Everything You Need to Know About Data Science Trials

Posted by Marbenz Antonio on March 22, 2022

When productivity and cooperation are strained, machine learning models can’t be audited or replicated, and models aren’t making it into production, it’s time to switch to a data science platform. With enterprises seeking to integrate diverse data sources and applications, data integration has become increasingly difficult.

If this describes your company, it’s time to try out a data science platform. While determining the ideal solution might be time-consuming, we’ve compiled a list of must-have components for a successful machine learning experiment.

In summary, your objective should be to identify a data science platform that solves the difficulties you face daily as a data scientist so that you can effectively drive business outcomes. This involves searching for a platform that provides a set of tools to assist you to do your job faster while also allowing it to be shared, audited, replicated, and scaled.

Know Which Data Science Problems You’re Trying to Solve

As you are aware, the nature of a data scientist’s work requires very little computing on some days and a great deal on others. This fluctuating workload can be difficult for IT, which may also have to deal with the pressures you place on databases or your requirements for increased security levels when you operate in production environments. A solid data science platform may help data scientists and their teams reduce their reliance on IT while increasing their productivity and efficiency.

Other data-science-related issues include:

  • The provenance of data and models
  • Keeping track of code versions
  • Notebooks are shared.
  • Using pipelined processes to speed up workflow
  • Once models are in production, they must be able to be replicated and audited.
  • Large volumes of data must be stored and moved.
  • Model deployment is separated from engineering, allowing them to own models from start to finish.

Keep in mind that once you’ve decided on a data science platform, you’ll need to submit your findings to IT. When you do, keep in mind that you’ll be able to operate more effectively without increasing expenses, compromising security, or demanding round-the-clock assistance.

What Should You Evaluate in a Machine Learning Trial?

Instead of spending hours on the phone with customer support reps, seek free or low-cost machine learning trials that let you at least a month to check out multiple services. Some trials include real-team coaching, but choose for ones that are automated and straightforward to use—you’ll have plenty of time to speak with a provider when you’re ready to move forward.

The following is a checklist of critical elements to consider while conducting a data science trial:

Data Science Service Set-up

One of the first things you’ll want to do is set up your primary work environment and assess your available resources. Keep an eye out for:

  • A data catalog service that uses a structured inventory of data assets to discover and regulate data.
  • There are a few example notebooks or lessons available to rapidly get you up to speed on the tools. They must present examples that are relevant to your process.
  • The flexibility to apply numerous tools and libraries seamlessly, as well as share notes with coworkers, to improve productivity.

Running Big Data Application

Running Spark on-prem can be difficult for data scientists since the systems are designed for production workloads rather than the bursty ad hoc workloads that data scientists generate. One of the most compelling reasons to use a cloud-based data science platform is this. Make sure the capabilities for big data applications are included in your data science trials:

  • Is well-suited for use in a laptop environment.
  • Batch and ad hoc processing are available.
  • Provides centralized application control and visibility.

For example, Oracle Cloud Infrastructure Data Flow, Oracle’s data science platform, supports MLlib in Spark, allowing you to create models using industry-standard methods. It’s serverless, which means data scientists may quickly supply only the resources they need to perform a job before destroying the cluster. The goal of a data scientist’s job is to put business insights and machine learning models into production. Patching, updating, and controlling the clusters are of no use. The serverless method relieves you of that load, allowing you to concentrate on areas where you can provide actual value to the company.

Cloud Analytics & Autonomous Databases

Strong cloud analytics and access to self-contained datasets are signs of a mature data science platform. Make a point of looking for:

  • The ability to create a temporary database easily.
  • The ability to create models by applying computation to data
  • Analytics solutions that can deal with data from different sources in a transparent manner
  • Data transfer is minimized using scale-out processing.
  • Databases with built-in machine learning tools

It proposes connecting to the Oracle Autonomous Database and playing with its data visualization capabilities on Oracle’s data science platform. To verify the simplicity of data migration, and also propose putting up Oracle Autonomous Data Warehouse and utilizing the example data in the SH scheme, or loading your data. Finally, try Oracle Machine Learning to discover how you can simply train, test, and tweak machine learning models using data science notebooks while the database handles the heavy work.

Block Storage and Data Integration

Ensure that your data science platform solution provides unlimited storage at a low cost, as well as easy interaction across databases and other data sources. Add the following to your to-do list:

  • A platform where you don’t have to worry about provisioning and maintaining your infrastructure.
  • Payment alternatives keep costs low by requiring payment only when infrastructure resources are used.
  • A solid data integration to the block-storage pipeline. The speed and ease of use of the solutions’ extract, transform, and load (ETL) functions will give you a decent indication of how well they integrate.
  • Possibility of replicating vast amounts of data and then discarding it

Data Catalog

The data catalog in your data science platform is critical to discovering, finding, organizing, enhancing, and tracing data assets. During your trials, you should search for the following features:

  • Self-service tools that assist you in finding and managing data throughout your organization.
  • Transparency and traceability help governance and audibility by allowing you to know where data comes from.
  • Data management processes may be automated to help you increase productivity at scale.

Innovative New Data Science Tools

Each data science platform will have cutting-edge technologies you may not be aware of. Keep track of which solutions provide the kind of advances that best suit your requirements and budget. It should help you improve your workflow by speeding up repetitive tasks and giving you the chance to add more value to the company.

Key Notebooks to Test Oracle’s Accelerated Data Science SDK

The Oracle Accelerated Data Science (ADS) is one of the platform’s standout features. ADS is a native Python library included in the Oracle Cloud Infrastructure Data Science service that includes capabilities for the whole predictive machine learning model lifetime. Data gathering, visualization, profiling, automated data transformation, feature engineering, model training, model assessment, model explanation, and recording of the model artifact itself are all included.

Once you’ve got your model, you may use its characteristics to conduct machine learning explainability. It should be agnostic to model structure and offer you knowledge of how the model works so that you can trust that it has learned the right things and that you can check for bias in the model. After you’ve done that, you can rest assured that it will function admirably once it’s in production.

When trying out ADS, strongly advises you to try out the following notebooks:

1. Working with an ADSDataset Object (adsdataset_working_with.ipynb): The data itself is one of the most critical aspects of any data science effort. This notebook will show you how to use the ADSDataset class. The ADSDataset is similar to a data frame, but it comes with a lot of extra capabilities that will help you optimize your workflow.

Why it is important: Having a powerful way of representing your data in the notebook will improve your performance. The ADSDataset allows the data scientist to work with data that is larger than what will fit into memory but manipulate it as if we’re all in memory. Also, it has features that link the data to the type of problem that you are working with. It allows you to define the dependent (target) variable that the ADS model will understand and it also helps in exploring the data.

2. Introduction to Loading Data with the Dataset Factory (datasetfactory_loading_data.ipynb): This notebook explains how to read data from a variety of common formats using ADSDataset. There’s no need to learn a new package for each data source or format because the DatasetFactory.open() function takes care of everything.

Why it is important: This notebook explains how to read data from a variety of common formats using ADSDataset. There’s no need to learn a new package for each data source or format because the DatasetFactory.open() function takes care of everything.

3. Introduction to Dataset Factory Transformations (transforming_data.ipynb): It is critical to recognize and correct data condition concerns to get the maximum performance out of your model. Different transformations should be employed depending on the type of model being utilized. This notebook demonstrates how ADS may assist you with this.

Why it is important: Cleaning up data condition concerns takes up a lot of a data scientist’s work. The ADSDatasetFactory class makes it simple to locate and resolve these issues. It also offers an automated workflow for the data scientist to follow.

4. Classification for Predicting Census Income with ADS (classification_adult.ipynb): You may use the OracleAutoMLProvider tool to create a classifier for the public Census Income dataset in this notebook. This is a binary classification task, and the dataset may be available at https://archive.ics.uci.edu/ml/datasets/Adult for additional information. You can investigate the Oracle AutoML tool’s numerous settings, which allow users to exert control over the AutoML training process. Finally, you may compare and contrast the various Oracle AutoML-trained models.

Why it is important: The ADS SDK includes a set of strong tools that are based on open-source libraries. This notebook shows how to use AutoML to create high-quality models in a real setting.

5. Introduction to Model Evaluation with ADSEvaluator (model_evaluation.ipynb): 

You can examine the possibilities of the ADSEvaluator, the ML evaluation component of the Accelerated Data Science (ADS) SDK, in this notebook demo. You’ll learn how to apply it to evaluate any broad class of supervised machine learning models, as well as to compare models within the same class.

This notebook covers binary classification with an asymmetric data set, multi-class classification using a synthetically created data set of three evenly distributed classes, and finally a regression issue. Open-source libraries would be used to train the models, which would then be assessed using ADSEvaluator. It highlights how ADSEvaluator may improve the tools you already use.

Why it is important: The process of evaluating models is quite conventional. The ADSEvaluator shortens the process by deciding which metrics to examine and then calculates them for you.

6. Model Explanations for a Regression Use Case (mlx_regression_housing.ipynb):

You will use this notebook to do an exploratory data analysis (EDA) to better understand the Boston housing dataset. The Boston housing dataset is a regression dataset that provides information about houses in Boston, Massachusetts’ various areas and suburbs. The target variables are continuous values that indicate the house’s monetary value.

You’ll train a model to forecast home prices, then assess how well it generalizes to the situation. When you’re happy with the model, you may investigate how it works utilizing model-agnostic explanation approaches. You’ll learn how to create global explanations (to assist you to understand the model’s overall behavior) as well as local explanations (to understand why the model made a specific prediction).

Why it is important: Understanding what a black box model is doing can be difficult. It’s also crucial to check for bias and ensure that the model has learned the proper information. The data scientist can achieve this with the use of machine learning explainability (MLX).


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