Why ML Ops Is Increasingly Relevant in Today’s Enterprise Environment
Machine learning is one of the most exciting new technologies that could change the world. We’re already seeing machine learning and artificial intelligence being deployed all around us, from automatic ATMs to customer service. Be that as it may, there’s no question that it’s integral to developing the modern world.
For this reason, we’ve decided to cover model training, explain it in layman’s terms, and put four of the most popular ML ops against each other to find out which one is best for your unique needs. But first, let’s clear up some common things.
What Does Machine Learning Consist Of?
Machine learning has been an intricate, complex, and utterly confusing technology at some point. Unless you’re a machine learning expert, getting a hand over all the technical jargon can be quite a challenge. In basic terms, machine learning consists of advanced computer algorithms.
These computer algorithms enable learning, which in turn enables the AI. The groups of these algorithms can define their purpose, which only works to extend the complexity and sophistication of the machine learning process. There are three core concepts of machine learning model building, which are:
The representative aspects of machine learning are made up of classifiers that communicate with a computer, allowing it to learn. Evaluation-based objectives allow for scoring function and decision making, and optimization is the highest classifier and search method, which enables AI as a whole. These can be preset or custom, depending on the unique needs of the machine learning process.
Another very significant portion of model building is direct messaging. Direct messaging platforms have been used for a while, and with a selection so vast, it’s tough to determine which one is best for each machine learning operation.
To get a better idea of how all that works, we’ll go over the most critical aspects of training ML models, the benefits of MLOps, and how the most successful companies out there are doing things.
How ML Models are Trained
The first thing to note is that ML algorithms are bombarded with data from which it learns specific patterns, and it’s the process itself that creates what we call a “model” of machine learning. You’d generally go about it by predetermining the volume of data that will be processed during training. That’s simply the maximal amount of patterns the ML algorithm can create, expressed in megabytes.
If there are enough patterns to fit 20 MB less than the maximum, then the model size will be smaller. However, if there are more patterns than can fit the maximum size, then only the most relevant and important ones will be used in the model.
The amount of data doesn’t need to be extremely large for you to get a model that makes solid predictions, as it skims through all that data looking for patterns. Adding another 50 or 100 MB of data may not yield many new patterns, which is why you need to experiment a bit to find the right model size. You can also set how many times the ML Model will pass over the data.
You’ll need a separate data source to use for evaluation – the rule of thumb is to use ⅔ of the available data for training and ⅓ for evaluation. The data takes the form of tabular text-based formats (.csv), columnar (.parquet, .orc, and .petastorm), nested (.tfrecords, .json, .xml, and .avro), array-based (.npy), and hierarchical (.h5 and .nc) – all of which have their unique uses and quirks.
The model itself will be saved in a binary format, and depending on the circumstance can include .zip, .mlmodel, .pkl, .onnx, .pmml, .pt, and .pb.
What Makes the Best MLOps Platforms so Effective
Companies are looking for quick returns and automatization in the current business landscape, making what we would term the “productization” MLOps platform approach a more viable option. Here, the focus is on automatization, ML pipelines, and the most effective ways of actually deploying the ML models – as opposed to purely data analytics oriented platforms.
Why Every Company Doing ML needs MLOps
Well, the simplest reason is that MLOps acts as a ferryboat that helps carry all that theoretical potential into the real world, where it can be deployed efficiently. You need that back and forth from the engineers in the field and the data scientist if you want to examine how an ML model stands up to the pressure of real-world data. That’s the only way to make the right modifications that will perfect the model, allowing it to truly shine when you redeploy it.
A Sampling of Four ML Ops Platforms
Finding the best machine learning operation platform is not the easiest thing in the world. For starters, there are so many systems and platforms that guarantee the best possible features for the lowest possible price, and without being an industry expert and testing these out, you can’t determine which is the best one.
We’ve decided to review many platforms on our own, and we’re determined to bring you the best that the industry has to offer.
Below, we’ll present four different machine learning software ops that we consider worth consideration for your ML needs.
Algorithmia is one of the most popular options available. It has a built-in AI/ML marketplace, which is one of its most prominent features. Outside of that, Algorithmia has a well-established community and many resources, which means anyone can get a hold of it in no time at all.
Complexity-wise, the whole platform is straightforward to use. It’s an entry-level ML Ops platform, which means that almost every aspect is streamlined to the maximum. If you have no prior experience building models or using an ML ops platform, you’ll find that Algorithmia is the premiere option for you.
Algorithmia is wholly compliant with every commercial operating system, including most of the denominations of Linux. It is also compatible with most API’s floating around the web, making integration a breeze.
Paperspace is a machine learning operations platform with a focus on simplicity. Every aspect of this service is entirely modern and streamlined, taking menial work out of the process.
It’s machine learning at it’s finest, as it’s employed to make this machine learning platform as simple, slick, and easy to use as possible.
Paper Space also has a selection of features, tools, and benefits, which, when combined, allow you to micromanage almost every aspect of your machine learning process, making this tool suitable for projects of all shapes and sizes.
Aside from all of the standard features, Paperspace has full CI/CD support, model versioning, and top-of-the-line performance monitoring. While still relatively simple to use, people with no prior experience won’t have an easy time using Paperspace.
Metaflow is a real-time platform with a particular focus on data science as a whole, It was initially developed for Netflix, and it has a full framework built for micromanagement. Every single aspect of this machine learning ops platform is fully adjustable to fit your specific and unique needs.
In most cases, this software is used where heavy-duty machine learning ops are required but works well with smaller-scale operations. It’s one of the strongest pieces of software available, but it’s also easy to use enough that you don’t need too much prior experience.
Where this platform falls behind is its relatively complex integration. Aside from that, it’s tried and tested by Netflix and powered by the Amazon AWS cloud, making it a top-tier platform.
Google Cloud AI Platform
Saying that Google’s AI Platform for machine learning ops is anything less than ideal would be a crime. Like any other Google software solution, this platform is powerful, durable, easy to use, and applicable to almost everything.
Unlike most Google software, though, this platform is a relatively new addition to Google’s assortment, meaning it still has a lot of development to go through before it’s the best on the market. One of the most appealing aspects of this platform is it’s virtually unmatched speed, which is standard with Google products.
ML Operations and Platforms, based on the reasons we’ve outlined above, will increasingly play an important role in the enterprise ecosystem. In fact, Forbes predicted that this market is expected to reach $4B by 2025. As such, we anticipate that these platforms will become more user-friendly and sophisticated. At the same time, enterprises that begin to think through and design their enterprise AI / ML strategy will benefit from leveraging such a platform to accelerate their ML efforts.
If you have questions, or would like to learn more about how Pandio plays a critical role in enabling enterprises to quickly take advantage of AI, contact us here.