How To Create An AI (Artificial Intelligence) Startup

According to research from IDC, the global spending on AI (Artificial Intelligence) is expected to hit $97.9 billion by 2023, up from $37.5 billion in 2019. This represents a compound annual growth rate of 28.4%.

No doubt, this is an enormous opportunity for startups. “These days, almost every company needs to leverage AI in order to thrive and build a meaningful future,” said Saar Yoskovitz, who is the CEO of Augury. “This is true for younger startups, and it is true for the largest companies, even in the most traditional and nascent industries like manufacturing and insurance. In a sense, AI has become another layer in the tech stack, like databases, and not a business model.”

All this is true. But then again, finding the right product-market fit is extremely challenging. Consider that AI models generally require a lot of customization. In fact, there is often much variation even for companies in the same industry. 

And yes, there must be a high-quality dataset. But of course, this is far from easy for a startup to develop.

“Data creates an interesting chicken-and-egg problem,” said Yoskovitz. “Without a customer, you don’t have data, which means you cannot train your algorithms. Without algorithms, you are not able to provide value to your customers and compete in the market. Therefore you will not have customers to provide data.”

What to do? You can look at forming partnerships, such as with companies that may not have strong AI capabilities. Or another approach is to create a free app that collects data.

“What we have found is that to build a successful AI startup, the key is sourcing and building proprietary data,” said Saniya, who is the CEO and co-founder of Pilota. “This is what makes your business defensible and attractive to investors. Almost every investor has asked us where our data comes from and what would stop others who can access this data from replicating what we do. So when creating a business centered around AI, it is extremely important to make sure that your data is proprietary and you are not just building a business on analyzing public datasets.”

But even with a good dataset, this is still not enough. For example, does the AI model make a real difference? Or are the results mostly minor improvements?

“Investors are interested in startups that are building tailored AI solutions for previously unsolvable problems,” said Jay Srinivasan, who is the CEO and co-founder of atSpoke. “So focus on areas where there are many inefficiencies and repetitive human processes, such as call centers and back-office paperwork processing. Investors want successful AI solutions that address specific workflows and problems, such as a legal document review.”

Then there is the issue of adoption.  Let’s face it, customers are still leery of the powers of AI. It does not help that the models are often complex and opaque. There are also the nagging problems with bias.

“Know your customer,” said Vasiliy Buharin, who is the Associate Director of Product Innovation at Activ Surgical. “You may have the algorithm to solve the worst LA traffic or shorten airplane boarding time 10-fold. But if the solution requires people to behave like preprogrammed automatons, it will never be adopted, and your company will fail. Your customer has a certain way of doing things. Your product must fit this workflow.”

Given all the challenges, a startup will likely undergo multiple pivots.  This is why you need a top-notch team that works well together. 

“If you start with a problem and leave yourself open to many different solutions, you will learn from the market and find the best solution to build your business around,” said Sean Byrnes, who is the CEO of Outlier. “Spending six months exploring and selecting the right problem can save you six years of wasted effort trying to build a business around a flawed idea.”

MLOps: What You Need To Know

MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for “machine learning operations.” Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. 

“MLOps is the natural progression of DevOps in the context of AI,” said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University’s School of Information Security & Applied Computing (SISAC). “While it leverages DevOps’ focus on security, compliance, and management of IT resources, MLOps’ real emphasis is on the consistent and smooth development of models and their scalability.”

The origins of MLOps goes back to 2015 from a paper entitled “Hidden Technical Debt in Machine Learning Systems.” And since then, the growth has been particularly strong. Consider that the market for MLOps solutions is expected to reach $4 billion by 2025. 

“Putting ML models in production, operating models, and scaling use cases has been challenging for companies due to technology sprawl and siloing,” said Santiago Giraldo, who is the Senior Product Marketing Manager and Data Engineer at Cloudera. “In fact, 87% of projects don’t get past the experiment phase and therefore, never make it into production.”

Then how can MLOps help? Well, the handling of data is a big part of it.

“Some key best practices are having a reproducible pipeline for data preparation and training, having a centralized experiment tracking system with well-defined metrics, and implementing a model management solution that makes it easy to compare alternative models across various metrics and roll back to an old model if there is a problem in production,” said Matei Zaharia, who is the chief technologist at Databricks. “These tools make it easy for ML teams to understand the performance of new models and catch and repair errors in production.”

Something else to consider is that AI models are subject to change. This has certainly been apparent with the COVID-19 pandemic. The result is that many AI models have essentially gone haywire because of the lack of relevant datasets. 

“People often think a given model can be deployed and continue operating forever, but this is not accurate,” said Randy LeBlanc, who is the VP of Customer Success at RapidMiner. “Like a machine, models must be continuously monitored and maintained over time to see how they’re performing and shifting with new data–ensuring that they’re delivering real, ongoing business impact. MLOps also allows for faster intervention when models degrade, meaning greater data security and accuracy, and allows businesses to develop and deploy models at a faster rate. For example, if you discovered an algorithm that will save you a million dollars per month, every month this model isn’t in production or deployment costs you $1 million.”

MLOps also requires rigorous tracking that is based on tangible metrics. If not, a project can easily go off the rails. “When monitoring models, you want to have standard performance KPIs as well as those that are specific to the business problem,” said Sarah Gates, who is an Analytics Strategist at SAS. “This should be through a central location regardless of where the model is deployed or what language it was written in. That tracking should be automated–so you immediately know and are alerted—when performance degrades. Performance monitoring should be multifaceted, so you are looking at your models from different perspectives.”

While MLOps tools can be a huge help, there still needs to be discipline within the organization. Success is more than just about technology. 

“Monitoring/testing of models requires a clear understanding of the data biases,” said Michael Berthold, who is the CEO and co-founder of KNIME. “Scientific research on event, model change, and drift detection has most of the answers, but they are generally ignored in real life. You need to test on independent data, use challenger models and have frequent recalibration. Most data science toolboxes today totally ignore this aspect and have a very limited view on ‘end-to-end’ data science.”

Python Language: What You Need To Know

Python is one of the world’s most popular computer languages, with over 8 million developers (this is according to research from SlashData).  The creator of Python is Guido van Rossum, a computer scientist and academic. Back in the late 1980s, he saw an opportunity to create a better language and also realized that the open source model would be ideal for bolstering innovation and adoption (by the way, the name for the language came from his favorite comedy, the Monty Python’s Flying Circus).

“Python is a high-level programming language, easy for beginners and advanced users to get started with,” said Jory Schwach, who is the CEO of Andium.com. “It’s forgiving in its usage, allowing coders to skip learning the nuances that are necessary in other, more structured languages like Java. Python was designed to be opinionated about how software should be created, so there’s often just a single appropriate way to write a piece of code, leaving developers with fewer design decisions to deliberate over.”

A way to get started with the language is to use a platform like Anaconda, which handles the configurations and installs various third-party modules. But there are cloud-based editors, such as REPL (I also have my own course on Python, which is focused on the fundamentals).

“Python has become the most popular language of choice for learning programming in school and university,” said Ben Finkel, who is a CBT Nuggets Trainer. “This is true not just in computer science departments, but also in other areas as programming has become more prevalent. Statistics, economics, physics, even traditionally non-technical fields such as sociology have all started introducing programming and data analysis into their curriculum.”

No doubt, a major catalyst for the growth of the language has been AI (Artificial Intelligence) and ML (Machine Learning), which rely on handling huge amounts of data and the use of sophisticated algorithms. 

“Because Python is easy to use and fast to iterate with, it was picked up early on by academics doing research in the ML/AI field,” said Mark Story, who is a principal developer at Sentry. “As a result, many libraries were created to build workflows in Python, including projects like TensorFlow and OpenAI.”

Although, Python has proven to be effective for a myriad of other areas, such as building websites and creating scripts for DevOps. Yet it is with AI/ML where the language has really shined.

“Analytics libraries such as NumPy, Pandas, SciPy, and several others have created an efficient way to build and test data models for use in analytics,” said Matt Ratliff, who is a Senior Data Science Mentor at NextUp Solutions. “In previous years, data scientists were confined to using proprietary platforms and C, and custom-building machine learning algorithms. But with Python libraries, data solutions can be built much faster and with more reliability. SciKit-Learn, for example, has built-in algorithms for classification, regression, clustering, and support for dimensionality reduction. Using Jupyter Notebooks, data scientists can add snippets of Python code to display calculations and visualizations, which can then be shared among colleagues and industry professionals.”

Granted, Python is certainly not perfect. No language is. 

“Due to its interpreted nature, Python does not have the most efficient runtime performance,” said Story. “A Python program will consume more memory than a similar program built in a compiled language like C++ would. Python is not well suited for mobile, or desktop application development as well.”

But despite all this, there are many more pros than cons–and Python is likely going to continue to grow. 

“Python is an excellent choice for most people to learn the basics of code, in the same way that everyone learns how to read and write,” said Tom Hatch, who is the CTO of SaltStack. “But the real beauty of Python is that it is also a language that can scale to large and complex software projects.”

UiPath’s $225M Round: What Does This Mean For RPA (Robotic Process Automation)?

This week UiPath announced a Series E round for $225 million at a $10.2 billion valuation. The lead on the deal was Alkeon and the other investors included Accel, Coatue, Dragoneer, IVP, Madrona Venture Group, Sequoia Capital, Tencent, Tiger Global, Wellington, and T. Rowe Price Associates.

UiPath is the leader in the RPA (Robotic Process Automation) space, with ARR (Annual Recurring Revenues) of more than $400 million. RPA technology allows for automation of tedious and repetitive corporate processes. The segment is also the fastest growing in the enterprise software market. 

“Their growth story is pretty simple, but often gets lost in Silicon Valley as we all strive for the shiny new object,” said Pankaj Chowdhry, who is the CEO of FortressIQ. “They provide a great product that helps their customers get more out of their existing legacy investments.”

The irony is that the core technology for RPA is based on something fairly simple: screen scraping. This makes it possible to replicate a user’s input patterns with applications.

Yet companies like UiPath have innovated RPA systems, such as with machine learning, computer vision, process mining, intelligent OCR (Optical Character Recognition) and NLP (Natural Language Processing). The result is that the bots are getting smarter. 

Note that the COVID-19 pandemic has also accelerated growth–at least for the larger players. “Companies today need to ensure they have the right structure and right processes in place to withstand whatever comes their way,” said Vijay Khanna, who is the Chief Corporate Development Officer at UiPath. “With that, they are increasingly turning to automation to accelerate their digital transformation efforts and ensure they can operate as productively and flexibly as possible moving forward.”

The RPA industry has been the subject of various criticisms. For example, the technology is often perceived as being mostly a Band Aid that cannot scale and requires too much training to create useful bots.

“RPA disruption needs to be consumerized to provide self-service for users,” said Muddu Sudhakar, who is the CEO and co-founder of Aisera.

The competitive environment is getting more intense as well. Keep in mind that Microsoft is investing heavily in its own RPA platform.  And there is buzz that Amazon and Google will make a play for the opportunity. 

“The funding for UiPath is consistent with our view of a technology at the proverbial inflection point,” said Adib Ghubril, who is the research director at Info-Tech Research Group. “IBM has actively sought to strengthen its position in this market since the beginning of the year. Indeed, many believed that Automation Anywhere or UiPath would be the target. The fact that IBM opted for a much smaller player–WDG Automation–suggests that the big three–Automation Anywhere, BluePrism, and UiPath–are liking their odds of going-it-alone, at least for now.”

The UiPath round also highlights that the RPA market is massive and still in the early stages. “The adoption of RPA keeps growing as enterprises realize the benefits automation brings to operational efficiency, cost reduction, and employee and customer experience–making RPA one of the highest priorities for tech investments,” said Barry Cooper, who is the Enterprise Group President at NICE. “With the existing economic reality causing some enterprises to cut down on their workforce and look for cost efficiencies, RPA helps to maintain business continuity by complementing and assisting the existing workforce to do more with less. Robotic software can either take over some of the manual tasks to free up employee’s time for other important areas, or assist the human workforce, side by side, with attended bots.”

But with the large RPA firms bolstering their balance sheets with large amounts of venture capital, does this mean that there are fewer opportunities for startups? Is there a crowding out effect?

Well, not necessarily.

“There is opportunity for small operators to provide unique and price competitive products to disrupt the digital world,” said Vadim Tabakman, who is the Director of Technical Evangelism at Nintex. “With the world focusing on how machine learning and artificial intelligence can help—there are interesting opportunities that small operators can investigate that larger ones will have a harder time to implement and modify their existing tools.”