Investing in Artificial Intelligence and Machine Learning is projected to reach $57.6 billion by 2021, according to the International Data Corporation. With Netflix saving one billion dollars every year thanks to its machine learning-powered recommendation system, it’s no wonder businesses are willing to spend massive amounts on such projects. If you’re one of them, finding trustworthy machine learning companies is probably frustrating as you probably don’t know which skills and expertise to look for. That’s why we joined forces with experts in the field to develop evaluation criteria against which we compared multiple firms. Scroll down to discover the most reputable companies, an explanation of our ranking process, and a hiring guide to help you choose wisely.
We compared many companies against our custom-developed evaluation criteria to curate a list of first-class outfits. They earned a position on our list based on their score in reputation, technical and business knowledge, quality, agility, and price. Note that our list also features some of the top deep learning companies, as this discipline is a subset of machine learning with extended capabilities.
Below, we provide a more detailed explanation of the steps we took to select the best firms.
Most of these agencies extend their offerings, at least in a few other AI subsets, on top of machine learning. With combined knowledge in these disciplines and other IT areas, they can solve various challenges under one roof, enabling faster turnarounds and seamless collaboration. We evaluate the capabilities that enhance machine learning as a service or the overall client experience, So, read on for more details on the critical services.
We included world-class AI companies with capabilities in more subset disciplines. Their expertise includes deep learning, NLP, expert system, robotics, machine vision, and speech recognition. Since deep learning is a sub-branch of machine learning, our list features some of the best deep learning companies as well.
We analyzed their knowledge in these fields for clients who would like to take full advantage of these technologies in one place.
While the application of machine learning shouldn’t rely on merely Big Data, leveraging large amounts of data is, undeniably, a critical skill. You’ll, therefore, find many companies primarily focused on AI, offering Big Data services as well. We compared the relevant names against our ranking criteria for the best Big Data companies to ensure they maintain high-quality standards for all of their additional offerings.
Some of the machine learning service providers on this list can help you leverage the benefits of IoT for your highest business advantage. They can map out the right IoT landscape based on your unique needs and preferences. We compared them against our custom-developed evaluation methodology for the best IoT companies to confirm their expertise.
Machine learning has brought significant alterations to the software engineering process. Software development companies should understand the differences and tailor the methods according to clients’ needs.
Machine learning has also changed the way developers create websites and web applications. Top machine learning companies apply their knowledge in this field to create a more personalized experience for users. We evaluated their expertise by employing our criteria for web development providers to ensure they are offering consistent quality across all of their service areas.
Mobile App Development
Given the potential of the technology, on-device machine learning may soon become the new standard in mobile application development. For this reason, the best machine learning companies are on a mission to build mobile apps that can hear, see, sense, and think. For now, we evaluate their mobile app development skills by employing our ranking methodology for the top app development companies.
Businesses are increasingly investing in Blockchain in an attempt to improve efficiency, security, traceability, transparency, and more. Innovation-driven organizations always keep their eyes peeled for the latest trends in the tech world, so getting all of their needs covered in one place is a huge plus. We therefore assessed the companies offering Blockchain services against our methodology.
We further evaluate machine learning companies by checking their past work and case studies which help us filter out the great from the just good. This step plays a crucial role in our ranking system as nothing speaks louder about the firms’ abilities than their applications in action.
What results have they achieved for their clients? Were they able to align their expertise with specific business objectives? Are they experienced with different projects? Most importantly, have they driven real ROI for clients? We assess all aspects of their machine learning-powered solutions to curate our unbiased list.
In today’s crowded market, reputation is hard-to-earn. We employ various qualitative and quantitative metrics to determine whether machine learning companies operate with integrity and transparency.
First of all, we assess user satisfaction. To gain a profound understanding, we source reviews and testimonials from multiple channels. From the company’s website to business directories, to social media platforms, we analyze plenty of feedback to find companies with high customer satisfaction and retention.
We contact past clients to learn more about their experience. What were the strongest points of each machine learning company? Were there any challenges they weren’t able to tackle? In which ways and to what extent have the developed solutions upgraded their business? Were they able to understand clients’ objectives in the first place? All this information affects the ranking of the companies.
Our evaluation methodology is designed to detect world-class companies that can handle even the most challenging projects. Those that can change the way we experience things through applied machine learning. To be able to bring such innovation in the world around us, top machine learning companies must house talent that possesses the following technical skills:
We demand expertise in the following areas:
Experts must know how to apply these concepts as well as to adapt them as projects demand.
Successful application of machine learning wouldn't be possible without mathematics and statistics knowledge, making it one of the critical areas we evaluate. Machine learning consulting companies need such expertise to choose the best machine learning algorithm in a given situation, manipulate with parameters and their settings, detect the right validation strategies, etc.
Understanding of the following concepts provides a solid foundation for machine learning:
The best machine learning companies can handle large amounts of data and translate them into predictive analytics. We evaluate their ability to decode, analyze, and interpret data. This lets them unlock hidden patterns and apply the insights into actionable business solutions.
With data modeling and evaluation critical for successful machine learning projects, we demand knowledge in the following concepts:
The right libraries, packages, and APIs are essential for standard implementations of algorithms. Successful application, however, requires an understanding of fundamental concepts such as the four main types of machine learning (supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning), their sub-categories and the advantages and disadvantages of each approach. Machine learning companies should also understand the influence of hyperparameters on learning.
We also assess their awareness of issues such as over lifting and underfitting, bias and variance, missing data, data leakage, and more factors that could affect the quality of the machine learning process.
Machine learning experts, in essence, are software engineers. Sometimes, the developed software is just a small part of a much bigger ecosystem of services and products. A machine learning engineer, therefore, needs to possess knowledge in the following areas of software programming and system design:
As Google Cloud Chief Decision Scientist Cassie Kozyrkov says, "Blind trust is a terrible thing. Force the algorithm to earn your trust by testing."
Our ranking criteria demand that organizations test everything. We check whether they verify the quality of their algorithms comprehensively.
To develop actionable systems, machine learning companies must think business above all. That’s why we don’t merely assess their technical skills, but the understanding of key business concepts as well. Since each client has unique challenges, we source companies that can align their technical expertise with different business goals.
Machine learning consultants are the ones who need to determine which route to take to elevate your performance. They should outline a plan with clear objectives and apply machine learning in business strategically. Our list is filled with top vendors with a keen sense of business, able to build best-suited applications that propel organizations.
A creative and curious mindset is imperative for machine learning and deep learning companies. They must be able to think outside the box, identifying the best route to complete a project. Through creative thinking, they should explore the machine learning models they can build, gather data to train those models, and choose the appropriate architecture. Finally, they need to select reliable methods to evaluate their work.
We sourced top agencies based on their ability to transform abstract problems into tangible solutions through tireless exploration of novel ways to tackle challenges.
Outdated systems can lead to adverse outcomes. For each machine learning or deep learning consulting company on our list and in general, up-to-date software is an absolute must. We ensure each agency operates with top-of-the-line machine learning technology and award with a higher rank those that offer proprietary software which is a testament to their skills.
We present ourselves as potential clients to learn more about the agencies’ project management and reporting practices. Since machine learning is a complex process, proper organization, and collaboration is essential.
Agencies should be equipped with diverse talent, from machine learning engineers to data scientists, to data analysts and other tech professionals. Iteration and improvisation could last forever. So, we check whether machine learning companies respect deadlines. After all, a machine learning expert should understand the viability of the project and decide when it’s time to stop. Top agencies know that the value of the project should never exceed the resources invested in its development.
We look for companies that breed machine learning innovation following agile methodologies. Dividing the project into bite-size tasks and scheduling frequent reports will keep everyone aware of the progress. Plus, you can request modifications while the product is still in development.
The ability to define clear metrics at the beginning of each project, it’s essential for measuring results accurately. While the nature of the process demands blind experiments, machine learning consulting companies must be well-versed in ROC, precision, conversion rates, etc. These metrics showcase the system quality and its success and failure parameters.
Developers can also inspect individual samples of data to gain valuable insights about the system’s performance. In a nutshell, the chosen metrics should show measurable results as clients are interested in maximizing their ROI.
It's hard to pin down an accurate price on machine learning as it much depends on the cost of data, research, and production required to complete your project successfully. These variables always depend on one's unique needs.
That said, we look for top machine learning companies that have a proven track record of driving back profits for their clients. We have carefully selected those with rates that fall within industry standards, being able to provide excellent value for the client’s investment.
Even with a list of the best-in-class practitioners, you could still use a few tips on how to select the right one for you. Our hiring guide will navigate you through the decision-making process, helping you recognize which company deserves your innovation budget.
While a machine learning outfit should figure out the best route to advance your business with its expertise, you should give some input as well. Map out your goals, challenges, and your target audience before reaching out to firms. Do you need someone with a narrow focus on machine learning, or your project could use a hybrid of machine and deep learning company? Create a list of all these things, including the desired outcome. Then, you can start interviewing different companies, presenting them with the same project.
Naturally, you might want to hire someone that understands the unique challenges of the environment you operate in and has assisted clients like you in the past. In this case, you need to go through companies’ portfolios looking for similar projects or ask them to provide relevant case studies.
Machine learning is a lengthy process, so opt for someone you’d be comfortable spending time with. Each machine learning company has something distinctive to offer outside of its specialty. It’s the team and its culture that can make or break a deal. Get to know as many people that will work on your project by scheduling online or in-person meetings with the agencies.
Machine learning today is still exclusive to businesses with higher development budgets. Yet, no one can give you an estimated price without knowing the specifics of your project. For this reason, it's best to present your idea to as many machine learning companies and ask for quotes. Then, you can compare these prices and set a more specific budget.
Due to the experimental nature of the project, you'll want experienced practitioners that can perform machine learning training and meet your goals within budget. While the investment could pay off big time, you should keep your finances in check. Don't opt for the lowest price but go with the most cost-effective offer instead.