Rethink Machine Learning and Harness the Power of AI With Pandio AI Orchestration

Simplify your data pipelines and put AI to work for you faster and with less expense than you’ve ever imagined

In today’s fast-paced, ever-evolving, technology-driven business environments, success is often tied directly to automation. 

Why? Because with automation, our business units can streamline processes, decrease chances for human error, and increase efficiencies while saving time and money—and often without having to hire more personnel or recruit staff with specialized skills.

Unfortunately, many companies find themselves quickly buried under disparate data stacks, where one team or department or location seeks out a specific software solution to solve a specific problem. Before you know it, in that drive to innovate and move into more automated work environments, our siloed teams often create disjointed workflows pulling data across systems and locations that often don’t sing the same tune. 

When moving alone in a single workflow or department, that data flow might be music to a few employees’ ears, but across an entire operation, there can be a lot of dissonance and disconnect, creating noise pollution in what should be more efficient operations to drive better data-driven business decision-making.

If you have to cover your ears and your eyes when your teams start talking about all this disparate data and workflows, then it may be time to look for a new way to conduct your business. That’s where data orchestration, or more specifically AI orchestration, comes into play.

Conducting Data Flows

Here’s the problem: If your business wants to automate workflows and data movement, and you’re trying to build efficient and free-flowing pipelines across your operations to direct this data into artificial intelligence and machine-learning models, then you’re going to need a conductor to coordinate the moves to get everything through and get all that data on the same page, or at least in tune with one another.

All that dissonance within organizations is why, in large part, more than 90% of most AI and machine-learning initiatives fail. 

Companies often get bogged down in unnecessary long, drawn-out processes that leave team members drowning in research about data source connections, doing all the back-end work to connect those sources, clean and process data, feed it into a pipeline, train it, create and train machine learning models, hand all that work off to other teams, and then re-loop processes over and over with few, if any, tangible results.

The reality is, however, adopting AI and machine learning for your business doesn’t have to be that complicated. That’s why Pandio is leading the way for AI orchestration for big data.

What is AI Orchestration?

In simple terms, AI orchestration represents all the people, processes, tools, and resources your business uses related to a common goal—to enable AI or machine learning for data-driven decisions.

With Pandio, that complex process above looks more like this: connect your data sources with Pandio’s datasets, create your pipeline, deploy it to Pandio.com, and start reaping the benefits of machine-learning for your business.

So, when it comes to AI adoption, where does the confusion or fail point usually happen for most businesses?

For a visual, in this scenario, it might be helpful to think of all the data moving through your company’s day-to-day operations as music notes. 

Each employee, system, application, etc. sings its own song and emits hundreds, thousands, maybe millions of music notes—your data. Those notes float around your departments or offices, and maybe land in the ears of a few key people who need to know that tune to make decisions. 

But some of those recipients are tone-deaf. Others don’t like your music, and some just can’t carry a tune. You’re left with what sounds like a middle-school band on warm-up or, quite simply, custom, complex, expensive, disconnected data workflows that are prone to fail.

What you want, instead, is a well-tuned, well-rehearsed, professional orchestra uniting to move your business to a standing ovation for success.

So how do you do it? If you haven’t already, consider employing a single platform that can handle all of your moving notes, get them in tune with one another, and then create harmonized, useful data. 

Pandio’s cloud-native AI solution does just that. Here’s how:

Data Access

Like an orchestra conductor, you need to get all of your data in the right place. Think of it as musicians sitting in the right chairs, grouped with similar instruments. While each instrument grouping may be different, they play well together. With AI and machine learning models, this is your data access point. It’s how you orchestrate your models and messages across your organization to reach new levels of AI automation.

Pandio’s managed open source Trino (the next generation of Apache Presto), for example, enables you to access and query all of your data from anywhere at any time. This access sets the stage for your performance—so you can move, query, and analyze that data to make data-driven decisions.

Like we see in many current work environments, some of which are now hybrid models of remote-work opportunities post-pandemic, your data can be in multiple places and in various forms, but it still needs to work together for common goals. 

For example, you might have some important data streaming in a cloud-hosted environment. Or maybe you have other data you need that’s in a database stored on an on-prem mainframe. 

Regardless of where that data lives, you need to access it and leverage the data for decision-making. 

And in a lot of situations, you don’t always want to have to replicate the streaming data in a data store then go get the database file and move that, too. In a more optimal environment, you want to be able to access that data for queries, wherever it is, without having to physically move it.

Managed Presto, with Pandio, gives you that ability. Presto enables you to query in place through a distributed SQL system.

Presto enables you to work through multiple data sources. You’re not limited to just one or two like other SQL engines. Presto can actually handle all of them, and it’s NoSQL compliant. It can even query multiple sources with just one query so you can get the data you want quickly.

With Presto, you can feed data from any source into your pipeline at scale.

Automation

But sometimes, your data is in the wrong chair. It’s like sitting a flutist in the middle of the percussion section. You know that little flute is in there somewhere, but you can’t hear it, so you need to move that data around in your organization so you can put it to better use. It’s like giving that flute player a solo instead of getting drowned out by all those drums.

In this instance, you likely want the ability to move data around easily within your organization, without that data bumping into each other and getting all messed up. A solution like Pandio’s Apache Pulsar can help you do just that. And, you don’t want to have to stand at the front of the room and tell all that data, piece by piece, where it needs to go. Ideally, you’d want to use automation to move that data seamlessly.

In most AI and machine learning models, that happens through streaming, queueing, or pub-subbing events. Yet, you’ll often find many AI and machine-learning solutions can’t handle all three. Most just do one, such as streaming, and require add-ons or extras to (sorta) get one or two of the other options.

But not Apache Pulsar. It works in any environment, using any message format, and via any connector. It’s the fastest way to build that data pipeline to get the data you need most to the cloud as fast as possible.

Pandio, which leads the AI orchestration market, can do all three, simply and easily with unmatched throughput, latency, and durability. It can even handle millions, even billions, of events for large enterprises every day. 

It’s why Apache Pulsar’s is the only solution you’ll need for distributed messaging and event streaming.

Unlike other solutions on the market, you can even stream and queue within a single subscription model—no need for add-ons or additional costs. Because Apache Pulsar has multi-tenant capabilities, you can even serve multiple applications within a shared environment with simple architecture and management. 

And Pandio can move data at more than two-and-a-half times faster and at nearly half the cost of what you might expect for similar services. The best part? There’s nearly infinite horizontal scalability with Pandio.

Machine Learning

And finally, if you’re writing that perfect song for organizational efficiency and data-driven business, you need to learn from the data you’ve accessed and automated. With Pandio, that happens within machine-learning models. 

With Pandio’s open source python library for machine learning, PandioML, you can build robust machine-learning models with powerful prediction capabilities—without ever needing to hire data scientists to train your proprietary systems to do that for you. 

Instead, with Pandio, your team can design, train, and deploy local machine learning models generally in 30 minutes or less. The result? You’re quickly using and adopting machine learning across your organization without the burden of additional staffing and other expensive resources.

One of the best parts is you can use your existing on-staff software engineers to develop these models in truly cost-efficient, effective ways.

From there, you can use your powerful machine-learning models to do predictive work to help run your business more efficiently and instantly adapt to your changing environments.

AI Orchestration with Pandio

If you’re looking to move your business into AI and machine learning, the process doesn’t have to be as painful as you might think. You don’t have to move every last piece of data—and related systems and software—within your organization into the cloud before you get started.

You don’t need giant budgets or months or even years of planning only to end up with something your team doesn’t understand and no one wants to use anyway.

Instead, Pandio’s easy-to-use AI-driven architecture does a lot of the hardest work for you. With automation, it can simplify orchestration of your models, your data, and your machine learning tools.

And, Pandio will work with you to put together a solution that works best for your company’s unique needs—whether that’s as a managed service, an on-premises solution, a cloud-hosted environment, or hybrid model.

Did you know Pandio is also agnostic for database language? It’s like putting bass clef players and treble clef players all on the same page. And because Pandio is open-source and works with a broad range of vendors, you can use it to craft your own tune—one that gets the most out of AI and machine learning for your company’s unique and changing needs. That means Pandio’s AI orchestration works with any data, any tools, and in any cloud.

Whether you’re a fast growing company working to leverage machine learning for the first time or you’re a large-scale enterprise with well-established infrastructures, Pandio has the flexibility to help you accelerate your path to AI. That open source back-end means it’s easy to integrate Pandio into your existing environments without re-architecting all the pieces that already work for you.

Ready to learn more about how Pandio can help you conduct AI orchestration that’s music to everyone’s ears? We’re happy to work with you on a proof of concept (POC) to demonstrate the tech’s value. Or, if you’re an engineer or developer and you want to see how Pandio works firsthand, you can try it out on a free trial basis online. 

And, if you already have data scientists on your team, they’re going to instantly fall in love with the simplicity of PandioML. Your team can build complex machine learning models in production in just a few days in their most preferred environments such as PyCharm or others. They won’t have to write as much code (which they’ll be happy about and it will decrease the chance for errors), and they can utilize existing data, models, and pipelines without a complete architecture overall.