If you are worried about the technology cost and returns on every investment, then the cloud computing solutions providers can offer you a complete solution. What is pushing enterprises adopt the cloud with a myriad of Artificial Intelligence solutions that offers a complete required solution for innovation? The big four cloud majors currently ruling the market- Amazon, Microsoft, Google, and IBM all providers offer the ability to construct and run neural networks. The cloud platforms offer other forms of AI solutions in the public cloud computing facilities combined with various tools and various prices for completely implementing it. There are also other AI tools that are available in the market provided by the other service providers such as SaaS champs, Salesforce and Oracle. Currently, if you see the pool of offers, you are bound to get confused with each type of offering based on the pricing. There is a lot of overlap in the services, and many of the providers offer initial free services based on the requirement.

Every enterprise differs in the requirement making them a unique challenge for evaluating each of them based on the offerings. When you start implementing the AI, you go through the process of evaluation for Machine Learning (ML) and to find how exactly the technology works; you have to spend some time understanding different factors. It’s always better to have a list of requirements that will assist you in understanding the offerings more productively, even understanding the basic machine learning algorithm can be a challenge. Machine learning lets the company find patterns in data, for most enterprises adopting the cloud-based AI technology to find patterns it encompasses wide goals that can be implemented. It can be used in detecting sentiment in a text document to projecting the next action to take with the certain customer based on the history of interactions.

If you want to take a deep dive in the Artificial Intelligence (AI) you have to determine the objective whether you are looking to build something completely new using the AI technology or will just implement the technology to improve the business operation. Those who want to build something completely new from scratch such as application with complete customization- include the steps such as preparing data, designing a neural network model that will derive the model. Such types of AI technology builders require a lot of experimentation with knowledge of data science and making the machine learning as the foundational element. Such process will have a completely different scenario where the spending on developing the AI technology will be different if you’re looking to build a complete AI then you will need an IT admin, business analyst, and data scientists.

Implementation of AI technology will different wherein the requirements are just adding certain AI capability to improve the operation process. Such implementations are done to reduce the manual process of marketing or sales or IT admins that want to deliver improved capabilities to customers or employee. A direct implementation will be less time sensitive while the building process is greatly time-dependent making the development need to both resources and technology.

How developers usually build AI technology?

Developers that build the neural networks train them and then unleash them in real time. This could be on the batches of transactional data or individual data or an e-commerce website data. Testing is usually done on the repository of data or partially on certain pages going through the live customer interaction on the website. A developer has to go through several steps before the implementation- preparing the test data, designing the model to test against the data, training it on data, testing of various data and implementation.

How cloud providers assist developers?

Cloud providers will assist in building a cloud account and choosing the storage option. During such stage, the AI developers will need various choices how much will be the total data stored and how to analyze the total data part. For example, Google offers two choices in terms of Dataproc and Dataflow. Dataproc is optimized for using the Hadoop file system with an analysis package that is meant to handle it such as Spark Machine Learning. Each has a different plan based on the requirement of per-gigabyte of requirement. Dataflow is meant to ingest either batch or stream of data via the Apache Beam. The point of implementing AI technology and taking the data on a public cloud platform is a major buying decision. Buying a strong option can be complicated; it might be better to access the free account from a vendor and monitor the economics based on the needs.

Pricing and Training

The cloud and combined AI technology pricing can be a tricky challenge for the enterprises that are looking for the sophistication of building a complete system. Technology is broken in separate training and inference pricing. The hours utilized in training are then multiplied by various forms of units of capacity and to reflect the computing power you are using the depending on the kind of compute instance selection. IBM costs vary depending on the Watson Machine Learning as a combined training and inference cost; somewhat reflecting the training may be done offline, behind the firewall. For some of the API choices such as we can reflect on the video search, text to speech or image categorization you will need the allotment of pennies or dollars per minute of video.


The online calculators provided by the cloud vendors can assist while buying, but the best bet for enterprises is to try free versions of each cloud providers and application. In the free versions, you can get a complete feel about the time required to develop an Artificial Intelligence platform or to customize the required machine learning modes along with the time required for training the machine learning. The cloud system will keep running on the basis and requirements that if you keep asking the system for predictions.

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