At its most basic description, Artificial Intelligence (AI) is the replication of human intelligence processes by machines. AI uses vast amounts of data and statistics to identify patterns, the likelihood of future events and to automate tasks typically done by humans.
AI can come in many forms depending on what task it is required to perform. Below is an example of some different types of AI solutions:
As AI continues to be introduced across organizations in a variety of industries, there are key areas that are advancing rapidly. One of these is the application of computer vision. For example, software that tracks, perceives and interprets images enables cars to drive safely and navigate through traffic, identifying other cars, people and other objects.
Another area advancing rapidly is deep learning, which is being driven by the availability of greater GPU power and large data-sets. This form of advanced AI can help companies gain deeper insights than would be possible by human thinking alone. In addition, as companies begin to adopt AI, they’re looking for ways to deploy it. AI engineering is facilitating the performance, scalability, interpretability and reliability of AI, while also addressing its maintenance, scalability and governance.
In very general terms, AI algorithm development can be broken down into four key phases:
AI developers integrate AI functionality via algorithms and logic into software applications. They are programmers well versed in Java, Python, and R. They train and program systems to solve problems the way a human would.
Often confused with data scientists, AI developers create the solution based on the machine learning models developed by data scientists, who are tech professionals, responsible for collecting, analyzing and interpreting extremely large amounts of data. A data scientist has backgrounds in math, computer science and statistics.
Additionally, both AI developers and data scientists today need to be well-trained in soft skills since AI needs to be designed to act upon things as humans would. This includes the ability to be empathetic, to have good communication and collaboration skills and be fair and open minded.
The ultimate goal of an AI solution is to augment the intelligence and activities of a human, working independently from humans as much as possible. While AI can be used to predict outcomes, interact with customers or help automate tedious tasks such as document processing, humans always will be in the loop to address issues that the AI solution has not been trained to address.
AI is reshaping businesses, yet some AI projects fail to meet expectations around social responsibility, ethics, transparency and fairness. When AI algorithms introduce bias, preventable errors and poor decision-making, it can cause mistrust among the very people it is supposed to be helping. Designing responsible AI applications requires focusing first on the human experience, and then aligning what is technically feasible and what is viable for the business.
Predictive AI analyzes historical data to identify patterns that can help predict future outcomes, but the next challenge is what to do with those predictions. That’s where prescriptive AI comes in. Based on the results of predictive analytics, prescriptive analytics aims to understand what variables can be manipulated to achieve the desired outcome and how. It requires data scientists to have a deep understanding of variables' causes and effects so they can be fine-tuned to get the results that organizations want.
ModelOps is focused primarily on the governance and life cycle management of a wide range of AI models. According to research firm, Gartner, “ It automates the development, validation, scoring, deployment, governance and maintenance of AI solutions. ModelOps helps companies shorten production cycles and deliver results to end-users quickly at scale, while also continuously improving the results.”
ModelOps shares many similarities with DevOps, a set of practices which integrate software development and IT operations to help shorten software development lifecycles and enable continuous updates. Both practices aim to remove the siloes between software engineers or data scientists and IT to make it easier to get projects running and keep them working smoothly.
Data science projects are packed with uncertainty, and almost always involve failed experiments. Business leaders grapple with this reality because they are used to making budget decisions based on a return on investment (ROI) analysis. To get the best results out of AI projects, teams follow an Agile data science process that blends techniques from the scientific method, design thinking methodologies and agile software development framework.
An Innovation Sprint is a short-term project that allows companies to experiment with AI. It’s used to develop a proof-of-concept and determine if AI is right for your organization. To start an AI Innovation sprint you need three things: A specific problem to address, a data science team and lots of relevant data.
An AI Innovation Sprint usually takes up to four weeks, yet one sprint is usually not enough to get a good solution to operationalize. The first AI sprint will answer your question, is AI valuable to me? If the answer is no, then you might get enough insights to reframe your problem or collect better data. It usually takes a couple of additional sprints to refine both the dataset and the AI model to get good results. It’s important to keep in mind that it is an iterative process that takes not only knowledge but also creativity and passion to get right.
Nearshore AI is a form of outsourcing, yet closer to home – within your own country or one very close by. It enables you to jumpstart your AI-based digital transformation initiatives, utilizing the expertise, capabilities and experience of highly experienced data scientists, data specialists and service design teams.
Nearshore AI development enables greater collaboration, responsiveness, and convenience. When you work with a nearshore provider, as opposed to other outsourcing models, projects can be completed and deployed faster, you can be assured that the solution was designed to fit your exact needs, it meets your quality standards and you save costs.
Building an AI algorithm is a highly specialized and complex process that requires the expertise of a skilled data scientist. Most companies do not have that skill set in house, since it’s not a role that is needed on a daily basis. In addition, there continues to be a shortage of AI talent, so even if they wanted to hire internally, they are hard-pressed to do so. Companies find that they’re able to secure the talent they need to initiate an AI project more effectively through an outsourced partner.
In addition, building an AI model requires large amounts of data. Training models requires manually identifying, classifying and tagging the data. Few mid-sized companies have the manpower and time to dedicate to the arduous task. Nearshore providers should have a private crowd, or data specialists, who have been rigorously trained to efficiently tag data - even millions of images. These teams also work in secure environments and they are under NDA to ensure confidentiality of data.
selecting a location, it’s important to understand different labor laws and other federal regulations. For example, some countries have labor laws that limit work to 40 hours per week and anything over that is considered overtime. Additionally, Intellectual Property (IP) interests can be substantially different between countries. And, when it comes to highly regulated industries, such as government, healthcare or banking, data privacy laws may prevent the sharing of some information across country borders.
Another thing to consider is the education, certifications and expertise of the team you will be working with. Inquire about this when meeting the team and research the university rankings, engineering programs and graduation rates of the region. A good outsourcing partner will have access to highly relevant and qualified professionals with the technical skills required to develop next-generation AI solutions.
The region you are considering also should have a vibrant entrepreneurial ecosystem, with incentives that reward entrepreneurial pursuits. Look for regions that promote accelerator or incubator programs, offer tax incentives for start-ups, hackathons and tech associations. These types of locations are often home to innovative and inspired tech firms, and graduating students and rising professionals who remain in the region.
An AI development team should include a project manager, data scientists, data specialists, software engineers and also design experience, or UX professionals.
An integral part of any good software development team is the design experience members who will ensure that what is being built is actually meeting end-users’ real needs. This design approach should inform every single software development project to not only develop the right solution, but also to save costs, reduce risk and boost a company’s reputation.
There are many ways to outsource your software development projects and there are strategic differences between each.
Offshore is defined as outsourcing to providers in another part of the world. These offshorers have large talent pools, the lowest rates globally and a mature market with many outsourcing providers. However, communication and quality standards can be difficult to manage, travel times are extensive for U.S. clients, and the physical distance can stunt team collaboration, despite the benefit of 24/7 workflows.
While it doesn’t have to be in the same country, nearshore always refers to companies with physical proximity and similar time zones. Time zone alignment and regional proximity are the most common characteristics of nearshore, and there are several benefits they enable. The time zone similarities enable efficient communication and collaboration since everyone is fully aligned. When everyone is working simultaneously, nearshore software development teams become an extension of a business, ensuring that high-priority issues are resolved quickly, and developers are available at the drop of a hat.
There are five key benefits to working with a nearshore for your tech project. This includes the fact that it is typically lower costs than having a full-time employee, you receive specialized expertise, you only pay for what you need, your timeline is accelerated and collaboration is much easier with a partner closer to home.
When you outsource projects or even specific parts of a project, you eliminate the risk and overhead associated with the costs and permanence of having a full-time employee (FTE). You only pay for what you need, when you need it.
Today, hiring great tech talent is difficult, with a scarce pool of candidates, so you can be assured a ready-made team of professionals with the experience needed for your project when you outsource. Additionally, it reduces the risk of hiring full-time employees when economic changes are causing companies to maintain a leaner workforce to reduce overhead.
When selecting a location, it’s important to understand different labor laws and other federal regulations. For example, some countries have labor laws that limit work to 40 hours per week and anything over that is considered overtime. Additionally, Intellectual Property (IP) interests can be substantially different between countries. And, when it comes to highly regulated industries, such as government, healthcare or banking, data privacy laws may prevent the sharing of some information across country borders.
Another thing to consider is the education, certifications and expertise of the team you will be working with.
You should do your research before hiring an outsourcing partner; determine which model will be best for your company, such as onshore, nearshore or offshore; take a tour of the facilities and ask to meet specifically with the team that will work on your project. A good outsourcing partner will have access to highly relevant and qualified professionals with the technical skills required to develop next-generation solutions.
Time zone alignment and regional proximity are the most common characteristics of onshoring, and there are several benefits they enable. The time zone similarities enable efficient communication and collaboration since everyone is fully aligned. When everyone is working simultaneously, nearshore software development teams become an extension of a business, ensuring that high-priority issues are resolved quickly, and developers are available at the drop of a hat. Additionally, when you work with an onshore partner, you have the same regulatory requirements, and many regulated industries are required to keep data within the country. This can be a problem when you work with an offshore firm.
A client should always own its proprietary data regardless of the outsourcing model, and your nearshore provider should work diligently to ensure the protection of that data. For many regulated industries, data transfer can only occur within the country, so a nearshore provider located in the same country as you, often is the best bet.
A machine learning model is a file that has been trained with large data-sets to recognize certain types of patterns and continuously learn from the data. Machine learning can be used to automate tedious tasks, such as document processing or identifying defects in manufacturing assembly lines. It also can be used to predict future outcomes, or to answer basic customer questions.
There are three key elements that determine the potential of a machine learning model. First and foremost is the data. Second, is the need to review the model’s assumptions, since most models can only excel under certain circumstances. Finally, it involves studying the results of the training and testing metrics, searching for answers beyond accuracy numbers.
Companies of all sizes are undertaking AI projects, such as machine learning, in order to be competitive, augment human staff and drive business growth. Companies simply don’t often have the inhouse resources or talent to work on such complex projects.
Machine learning is simply a form of Artificial Intelligence. A machine learning model is a file that has been trained with large data-sets to recognize certain types of patterns and continuously learn from the data. Machine learning can be used to automate tedious tasks, such as document processing or identifying defects in manufacturing assembly lines. It also can be used to predict future outcomes, or to answer basic customer questions.
Building an AI algorithm is a highly specialized and complex process that requires the expertise of a skilled data scientist. Most companies do not have that skill set in house, since it’s not a role that is needed on a daily basis. In addition, there continues to be a shortage of AI talent, so even if they wanted to hire internally, they are hard-pressed to do so. Companies find that they’re able to secure the talent they need to initiate an machine learning project more effectively through an outsourced partner.
Outsourcing machine learning projects can help you accelerate your digital transformation initiatives, and help you address the critical tech talent shortage, yet it’s vital that you become a true partner with your outsourcing provider. It’s not the kind of project that can be sent to your provider and then ignored until it’s ready to be deployed. In order to be effective it requires your involvement to truly help your provider understand the business challenges and needs and adapt the solution to meet those needs.
It’s important to do your homework and consider the location. Since it’s a partnership, it’s important that you are in proximity to operate in similar time zones, with the same regulatory and data regulations and cultures. Also, inquire about projects that the outsourcing provider has completed that may be similar to your own, and meet the team that will be working on your project.
Onshoring in the U.S. often means you are operating under the same regulations, in similar time zones and ways of doing business. If you are in a highly regulated industry, you also may be required to keep key data within the confines of your own country. For such a collaborative endeavor as developing a machine learning solution, it’s important that you are close to your provider.
The key risk is investing in a provider is that will not be able to meet your business needs with a highly accurate solution, after significant investment in time and money. Risk can be avoided by working with a provider that begins each project with an innovation sprint, that can very quickly determine the viability of the solution before too much time and money is invested.
Since data is really the lifeblood to effective algorithms, massive amounts of data train the algorithm. Since data comes from many different places and in many different formats, you need to get the raw data into shape. It needs to be cleaned, with errors fixed and duplicate information deleted and then labeled with its proper identification. Much of data labeling is a manual and laborious process and is usually done by a group of people called data specialists or digitizers.
An AI algorithm cannot be effectively trained without having structured data. Data labeling is key to structured data, which trains an algorithm how to behave.
Data labeling can be a laborious time-consuming process, which helps an algorithm understand text or images in various contexts. An outsource provider often has the data labeling, or digitizer teams required to sort through huge datasets.
When selecting an outsourcing firm to handle your data labeling needs, it’s beneficial if the same company can provide both the data labeling and cleansing function, as well as the AI development. An integrated team ensures that product timelines are accelerated and algorithms are properly trained to ensure an extremely high level of AI accuracy.
An in-house data labeling team is one which is done by your own staff, in your own facilities. Most companies don’t have the resources or staff to accomplish this, and unless they are developing many AI solutions on a regular basis, it’s not a cost-effective option.
Before any AI project can be initiated, huge amounts of data need to be compiled, cleaned and classified, or annotated. All types of data in the form of text and images must become structured in order for it to be utilized in AI development.
Synthetic data is precisely that - it is data that is artificially created and based on possible scenarios. As an example of the effective use of synthetic data. A healthcare insurer needed to calculate how frequently customers with kidney disease file claims and for what reasons. After lacking sufficient internal data, they needed to integrate synthetic data to better support the algorithm. The synthetic data was created from made-up, but possible scenarios related to ailments of those with chronic kidney disease. The added data was also created by using the initial dataset as a guide.
Even with the enormous amount of data that companies are accumulating, there never seems to be enough of the right data to train algorithms to perform specific tasks. Data can be found in internal databases, systems and files, or externally with suppliers and partners. It’s important to take a data audit to see what data you have and whether or not you need to augment it with synthetic data.
Once you have collected your data, it needs to be cleansed, validated and prepared to ensure that it is in good shape and ready for analysis. Then, a good way to know if the data is accurate is to test it, and find out if there is a problem before you are too far along in the process. You should divide your data into two parts and set one aside for testing and the other for feeding the algorithm.