Imagine that instead of constantly working on mundane tasks that take up a lot of time, with a click of a mouse, you can get accurate results that allow you to move on to more productive things. Using artificial intelligence (Ai) and machine learning, you can deliver desired impact for your business and stakeholders in a fraction of the time.
Reducing the need to spend hours on mundane tasks is just one example of the power of Ai and machine learning. So, how do you get started with this technology?
Many companies do not have the data science expertise, infrastructure, budget, or the appetite for risk when trying to integrate Ai into business functions. You could be excited to get started, establish a data science team who spends eight months or more working on a machine learning model, and then fail to deliver real value if the model doesn’t produce the expected results and meet business objectives.
A recent Rackspace Technology survey suggests that organizations are struggling to fully realize the capabilities of Ai and machine learning.
“More than one-third (32%) of respondents report Artificial Intelligence R&D initiatives that have been tested and abandoned or failed. The leading causes for these failures included poorly conceived strategy (43%), lack of data quality (36%), lack of production-ready data (36%), and lack of expertise within the organization (34%).”
Many organizations do not know if the best product to implement Ai and machine learning is to build an internal team or outsource to a trusted partner. Building machine learning yourself carries a high risk of implementation failure, such that the same study also indicates “organizations (66%) prefer working with an experienced provider to navigate the complexities of AI and Machine Learning development.”
At ElectrifAi, we are making it even easier to partner with an experienced provider through what’s called Machine Learning as a Service (MLaaS). This service accelerates your machine learning business outcomes. It connects to your cloud or on-premises workloads and no experience with machine learning is required.
MLaaS can reduce the time to value of machine learning models much faster than using a machine learning platform as a service (PaaS), especially as it overcomes some key challenges organizations face when embarking on their machine learning journey.
For example, selecting which platform best suits your business needs and fits your technology ecosystem can be daunting with too many options available. Also, machine learning platforms in general assume there is an experienced data science team in place. Due to organizational silos, functions might keep selecting their own platform and toolset to satisfy their specific business needs.
Finally, choosing to use a platform may prevent you from realizing the benefits of machine learning until the platform is implemented and machine learning models are deployed to production. Through MLaaS, you can quickly achieve these benefits and begin utilizing the insights for your business.
The following is meant to be an example list of use cases and is not exhaustive.
After the machine learning model is deployed, both near real time inference and batch processing can be used to invoke the model and generate inference. Multiple factors are evaluated to determine the optimal and most cost-effective approach:
The following are high level deployment examples to explain the difference in approaches.
In general, batch inference is used when inference or prediction is needed for a large dataset and is not required to be delivered in real-time.
How does it work?
The following is a basic illustration example. In this scenario, the machine learning model is deployed on AWS and multiple data sources can be used to upload a dataset that will be consumed by the model. The dataset should adhere to specified data requirements (i.e., fields, format, file type, etc.).
Once the data is uploaded to a designated S3 Bucket, batch processing is triggered using Lambda function. The event can also be scheduled if needed. Depending on the data volume and use case, the model’s output can then be stored in a database or S3 bucket and then used for visualization. Other mechanisms can also be used to broadcast the result, such as Kafka message.
The name real-time inference suggests a best fit scenario. In this case, you would need a persistent endpoint with low latency requirements. Depending on the use case, one or multiple machine learning models can be deployed to one endpoint.
With a simple API call from the desired application, the model is invoked via endpoint for inference and results displayed interactively or stored.
Our goal is to help people create machine learning success stories. We want you to quickly realize the benefits of scaling Ai across the enterprise. By using the machine learning generated insights, you can increase revenue and reduce costs and risk.
We are making it even easier to gain access to our large library of machine learning models through our MLaaS offering. Why wait to get results your business can use to increase customer satisfaction and beat the competition? Our team of data scientists drive superior results in record time, significantly accelerating your time to value.
