep the service level up to scratch. So Appian is in the throes of developing a new model for utilising AI that prioritizes data security and, as an added advantage, is expected to be faster, more accurate, and less complex in operation.

The third way to do it is to put a generic, plain vanilla AI in a private cloud space, and then inform it in real time. This is where having a Data Fabric available becomes important. Before a question is actually posed to the AI service a query is run against the entire enterprise data set, a task that requires a Data Fabric. This surfaces all the company data relevant to the query and this is sent, together with the question, to the vanilla AI service in a private cloud. Combining the question with all relevant current data extracted from the live business applications gives the AI the not only the information but also the context underpinning the question.

This will make it far more secure, for the data can be deleted as soon as the result has been accepted. It will also be more cost-effective as users will not have to pay to store large amounts of training data, ongoing data upload fees and running costs of a large cloud services site. Finally, its answers are auditable. The source data can be filtered to remove information that is not relevant for the question being asked, which can also help manage the introduction of accidental biases.

My take

If AI achieves anything over the next few years it will be an almost brutal demonstration of the famous Gartner Hype Cycle in action. At the moment, the expectations for AI are humungous – and continuing to grow – with possible results for humanity ranging from a future life of enduring, blissful indolence to total annihilation. So reaching the 'Peak of Inflated Expectations' still seems a long way off. The subsequent fall into the 'Trough of Disillusionment', when it comes, will no doubt be equally rapid – with the likelihood of many corporate fingers being badly burned.

But as and when AI moves on to the Plateau of Productivity, an operational model such set out by Calkins is likely to be playing a significant part in achieving that goal. It targets a role for AI that just about every business will need at their core, whatever their size, and offers a way of achieving it, if Calkins proves right, that offers speed, flexibility, accuracy and, above all, greater security.    

AI is big. The deployment of Artificial Intelligence (AI) continues to dominate the software application development landscape with commentators, industry analysts, AI evangelists and chief AI officer (CAIO) positions now being created as adjuncts to the traditional C-suite tech leadership roles.

The conversation in this space appears to circulate between the backend infrastructure we will require to handle the compute power needed for AI, through the middle tier where the industry is starting to worry more pressingly around guardrails and AI’s exposure to Personally Identifiable Information (PII)... and then onward to the upper tier of the applications themselves with the use of co-pilot tools for humans and the looming spectre of AI bots having (and indeed populating) their own social media accounts.

As this move to mainstreaming predictive, reactive and of course generative AI has continued, many of the new services we’re seeing are making use of Natural Language Processing (NLP) techniques at their heart to drive the use of Large Language Models (LLMs). Notable examples include OpenAI’s GPT, PaLM from Google AI being used to power Bard, Anthropic’s Claude AI, Meta’s LLaMA, BLOOM and Ernie Titan. As a core chunk of algorithmic logic, most organizations don’t have the resources, time or skills needed to create their own LLM, so this reality again fuels industry chatter around which LLM to use and how closely we need to look at its DNA.

Given the complexity that we face here (in the pursuit of simplicity), how should IT teams think about their use of these core components of AI and what automations are available to make the process easier?

The right foundation foundations

“The first step when considering different LLMs is to identify an organization’s business problem or an opportunity for improvement. We can then move to understand the tasks, processes and people involved to establish clearly defined use cases. It’s important to realize that different LLMs have different architectures, training data, strengths and suitable use cases,” said Ysanne Baxter, solutions consultant at low-code software platform and process automation company Appian. “Commercially available LLMs are often used as a base model in other projects, which provides a platform to introduce further bespoke or specialized tasks. Designed for flexibility and scalability, these language models generally yield better results when the foundation dataset is vast, diverse, and complex.”

Baxter explains that the differences are down to LLM architecture, potentially resulting in different outputs. For a start, a model needs to have a vast body of textual data to draw from - let’s say, a set of customer reviews about a product or service - and if an organization’s objective is to determine the reviews’ main points or themes, it could then use findings to generate a set of responses and new features to improve the offering. Baxter says that multiple models could help tackle this kind of task and there are tools to make it possible to train multiple models at once. Software developers can then assess the actual metrics after this process is complete, enabling them to compare these outputs to help guide their assessment of which is likely to be the best fit.

Ultimately, the question is: which is the most appropriate LLM for the task? If an organization has framed its problem (or opportunity) well and understands the data it has to work with and the desired outcomes, then it has a good foundation to start with when assessing models to find the best fit for the task at hand.

Low-code accelerators

“Tackling ML/AI is no small feat and knowing where to start can be overwhelming. Low-code development tools can make this undertaking significantly more accessible with a common language,” asserted Baxter. “Developers could use low-code tools to draw on expert teams with a deep understanding of these technologies. We don’t need to individually define a new model from scratch to perform every new task, but instead, we could use their deep research and development of ML/AI tools to help us achieve the same objectives. Low-code approaches essentially become a multi-disciplinary collaboration in this way: taking that expertise and adding our own, applying it in a way that’s deeply relevant to our organization in a fraction of the time.”

The Appian team say that there are plenty of use cases where existing low-code AI tools are appropriate and sufficient. For example, many email providers already use NLP to help classify and separate spam from an inbox. Developers could use low-code tools for appropriate AI use cases for productivity gains, as it would be very time-intensive to keep engineering similar products or features anew.

“Ultimately, low-code tools allow developers to build on the best in our industry. Like in academia, quality research always builds on what came before; it can break new ground, but there’s always the context of the existing body of work. When one applies low-code approaches to create new AI solutions leveraging an existing LLM, the result is hyperproductivity, output and impact in accelerated time,” advised Baxter. “Protecting or omitting personal and mission-critical data requires a thoughtful approach at every step of development. With an ‘out of the box’ LLM, there is little to determine what qualifies as sensitive data. You could put your UK national insurance or US social security number into ChatGPT and as far as I know, there is nothing, in the model alone, to stop it from being ingested and shared in full.”

Open or closed source?

While we’re busy working out how to assemble the componentry inside our AI models and where to make LLM connections, there’s also a lot of plenty of debate as to whether we should be adopting open source or closed source models, datasets and perhaps today (what with the amount of licensing change we have seen in recent months) even languages and operating systems.

In general terms, Baxter and the Appian team would say that an open source project will enable us to review source materials and assess their usefulness, this is useful because the extensive size of data sets required for performant LLMs can make analyzing the data time-consuming and often impossible. This requires completing an impact/importance assessment. Closed source models often exhibit a ‘black box’ effect, which reduces the ability to interrogate and scrutinize the model's inner workings. Total transparency in technology is often the goal - but these are not the only methods of safeguarding data.

“One technique for enhancing the accuracy and reliability of generative language models is Retrieval Augmented Generation (RAG). This combines using LLMs with a knowledge base of external sources. The first step is to establish whether the dataset is appropriate and fit for purpose. Next is to implement robust access control measures and establish a comprehensive process to remove sensitive or confidential information from the data sources. Finally, validate incoming queries to the system,” heeded Baxter, echoing the industry’s now quite upbeat attitude to RAG and its ability to ratify AI data with additional exposure to trusted or at least acknowledged sources.

A selection of prompt injections

We also need to understand more about prompt injections i.e. the process of skewing and influencing how an AI model reacts in various different ways. As detailed here, we can see that prompt injections range from basic injection attacks, translation injections (attempting to throw a curveball at an AI model and LLM by asking questions in a different human language), maths injections, external prompt injections and the ever so sneaky context switching injection technique i.e. asking a model about holidays to Greece and the price of cabbage in Alabama at the same time.

They contravene previous instructions or guardrails provided to the model and manipulate future outputs. An attacker would seek to find a system vulnerability that enables them to inject prompts that drive outputs that would usually be unauthorized or undesired to the developer(s) of the original model.

“It can be challenging to identify system vulnerabilities to prompt injections, especially when using natural language, as there is a much wider scope for attackers to get creative and circumvent security measures,” said Baxter. “With something like a SQL injection, the attacker would need to provide injections that result in valid SQL syntax. But when natural language is used, the syntax is much more malleable. In essence, the same levels of structure are not present as a guide when considering potential countermeasures. Validating and sanitizing inputs provided by users is vital. Developers can support cybersecurity colleagues by sharing insights into how these tools are implemented and manipulated.”

Future (specified) trajectories

Asked about the future of AI and how we use LLMs in modern enterprise software systems, most of our initially mentioned evangelists that work in this space will talk about the move to more specifically aligned LLMs and the need to provide a more specialized focus with these technologies.

Appian’s Baxter concurs with this sentiment and suggests that the future trajectory of LLMs points toward task-specific and industry-specific applications... and so notes that, “While the current landscape features broad models and narrowly focused solutions, the future will see a tier of offerings that strike a balance with industry contextualization and the flexibility to cater to specific use cases.”

The good news, perhaps, here is that a) we’re not just seeing another IT simplicity story directly creating more complexity (we hope) and that b) the development and application of new AI strains will be a task that software application developers do alongside business domain specialists and non-technical professionals who simply know how things work in the real world.

As Baxter put it, the path forward requires a 'collaborative and nuanced' approach, so let’s make sure that’s not the road less travelled.

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You can beef up your training and development expertise and show your value to employers by obtaining a relevant certification. Read on for the top certification programs to help you get ahead in the world of training and development. 

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Machine learning stocks sit at a powerful intersection of artificial intelligence (AI) and business growth. We can largely attribute the success of tools like ChatGPT to machine learning technology, which has spawned a new arms race for companies to crown themselves as having the dominant platform. Yet machine learning stocks are pioneering other fields of tools, such as medicine for assessing patients’ risk of contracting cancer to banks assessing a creditor’s risk of default.

Therefore, machine learning stocks have wide and varied applications across various industries, so it may be worth investigating some of the lesser-known brands to include in your portfolio. In this article, we’ll cover three companies that aren’t exactly marquee names but hold great potential for investors willing to look beyond the most obvious choices.

So here are the best machine learning stocks for investors to consider buying.

C3.ai (AI)

Source: Owlie Productions / Shutterstock.com

C3.ai (NYSE:AI) specializes in enterprise artificial intelligence applications. There’s a strong bull case for buying AI stock right now since Oppenheimer recognized it as a stock to watch in 2024. The bank described AI stock as an “infrastructure” company predicted to perform strongly next year.

Some company-specific catalysts are also on the horizon for AI stock, including an anticipated revenue boost from increased data collection and model training in the year’s second half.

Some analysts have praised the company for its strides in bringing generative AI to enterprise clients. The biggest hurdles these large businesses face revolve around data security and hallucination problems, which can’t be met with off-the-shelf solutions that companies like OpenAI provide.

AI stock claims that its generative AI solution will prevent cloud leakage and be free from hallucinations, making the technology much more attractive and usable for large corporations.

Given its position in the industry and growth prospects, C3.ai is one of those machine learning stocks to buy.

Appian (APPN)

Source: JHVEPhoto / Shutterstock.com

Appian (NASDAQ:APPN) integrates machine learning into its low-code automation platform. It allows developers to deploy quickly and scale applications that would otherwise take weeks or months of testing and development, saving organizations time and potentially reducing overheads.

APPN is my contrarian pick of these machine learning stocks, mostly due to its disappointing recent quarter. Appian shares fell 5.88% after posting its Q3 results to the market last month. Much of the pessimism stemmed from its revenue and adjusted EBITDA guidance falling below expectations. It also slightly missed Wall Street’s full-year revenue estimates and continued to burn through its cash reserves quickly.

However, there is some optimism on the horizon. Wall Street, for instance, rates APPN stock as a “Buy,” and this comes with a $48.33 implied price target from nine analysts. It’s also trading at just 5.29 times sales and has a five-year revenue growth forecast of 18.47%.

As a volatile machine learning stock, APPN is prone to up-and-down swings as it continues to onboard new users to its platforms. There are no structural problems for its business that I can make sense of, and with a lower interest rate environment expected into next year, we could see a substantial rebound in its share price moving forward.

Elastic N.V. (ESTC)

Source: Tada Images / Shutterstock.com

Elastic N.V. (NYSE:ESTC) is a cloud-based enterprise data search company that uses machine learning to deliver relevant results to users. Like other companies on this list, ESTC stock leverages generative AI in the enterprise space to the great enthusiasm of its clients.

Its recent financial results have also been impressive. Q2 revenue came in at $311 million, up 17% year-over-year. Meanwhile, its FY 2024 guidance was also increased to $1.24 – $1.25 billion.

Although ESTC stock is currently unprofitable, analysts predict that it may break even within the next twelve months and also forecast a five-year revenue growth of 19.63%.

ESTC also looks like a safer option than other machine-leaning startups, having nearly a billion dollars of cash and cash equivalents on its balance sheet and burning through only around 12% of that in 2023. This combination of factors makes it one of those machine learning stocks to buy.

On the date of publication, Matthew Farley did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

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‘The number of consumers who interacted with Alexa doubled in 2018, so we see a large number – hundreds of thousands of developers -- developing skills on it,’ says Maureen Lonergan, AWS’ director of worldwide training and certification.

Amazon Web Services today rolled out its new specialty certification for developers working with Alexa, Amazon’s cloud-based, voice-enabled virtual assistant that’s available on more than 100 million devices from Amazon – including the Amazon Echo smart speakers -- and third-party manufacturers.

The AWS Certified Alexa Skill Builder Specialty certification exam, which AWS says is the industry’s only certification that validates developers’ ability to build, test and publish Amazon Alexa skills, has gone live at testing centers after a beta period that started in January.

“The number of consumers who interacted with Alexa doubled in 2018, so we see a large number – hundreds of thousands of developers -- developing skills on it,” said Maureen Lonergan, AWS’ director of worldwide training and certification.

“Skills” are essentially third-party apps that extend Alexa capabilities and allow it to perform certain tasks, such as connecting to and controlling smart devices at home or an office. Companies can build Alexa into their products to enable voice-enabled features including streaming media, speech recognition, notifications, weather reports and other capabilities. There are tens of thousands of skills in the Alex Skills Store in categories including business and finance, communication, the connected car, games and trivia, education and reference, home services, music and audio, news, productivity, shopping, travel and transportation, and utilities.

Some 20 million vehicles shipping in 2023 are expected to be equipped with Amazon Alexa, Google Assistant – the artificial intelligence-powered virtual assistant developed by Google -- or both, according to an ABI Research report released last week. Amazon has been testing features for Alexa to aid in its entry into the healthcare industry, according to a Morgan Stanley report last week that said Amazon has filed a patent for Alexa to detect when a user is sick and help sell users' medications.

AWS announced the beta version of the Alexa certification exam in January at the Consumer Technology Association’s CES trade show, where more than 100 Alexa products were announced by companies including LG, Lenovo, Audi, Bosch, North and Razer.

“The (exam) registrations out of the gate were very rapid,” Lonergan said.

AWS closed its availability after several hundred people registered and used a third-party vendor to run the test takers’ answers through a psychometric analysis to validate exam questions.

“This exam is going to allow companies to validate skills that they’re hiring for,” Lonergan said. “There’s a lot of organizations building Alexa-enabled applications, and having the right skills and being able to validate the skills to build on Alexa is going to be important.”

Alexa came under deepened scrutiny last week after Bloomberg reported that a global team of Amazon workers transcribes and annotates Alexa voice recordings captured from Echo owners and feeds them back into software to help the voice-activated assistant better respond to commands.

An Amazon spokesperson said the company only annotates an “extremely small number of interactions from a random set of customers in order to improve the customer experience.”

“This information helps us train our speech recognition and natural language understanding systems, so Alexa can better understand your requests and ensure the service works well for everyone,” an Amazon spokesperson said. “We have strict technical and operational safeguards, and have a zero-tolerance policy for the abuse of our system. Employees do not have direct access to information that can identify the person or account as part of this workflow. While all information is treated with high confidentiality, and we use multi-factor authentication to restrict access, service encryption, and audits of our control environment to protect it, customers can delete their voice recordings associated with their account at any time.”

Amazon said no audio is stored or sent to its cloud unless a device detects the “wake word.”

“By default, Echo devices are designed to detect only your chosen wake word –'Alexa,’ ‘Amazon,’ ‘computer’ or ‘Echo,’” an Amazon spokesperson said. “The device detects the wake word by identifying acoustic patterns that match the wake word. No audio is stored or sent to the cloud unless the device detects the wake word or Alexa is activated by pressing a button.”

Certified Alexa skill builders will receive a downloadable AWS Certification digital badge that can be shared on social media and in their email signatures, and the badge will give them access to AWS Certified Lounges at AWS events.

AWS recommends Alexa exam takers have at least six months of hands-on experience designing and building Alexa skills, proficiency with a programming language and at least one published Alexa skill.

AWS now has 11 certification exams, and the Alexa exam is the fifth in its specialty series. It has a couple more specialty exams under development that it plans to launch later this year, according to Lonergan, who declined to reveal details.

“We’re continuing to listen to what our customers need and how they want to validate the skills of the people they’re hiring,” she said.