Our CEO, Chander Dhall, became fascinated with machine learning over a decade ago. Having a masters in computer science, he has always kept up with academia even though the company primarily works on projects for mid and large size Fortune 500 corporations. Having been awarded by both Microsoft (Microsoft Most Valuable Professional for close to a decade) and Google (Google Developer Expert), he has been fortunate to interact and share knowledge with the ones who create these technologies.
Since Cazton is a big supporter of open source, we have experts who have contributed to open source machine learning libraries as well as data engineering libraries. Our team consists of experts who have PhDs as well as Masters’ degrees in data science and machine learning, are open source contributors with their own ML libraries, and have years of experience in the industry. That’s one reason our team has been working on serious machine learning projects long before our competitors.
Machine learning projects require serious understanding of data. Cazton data scientists with years of successful experience in the industry have worked in multiple business domains. Our machine learning experts not only evaluate the data, but are adept at all facets of data engineering. If you are serious about doing machine learning properly, please check out our expert machine learning team of PhDs, as well as Microsoft-awarded Most Valuable Professionals and Google Developer Experts.
Imagine Amazon's Alexa tool, do you like it? Even at the time of writing the article, it doesn’t understand context. It’s not conversational. It takes most commands from the user in a mutually exclusive fashion. For the most part, it’s not very good at correlating the commands subsequently given to it. So what’s the point? Machine learning is not that straightforward. In order to understand it, we need to understand TensorFlow, which is a deep learning library created and open-sourced by Google. We also need to understand machine learning and most importantly, deep learning which is a subset of ML. If you are new to Machine Learning please watch this 3-min video demonstration.
With the current popularity of ML/AI, having a serious data science practice has been one of the top prerogatives for most corporations. However, there is a shortage of data scientists currently. To be more accurate, there is a shortage of good data scientists. Many people have tried to take advantage of the latest hype and game the system. Why is data science so complex? In order to be successful at data science and machine learning, there are usually major steps we need to follow, at least, at a very high level:
Once all of this is done and the model is trained, we can use it to solve real world problems in the domain of our choice. Our team works in different industries like airlines, finance, insurance, engineering, healthcare, tech and manufacturing just to name a few. Understanding the domain helps identify the right approach in using machine learning to solve complex problems. We are fortunate to have the priceless experience of knowing what works and what doesn’t work given the breadth of domains we work in.
This helps us bring best practices and unique perspective to future projects. We use various machine learning and data engineering technologies to make the projects successful. Some of these technologies are TensorFlow, Keras, Scikit-learn, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, Kafka, Hadoop, Spark, Ignite and many others. The great news is that our team works on all major cloud platforms including Microsoft, Google and AWS and has experience with VMware and Pivotal so we can work with the existing tech stack of the client. Machine learning can be used to solve a wide variety of problems including:
We can go on and on with examples of where to use machine learning as the practical applications are truly endless. The above process (Steps 1-8) can be very complex. The individual problems may not be that hard to solve, but if the team isn’t experienced or there is a lack of people with good aptitude, the process could be a never ending and the likelihood of success very low. We do need to remember one thing in our competitive world, success doesn’t simply mean delivery. Imagine doing a project in five years after spending $100 million on it. What if the same project could be done in one year for just $5 million? If delivery is the only criterion of success, both projects would be deemed successful. However, we clearly know that the former is more of a failure than success.
With data science and AI, it’s essential to have the right team and understand how to manage them. Some of the work our team has to do is change the mindset of using archaic processes for data science and ML. Expertise, experience and our company’s history of success is crucial in making a project successful. Delay in projects not only reduces the competitive edge of companies, but can also result in massive layoffs. We, at Cazton, work with you ensure you are successful both as an individual by rising higher in your career and as a company by staying innovative and ahead of the competition.
Cazton is composed of technical professionals with expertise gained all over the world and in all fields of the tech industry and we put this expertise to work for you. We serve all industries, including banking, finance, legal services, life sciences & healthcare, technology, media, and the public sector. Check out some of our services:
Cazton has expanded into a global company, servicing clients not only across the United States, but in Oslo, Norway; Stockholm, Sweden; London, England; Berlin, Germany; Frankfurt, Germany; Paris, France; Amsterdam, Netherlands; Brussels, Belgium; Rome, Italy; Quebec City, Toronto Vancouver, Montreal, Ottawa, Calgary, Edmonton, Victoria, and Winnipeg as well. In the United States, we provide our consulting and training services across various cities like Austin, Dallas, Houston, New York, New Jersey, Irvine, Los Angeles, Denver, Boulder, Charlotte, Atlanta, Orlando, Miami, San Antonio, San Diego, Stamford and others. Contact us today to learn more about what our experts can do for you.