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AI Ready

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How to be AI ready

You need to create a pipeline in order to have a successful AI use case

We align your business needs with a carefully crafted solution based on the best practices available. The process needs close connection and communication between us and your domain experts, we analyze the whole situation and will proceed as stated to create the pipeline for you in order to be able to use cloud to serve as an AI driver for your specific machine learning need

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We create your ML pipeline

In AppiLux we understand all the concerns for having a thriving AI system

we have paved this path and know that by using the right tools among the best cloud options for a specific use case, the best achievements can be seen. That's why we are running our AI solutions on the cloud and we chose to be multi cloud rather than a single cloud platform reseller.

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Want to know more?

Read below content to be more familiar with the process

Be AI ready by having the ML pipeline in place

ML pipeline

Just as developers have a pipeline for code, data scientists have a pipeline for data as it flows through their machine learning solutions. Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

It’s tempting to think of machine learning as a magic black box. In goes the data; out, come predictions. But there’s no magic in there—just data and algorithms, and models created by processing the data through the algorithms.

If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box. The more you understand what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.

ML pipeline

The machine learning pipeline consists of four phases:

 

  1. Ingesting data
  2. Preparing data (including data exploration and governance)
  3. Training Models
  4. Serving Predictions

A machine learning pipeline needs to start with two things: data to be trained on, and algorithms to perform the training. Odds are the data will come in one of two forms:

 

Live data you’re already collecting and aggregating somewhere, which you plan on making regularly updated predictions with.

A “frozen” data set, something you’re downloading and using as is, or deriving from an existing data source via an ETL operation.

 

With frozen data, you generally perform only one kind of processing: You train a model with it, deploy the model, and depending on your needs, you update the model periodically, if at all.

with live or “streamed” data, you have two choices regarding how to produce models and results from the data. The first option is to save the data somewhere—a database, a “data lake”—and perform analytics on it later. The second option is to train models on streamed data as the data comes in.

 

Depending on your use case, it may be needed to train a previously created model using the new data but we’re not adjusting the underlying model all that much or train a whole new bunch of fresh models from scratch.

This is why choosing your algorithms early on is important. Some algorithms support incremental retraining, while others have to be retrained from scratch with the new data. If you will be streaming in fresh data all the time to retrain your models, you want to go with an algorithm that supports incremental retraining.

Data preparation

Once you have a data source to train on, the next step is to ensure it can be used for training.

Real-world data can be noisy. If the data is drawn from a database, you can assume a certain amount of normalization in the data. But many machine learning applications may also draw data straight from data lakes or other heterogeneous sources, where the data isn’t necessarily normalized for production use. This step can be done locally using programming tools and experts or also can be done using some of the cloud tools that are available to do that which are designed for data preprocessing. Although this step is not necessary for all use cases it doesn’t hurt to do that anyway, although for some algorithms and use cases its vital.

Cloud, yes or no?

Depending on the scenario we may choose to have hybrid solutions which is a collection of on premise and cloud setups

Training

Once you have your data set established, next comes the training process, where the data is used to generate a model from which predictions can be made. You will generally try many different algorithms before finding the one that performs best with your data.

Cloud Deployment

The last phase in the pipeline is deploying the trained model, or the “predict and serve” phase.

Where and how this prediction is served constitutes another part of the pipeline. The most common scenario is providing predictions from a cloud instance by way of a RESTful API. All the obvious advantages of serving from the cloud come into play here. You can spin up more instances to satisfy demand, for example.

With a cloud-hosted model, you can also keep more of the pipeline in the same place—training data, trained models, and the prediction infrastructure. Data doesn’t have to be moved around as much, so everything is faster. Incremental retraining of a model can be done more quickly, because the model can be retrained and deployed in the same environment.

The term pipeline implies a one-way, unbroken flow from one end to another. In reality, the machine learning flow is more cyclical: Data comes in, it is used to train a model, and then the accuracy of that model is assessed and the model is retrained as new data arrives and the meaning of that data evolves.

Real-world data can be noisy. If the data is drawn from a database, you can assume a certain amount of normalization in the data. But many machine learning applications may also draw data straight from data lakes or other heterogeneous sources, where the data isn’t necessarily normalized for production use. This step can be done locally using programming tools and experts or also can be done using some of the cloud tools that are available to do that which are designed for data preprocessing. Although this step is not necessary for all use cases it doesn’t hurt to do that anyway, although for some algorithms and use cases its vital.

Problems of Scale

Even in 2019, most data scientists are doing their work on their laptops and are limited by the constraints of their hardware. As pointed out Thaise Skogstad, director of product marketing at Anaconda:

 

For datasets that do not fit on their laptop, they are still using traditional data lakes and running jobs, at great expense, on Spark or Hadoop which were ground-breaking a decade ago. However, most companies are now looking for a path to modern, potentially cloud-based solutions.

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