6 Benefits of Using MLOps For Your Machine Learning Application

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Generated: 8/8/2022
6 Benefits of Using MLOps For Your Machine Learning Application

The most interesting thing about machine learning (also known more recently as artificial intelligence) is of course how quickly it is taking off in the world. It took years for it to become really tangible; but now that its basic building blocks have emerged, AI shows no signs of slowing down. As with previous major technology leaps, it does not appear to be slowing down anytime soon.

All that said, this technological innovation is still fairly new in some respects. For one, its use cases are fairly complex for end users; and secondly, not too many businesses are really using it in a mature way – at least not at the same level as more traditional business areas.

However, as machine learning is gaining traction in the market, companies need to invest in more tech for it; and more particularly to build an efficient infrastructure to support that technology. That means companies have to deal with the basic building blocks that machine learning is requiring.

So what are these blocks? You’re very likely already familiar with some aspects of Machine Learning. These are things like:

Signal Detection and Estimation: One of the most basic parts of a machine learning algorithm is to first determine if something in the environment matches something in the database.

Optimization and Ranking: An essential part of any predictive modeling is to try to rank the quality of the results. When done well, you can also determine the probability of future results.

Classification: The third type of machine learning is the one that gets the most attention lately, but it is actually the least common. The first type of this is known as supervised or semi-supervised learning (which uses a small sample of data to train a learning algorithm and then use it to classify the next large sample of data)

Clustering: The fourth type is one that is often overlooked (but is gaining widespread traction lately) and is based on groups of similar items. It tends to be more focused on understanding the patterns in the data rather than the ‘cause and effect’ relationship.

This is typically how MLops and DataOps work together. For example, with a data science project, the data science team will usually determine what they will be using the algorithm for. What is the type of data they’re going to be dealing with? What will the result from the algorithm be?

Once that is determined, DataOps (sometimes referred to as Operations of machine learning) will then start to research what platforms are needed, how do these platforms operate, and how is data stored in them.

To do that, DataOps is looking at how data will have to be ingested, processed, and used with their data store of choice.

So what types or types of platforms are required? This will depend on what types of analysis will be used; and what types of data storage and compute services are needed. Here are some of the most common platforms used to do analytics for business:

Big Data

Data warehousing

Data warehousing is an attempt to use a process to organize data for a different problem. In most cases, there will be an analytics layer (or something similar) which will try to make sense of the data in as few steps as possible. While there are several different implementations of data warehousing today, more of the technology’s power is coming from the advanced features it may be able to offer.

Hadoop (Hadoop), or the HDFS platform of Hadoop, is often seen as the leader of the pack. This is because it offers a high performing, high throughput, and highly scalable platform. It is the standard of analytics.

Big data itself is just a type of data warehousing. Big is very relative here. It is based on being able to handle very large amounts of data, while at the same time performing the analytics quickly to bring valuable insights to the analysts.

Analytics clouds are also part of the Big Data category. They offer similar capabilities as big data. They do so using a more standardized programming approach (and are based on the same type of architecture – Hadoop).

Analytics on Hadoop and analytics clouds are both fairly recent innovations. There were no large-scale analytic platforms like this in the past. They are seen as necessary for big data in the future.

Data lake

Data lakes – as opposed to being just a huge data warehouse – are very different. They actually make data available for multiple different uses. Instead of being used with more targeted systems, a data lake is more of a standard container that can be taken advantage of by multiple different types of apps.

It is this multi-use factor that appeals to more businesses. For data lake, its appeal is seen with organizations that will use machine learning, but also other more traditional use cases, like business intelligence. It can also be adopted for everything in between. It enables the data to be used in different ways without having to be taken down and re-organized.

It can be an added extra to the business. It isn’t about just being efficient. It is also about enabling the analysts to be able to interact with the data in ways they are currently not able to. This can make a big difference in how they feel about using the data – something they would be more comfortable with if they saw things they were doing already.

Data lake is not a platform, but it is built in many of the big data platforms, including some data warehouse platforms (such as Cloudera). Its appeal is not just as a data warehouse, because of its flexibility and open nature.

What is also different about data lake is the fact that it often comes pre-processed – all the data is already present and ready to use. It just needs to be managed differently; and this again gives it versatility that some of the other platforms can’t match.

Big data platforms

There are a lot of different big data platforms today. There are vendors that have existed since the beginning of the big data age, like Google and Amazon, and others that are just beginning to emerge.

It’s hard to really tell which are the best because none of the technologies is fully mature. No one company is offering the perfect solution. Each platform has advantages and disadvantages.

One of the most well known vendors today is Google. They offer an analytics cloud which is similar to Hadoop in a lot of ways. Their analytics cloud offering is a bit different since it does not use Hadoop. Instead, it uses an open source solution called BigTable.

Many of the features in Google’s analytics cloud can be used by other vendors.

They recently expanded their machine learning capabilities by doing some work on Apache Stratos (Stratos), which is another platform focused on analytics. It includes some components (Kibana), but it is not tied to that platform. In fact, one of the main selling points of Stratos is the fact that it can function as both Big Data and machine learning platforms. But it was specifically built to be used as a platform. It has been around for a while – since 2011.

Microsoft has an analytics engine – Analytics Application Programming Interface (API). It has a couple of different implementations for different languages. The main difference here is that it is a.NET-based platform. That means the languages it uses will be based around C# and Visual Basic.

IBM is also an older and very well-known big data technology platform provider. It has a Hadoop-based strategy, which offers a platform similar to Hadoop. It is available as an appliance. It can be deployed on-premises or as a cloud-based service. It can also be a hybrid – which is not ideal. For hybrid solutions, the company offers a PaaS (Platform as a Service) solution which is hosted. It is a way for big data users who want to use a hybrid approach but still want to utilize a large-scale infrastructure.

Amazon is fairly new in the big data space. It offers its own AWS platform for running analytics apps as well as several other services. It also recently brought in Hadoop natively and now offers a way to deploy Hadoop. The AWS Hadoop platform is based on Hadoop 2.0. That might sound strange, but it just means they’re using some components that have been built specifically for Hadoop 2.0, not the full version of Hadoop.

Google analytics

Google Analytics is also one example of where big data is not always a good solution. This is because when it comes to machine learning, it offers relatively little. It has a data modeling and segmentation tool. It is pretty basic, but this is the case with most analytics or analytics ‘clouds’. It is designed to make it easier to use.

There are other analytics offerings which are very similar and can be used in tandem with other machine-learning tools; but Google Analytics is more or less what is available.

In this case, it would be helpful to understand why it does not offer much in the way of machine learning. The fact is that the reason for the lack of these capabilities is Google’s strategy to focus on the mobile first movement within marketing management. They want to ensure marketers do not confuse web analytics for mobile analytics. It is designed to help marketers make informed decisions about their mobile sites. It is not meant to be used for more complex analysis. This will still need to be done by someone. But Google’s analytics engine does not offer the tools to do this. Those do need to be supported by platforms like Big Data.

Data lake

A data lake is much more of a general purpose database than a data warehouse.

Garett MacGowan

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