AI and Machine Learning as a service from IT Svit

There are various scenarios when a business can benefit from Artificial Intelligence and Machine Learning models and applications. These Artificial Intelligence algorithms can be applied to improve your customer-facing products or services or to optimize the internal workings of your infrastructure and processes. IT Svit has wide experience with building, training and maintaining ML & AI applications for various business benefits.

Configuring AI tools from Google Cloud and Amazon Web Services

All of the leading cloud service providers, namely MS Azure, Google Cloud Platform and Amazon Web Services, provide various types of services and products like Amazon Sagemaker and Google Cloud AI platform or Azure Machine Learning Studio. IT Svit has wide experience with designing and configuring Machine Learning algorithms on these platforms to help businesses reach their project objectives.

Building bespoke infrastructure for Machine Learning models

In order to work efficiently and provide value for your business, your Machine Learning models must be deployed to appropriate cloud infrastructure. IT Svit has 5+ years of expertise with delivering DevOps services and solutions, so we are able to design and deliver resilient, scalable and cost-efficient infrastructures to run your ML & AI algorithms.

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If you are striving to succeed in the modern business world, you must be willing to use all your investments to the fullest capacity and provide exceedingly superior service to your customers. Machine Learning models and Artificial Intelligence algorithms are great tools that can serve this purpose, as they can both augment your customer-facing systems and optimize the performance of your mission-critical infrastructure. Gaining access to them is not so hard — making them work efficiently is the real challenge!

Every cloud service provider, be it AWS, Google Cloud, MS Azure, DO, IBM or Oracle provides their platform-specific Machine Learning solutions and Artificial Intelligence services. Amazon Sagemaker or Google Cloud Machine Learning tools are easily accessible and can be deployed to fulfill various roles within your products. The question here is how to deploy them correctly, as there is a huge variety of factors that affect the efficiency of Machine Learning and can affect it direly.

1. Big Data testing. Every Artificial Intelligence algorithm has to be trained on huge data sets in order to be able to perform its task efficiently. These historical data sets can be obtained by data mining from publicly available sources, like image bases of Shutterstock images, or they have to be privately stored within your company — but you need to have access to large volumes of data of varying types, sizes and quantities.
Unfortunately, there is no telling from the beginning if some types of data will be useful or not, and “analyze everything” approach can be quite too costly, so before even storing the data to train the model, it must be prepared in some way. The following initial data validation must be performed:

  • the data must be deduplicated to remove the redundant copies,
  • it must be time-filtered to get uniquely timestamped latest, most relevant input,
  • the white noise must be removed to minimize the volume of computing resources needed later on,
  • the data must be normalized, so various data types are represented in a single format, mostly JSON
  • it must be checked for completeness and consistency, so the data is not partial or broken, etc.

2. Map-reduce. Once the data is validated, the map-reduce process begins. All the available data is mapped — split into chunks for data processing by Hadoop nodes, then it is reduced — the ready result is provided. The Hadoop cluster configuration is theoretically not too complicated (though the manual configuration takes around 10 pages and 4 hours to complete). However, configuring Hadoop nodes’ business logic correctly from the first time is something fantastic and the errors can be quite costly with this.

3. Data output validation. A data scientist must ensure the transformation rules were applied correctly and the output data is relevant to the business objectives and tasks. He or she must also validate the output to check that the final data visualization is correct and correlates with the content of the Hadoop distributed file system.

Above is the simplified description of the Machine Learning and AI algorithm work process. However, each of these stages requires selecting the best among multiple viable approaches.

For example, a natural language processing tool will need quite a different approach as compared to computer vision or an Optical Character Recognition model. They serve different purposes, process different types of input data, require different types of cloud infrastructure, etc.

This applies to the case of depicting the type of task the AI models will perform in your project. Another level of complexity comes with the decision among several suitable models. For example, one of the most popular types of Machine Learning models is Neural Networks, and their specific kind, which is Deep Neural Networks.

Neural Networks are built of nodes, which are structured into various layers, with connections between these nodes. The leftmost node is the input one, the rightmost node is the output one. The nodes between them are organized in layers, and if there are more than 10 layers, this Machine Learning process is called deep learning. By adjusting the values or so-called “weights” of the connections between nodes, the data scientist can influence the output, emphasizing the most important parameters.

Deep Learning types and use cases

The Deep Neural Network model training can follow one of three routes: it can be supervised, unsupervised or reinforced. Unsupervised learning is an approach, when the software engineers don’t specify the correct input and the desired output, so the model has to find the most important parameters on its own. It takes lots of time, but this is the only possible approach when you need to find hidden patterns or uncover previously unseen prospects of revenue.

Supervised learning, quite contrary, is the process when the data scientists provide some marked training data and specify the expected results. This approach is the best for training Machine Learning models based on historical system performance data to enable predictive analytics. Thus said, the model is able to quickly determine normal operational patterns and track them. Once this is done, such models can quickly detect abnormal operational patterns and alert of them, while also enacting some preconfigured response scenarios.

This helps automatically scale up and down based on workload, mitigate the impact of system module failures and reboot faulty modules or respond to DDoS attacks faster. Thus said, such Artificial Intelligence algorithms enable self-healing cloud infrastructure operations at scale, which is cost-efficient and resilient and requires only a handful of specialists, instead of employing huge support centers to keep abreast of your system performance.

Reinforced learning is a subdivision of supervised learning, where correct ML model decisions are awarded with points or some other way of gratification, so the model learns faster. This is especially useful for natural language processing or computer vision applications, as it allows to train an AI algorithm to a production-ready stage in 20-30 iterations (also called “epochs”), instead of standard 100+.

Thus said, selecting the best Deep Learning approach for your project, as well as selecting the appropriate ML model, data mining and forming training data sets requires certain degree of Big Data expertise and profound knowledge of data science operations, as well as cloud infrastructure design and management best practices.

IT Svit has gained this expertise over 5+ years of providing AI and ML solutions as a service. We have an in-depth understanding of the best ways to design, deploy and operate Machine Learning algorithms, and help make it easy for your business to utilize these AI modules cost-efficiently and at maximum performance.

Cloud-specific AI and ML services from IT Svit

Every big cloud service provider like Amazon Web Services or Google Cloud Platform has Big Data, ML and AI solutions in their product line. They are powerful, complex and can be configured precisely to meet your project needs and utilize cloud platform benefits like scalability, security and high-availability to the max.

The only downside with this approach is that these solutions have quite a steep learning curve, so it takes quite some time to configure them precisely to meet your needs, and time is of the essence in the modern fast-paced world. This is why businesses and organizations prefer to hire experienced professionals to implement their projects, instead of mastering Amazon machine learning services solely on their own. However, the job market demand is much higher than the supply, meaning highly-skilled data scientists and ML/AI engineers are rarely unemployed. This leaves a company that wants to use cloud-specific AI and ML services with three choices:

  • hiring freelancers remotely or searching for an in-house talent to do the job
  • subscribing to technical support from AWS or Google
  • working with a Managed Services provider like IT Svit

There are many highly-qualified professionals among freelancers, but finding an unemployed Big Data superhero is very unlikely, and many companies don’t want to settle for mediocre specialists. That is why finding the best fit might take a long time — and time is very valuable, as we have said above. The reason for that is that highly-skilled Big Data engineers are employed either with the industry-leading enterprises, with cloud computing providers or with IT outsourcing companies like IT Svit.

This is why quite a lot of businesses delegate their Big Data projects to the cloud platforms. Amazon Sagemaker and Google BigQuery are fully-managed services, so all you need to do is upload the code or specify the task requirements, and certified AWS or Google support specialists will deliver the results. The downside of this approach is that AWS support specialists build systems using AWS web services and modules, not free-to-use open-source alternatives. This might result in a vendor lock-in, or you risk overpaying for ML&AI services.

Another shortcoming of working with AWS and GCP support specialists under SLA is that the TTR is much more likely to be around 4 hours, not 15 minutes, due to the sheer numbers of tickets they must resolve.

For the reasons listed above, multiple companies prefer to hire Managed Services Providers like IT Svit for implementing their Big Data, ML and AI projects. We house a team of skilled and experienced data scientists with a wide variety of successfully accomplished projects, large codebase of ready solutions for typical challenges and an in-depth understanding of both Big Data workflows and cloud infrastructure required to support them.

IT Svit provides Big Data services for 5+ years and we can implement computer vision, optical character recognition, data mining for search engines and natural language processing, predictive and prescriptive analytics, etc. Most importantly, we use cloud platform-specific modules only where they are absolutely necessary and replace system components with open-source analogs. Such systems are easy to use, modular and can be adjusted based on your project needs.

We would be glad to help you implement Machine Learning and Artificial Intelligence in your workflows and products, to deliver value to your business and customers. Would you like our assistance — contact us right away!

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