According to a Gartner report in 2019, there has been a 270% increase in the use of AI in some form in enterprises in the last four years which means the use of AI has increased from 10% to 37% in the last four years and tripled in the last one year. This is a huge change in the industry and it's not confined to any particular sector but it has been the story for every sector. It is also reported that AI is used in some form in all the industries but there is an acute shortage of talents. Thus the industry requires a talent pool that can apply AI in various industries and solve different use-cases according to the industry needs. Check out my article - How To Become A Machine Learning Engineer? and Why we need to know about Machine Learning? (ML001) to understand the Machine learning ecosystem and what is expected from a Machine Learning engineer.
Why we need an Artificial Intelligence Platform?
We all must be thinking about why there is a need for an AI platform, and in this section, I will be exactly discussing why we need an AI platform for enterprise use-cases.
AI is changing the way we used to perceive how work was done previously in an era where there was no AI. In the last few years, AI capabilities have increased manifold due to the huge data that the enterprises have these days and also because of the compute capability we have today with the advancement in processors and GPUs.
We are still far from AGI which is Artificial General Intelligence where AI can take up all the tasks but we have entered a phase known as the AI-augmented work which is also known as "Augmented Intelligence".
The main question that we see coming with the current AI ecosystem is that how many AI POCs are ending up in production? This is a very good question one can ask because at the end of the day it all depends on how your work can be put into production and how much can it scale. According to a report by Gartner suggested that by 2021, more than fifty percent of the AI POCs will not end up in production due to operational problems. This is the main challenge that the Enterprises face today in the AI ecosystem as the main of the AI projects fail when it comes to production. Thus there is a need for an Artificial Intelligence Platform which will help enterprises productionize and scale their AI models.
HPE Machine Learning Ops
In 2019, Hewlett Packard Enterprises acquired a company named BlueData which was a leader in providing software that helped enterprises deploy AI and big data analytics. This acquisition was made by HPE to boost their capabilities in this rapidly growing AI market. By 2022, the AI and big data market are expected to grow to approximately $160 billion. So, everyone wants a pie of it.
BlueData was found in 2012 and it used container-based technique to help enterprises deploy large scale machine learning and big data environments at a low cost and simpler way. By combing BlueData's platform with existing HPE infrastructure, it can lead a pathway for digital transformation by providing companies and enterprises with an AI platform where they can easily deploy their Machine learning models.
Jason Schroedl, VP Marketing, BlueData HPE, told Gartner, “Data scientists, they’re not operations people. They don’t understand what it takes to deploy models into production in an enterprise environment, to get all the systems working, make sure it meets all the security requirements, that it’s run on the right infrastructure, that it has access to the GPUs it needs for training. Data scientists are guys who write algorithms, they build models, that’s the stuff they’re really good at, and you don’t want them to spend time on operations. It’s a team sport, you need multiple different players, multiple different users involved, from the data engineers to the data scientists and analysts, to the machine learning developers and architects all the way through to the DevOps and operations teams.”
He also added, "A new model may work the first time as an artisan, hand-crafted solution, but how do you do this at scale, when you’ve got different data science teams and use cases and projects they’ve got underway, to operationalize these models and make sure they’re in production?”
Features of HPE
The HPE Machine Learning Ops comes with a variety of features to make the life of a data scientist easier. It offers a large range of features to ensure that the machine learning models are easily productionized and scalable. Some of its features include -
Data Management - It provides a bunch of features and tools which enable users to easily manage their datasets which is the most essential part of a machine learning project pipeline.
Model Building - It comes with pre-defined packages which will help data scientist and ML engineers to get started with model building.
Model Training - Supports different types of model training which includes training with multiple GPUs.
Model Deployment - Easy deployment in a few clicks.
Model Realtime Monitoring - One can monitor the model lifecycle easily using tools.
Collaboration - Teams can collaborate on Machine learning projects with features like continuous deployment, continuous integration, delivery. They can collaboratively code, build models, and train machine learning models.
Security and Roles - Supports several levels of the authentication mechanism and follows industry level security norms for data and models.
Hybrid Deployment - Support for public, hybrid clouds.
It also supports machine learning and deep learning libraries like Tensorflow, MxNet, Keras, Pytorch along with H20.ai.
You can visit HPE and know more about Machine Learning Ops - Link
Hope you liked my article. If you have any questions and doubts related to this topic or any topic in AI and machine learning, do let me know in the comment section, and I will be more than happy to help you out. Do hit like on this article and share it among your friends who are in AI. Follow us on Instagram and Twitter - @theaibuddy. Let's democratize AI.