Cazton offers consulting, recruiting and training services for Machine Learning, Deep Learning, Data Science, Artificial Intelligence and much more. Our team is composed of experienced Machine Learning Engineers, Data Scientists, Research Scientists, R&D Engineers, Distributed Systems Engineer, Business Intelligence Developers, Computer Vision Engineers, AI Experts who can provide you services at a cost effective rate. Contact us now to learn more about our artificial intelligence services.
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.
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.
Cazton team has solved all the major challenges and can help you create a fully customizable model that is secure, keeps data private, platform agnostic, integrates with existing systems, allows frequent granular model updates and trains even on extremely small data.
PyTorch is one of the most popular deep learning frameworks and is based on the library Torch. Computer vision and natural language processing are two major use cases. When it was launched, it was easier to use with Graphics Processing Units (GPUs) and that explains why researchers preferred it over competitive offerings like TensorFlow.
Spark is an open-source, lightning fast, cluster computing framework that provides a fast and powerful engine for large-scale data (Big Data) processing. It runs programs up to 100x faster in-memory and 10x faster on disk when compared to Hadoop’s MapReduce system. The reason for Spark’s success is its ability to process data in-memory (using RAM) that allows faster retrieval of data as compared to querying and searching on disk.
Imagine being able to build almost any digital asset just by providing prompts in natural language. Language models (LMs) like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on such tasks. Recent studies suggest that scaling up the size of the model is crucial for solving complex natural language problems.
Over the years, Spark has seen great acceptance in the technology industry. When it comes to large scale data processing or Big Data analytics, Spark has gained a lot of attention due to its lightning fast processing speed, batch and stream data processing, support for a variety of data sources and easy to integrate with applications written in C#, Java, Scala, Python and R.
ChatGPT is the fastest product in history to have acquired more than a million customers in just five days. Generative AI or GANs (Generative Adversarial Network) is a type of artificial intelligence that generates new content, such as text, images, or music, based on a set of input data.
With every passing second, the amount of data shared and transferred between humans is unimaginable. To manage, analyze, make predictions and decisions using that data is a daunting task. With data being a critical asset, companies today strive to understand the latest market trends, customer preferences and other requirements, thus making understanding large amount of data imperative.
Imagine a process which converts unstructured, unreadable pieces of information into something that is extremely valuable for your organization? information that gives you insights about your business, your products, customers and their preferences. Now imagine getting those insights in real time! We are talking about a process that gives you instant information about an active transaction. Such information is always valuable, isn't it
Database technologies have undergone several generations of evolution, right from flat-file systems to relational databases to schemaless databases. Some people might say that traditional relational databases are a thing of the past, but that is not true for all the scenarios. Changing requirements and evolution of the internet has meant that new types of databases have emerged, but most have specific use cases, which makes it difficult to decide on which database should be used when. At the same time, different types of data models have emerged throughout the history of databases but only Relational and NoSQL models have prevailed.
Did you know relational databases can scale up, but have a hard time scaling out? NoSQL databases, on the other hand, are meant to scale out with commodity-grade hardware. Many organizations prefer using NoSQL over SQL databases as it offers a great set of features. There are different types of NoSQL databases available including Key-Value Store, Document Databases, Column-Family Databases, Graph Databases and Full-Text Search Engine Databases.
Did you know there are more than 1500 companies using Cassandra to handle huge volumes of data? Did you know that some of the largest production deployments include Apple's, with over 75,000 nodes storing over 10 PB of data, Netflix (2,500 nodes, 420 TB, over 1 trillion requests per day), Chinese search engine Easou (270 nodes, 300 TB, over 800 million requests per day), and eBay (over 100 nodes, 250 TB)?
Do you have a good caching strategy for your applications? Have you felt the pain of sticky sessions? Have you had a caching strategy that didn't work for you? Do you need a caching strategy that scales seamlessly with the least amount of effort? If the answer to any of these questions is a "yes," the good news is that you are in the right place.
Do you face problems while scaling data in memory? Are you facing slow processing times? Do you want scalability and as well as atomic transactions? Do your machine learning models require a lot of time for training and production?
Search is one of the most important tools in any web application. Having a robust and fool-proof search system can boost your business growth in many ways. One such technology that empowers search is called Elasticsearch. Unlike traditional RDBMS, Elasticsearch is designed to allow full-text searching. It allows you to create your own search-engine that is fast, powerful and scalable. In addition to web searches, Elasticsearch is also used for log analysis and big data analytics.
Search is one of the most important aspects in any application. Could you imagine Amazon, one of the world's largest e-commerce websites, without a search functionality? It would be nearly impossible to find products if we had to look for them manually. A robust search in this case is the key towards best user experience, increased customer traffic, growth in sales and generation of customer data, which is invaluable for such a big organization.
Have you worked with multi-billion dollar consulting and recruiting companies? If yes, we are sure we can provide more quality services at a much more affordable rates. We have been fortunate to work directly with Microsoft product teams for many years. Our team includes Microsoft awarded Most Valuable Professionals, Azure Insiders, Docker Insiders, ASP.NET Insiders, Web API Advisors, Cosmos DB Insiders as well as experts in other Microsoft and notable open source technologies.