Databricks Machine Learning: 3 Critical Aspects
Databricks is a highly-preferred Cloud Data Warehousing and Analytics Solution. It enables its users to build data lakes in the cloud which they can use to store their data. This makes it a very important platform at a time when businesses are generating a lot of big data.
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Data is not useful to any organization unless it is analyzed to extract meaningful insights. These insights help business managers to make sound decisions about running their businesses. Thus, businesses should have access to a tool that they can use for Data Analytics.
Databricks offers many Data Analytics features. One of such features is Databricks Machine Learning (ML). This feature allows Databricks users to create and train Machine Learning Models using their data and deploy them into production environments. In this article, we will be discussing the Databricks Machine Learning feature in detail.
Table of Contents
- What is Databricks?
- What is Machine Learning?
- What is Databricks Machine Learning?
- What are the Critical Aspects of Databricks Machine Learning?
What is Databricks?
Let us start by answering this main question of What is Databricks. Databricks, developed by the creators of Apache Spark, is a Web-based platform, which is also a one-stop product for all Data requirements, like Storage and Analysis. It can derive insights using SparkSQL, provide active connections to visualization tools such as Power BI, Qlikview, and Tableau, and build Predictive Models using SparkML. Databricks also can create interactive displays, text, and code tangibly. Databricks is an alternative to the MapReduce system.
Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform, making it easy for businesses to manage a colossal amount of data and carry out Machine Learning tasks.
It deciphers the complexities of processing data for data scientists and engineers, which allows them to develop ML applications using R, Scala, Python, or SQL interfaces in Apache Spark. Organizations collect large amounts of data either in data warehouses or data lakes. According to requirements, data is often moved between them at a high frequency which is complicated, expensive, and non-collaborative.
However, Databricks simplifies Big Data Analytics by incorporating a LakeHouse architecture that provides data warehousing capabilities to a data lake. As a result, it eliminates unwanted data silos created while pushing data into data lakes or multiple data warehouses. It also provides data teams with a single source of the data by leveraging LakeHouse architecture
What are the Key Features of Databricks?
After getting to know What is Databricks, let us also get started with some of its key features. Below are a few benefits of Databricks:
- Language: It provides a notebook interface that supports multiple coding languages in the same environment. Using magical commands (%python, %r, %scala, and %sql), a developer can build algorithms using Python, R, Scala, or SQL. For instance, data transformation tasks can be performed using Spark SQL, model predictions made by Scala, model performance can be evaluated using Python, and data visualized using R.
- Productivity: It increases productivity by allowing users to deploy notebooks into production instantly. Databricks provides a collaborative environment with a common workspace for data scientists, engineers, and business analysts. Collaboration not only brings innovative ideas but also allows others to introduce frequent changes while expediting development processes simultaneously. Databricks manages the recent changes with a built-in version control tool that reduces the effort of finding recent changes.
- Flexibility: It is built on top of Apache Spark which is specifically optimized for Cloud environments. Databricks provides scalable Spark jobs in the data science domain. It is flexible for small-scale jobs like development or testing as well as running large-scale jobs like Big Data processing. If a cluster is idle for a specified amount of time (not-in-use), it shuts down the cluster to remain highly available.
- Data Source: It connects with many data sources to perform limitless Big Data Analytics. Databricks not only connects with Cloud storage services provided by AWS, Azure, or Google Cloud but also connects to on-premise SQL servers, CSV, and JSON. The platform also extends connectivity to MongoDB, Avro files, and many other files
What is Machine Learning?
Machines can now be trained using a data-driven approach. On a broader scale, if you consider Artificial Intelligence to be the main umbrella, Machine Learning is a subset of AI. Machine Learning, a collection of Algorithms, enables Machines or Computers to learn from data on their own without the need for human intervention.
Machine Learning is based on the idea of teaching and training machines by feeding them data and defining features. When fed new and relevant data, computers learn, grow, adapt, and develop on their own, without the need for explicit programming. Machines can learn very little in the absence of data. The Machine observes the dataset, identifies patterns in it, learns from past behavior, and makes predictions
What is Databricks Machine Learning?
Databricks Machine Learning is an integrated Machine Learning environment with managed services for model training, experiment tracking, feature and model serving, and feature development and management.
You can use Databricks Machine Learning to:
- Train models manually or using AutoML.
- Track the training parameters and models via experiments using MLflow tracking.
- Create feature tables for model training and inference.
- Manage, serve, and share models using the Model Registry.
You will also be able to access other Databricks features like Clusters, Notebooks, Jobs, Security, Delta Tables, Admin Controls, and others.
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- An active Databricks Account.
- Knowledge of Machine Learning Concepts.
What are the Critical Aspects of Databricks Machine Learning?
Now, let’s understand the Databricks Machine Learning feature in detail via the following critical aspects:
- Databricks Machine Learning Home Page
- Data Preparation
- Databricks AutoML
A) Databricks Machine Learning Home Page
The Databricks Machine Learning home page gives you access to the Machine Learning features in Databricks.
To access Databricks Machine Learning, move the mouse pointer to the sidebar located on the left of the Databricks workspace. As the mouse pointer hovers over the sidebar, it will expand. Select the “Machine Learning” option at the top.
Once you visit the Databricks Machine Learning page, you will be automatically changed to a Machine Learning persona. To return to the Databricks Machine Learning page, click on the Databricks logo located at the top of the sidebar.
The following are the resources that you can access at the Databricks Machine Learning homepage:
- The top part of the page shows shortcuts. These allow you to open a tutorial notebook, start AutoML, or create an empty notebook.
- The center of the page shows the items that you have viewed. A click on any of these items will open it.
- The sidebar gives you quick access to the Experiments page, Model Registry, and Databricks Feature Store.
- The bottom of the screen shows links to the documentation resources.
B) Data Preparation
The Databricks Machine Learning feature uses data for training Machine Learning models. The good news is that Databricks can ingest data stored in different formats, for example, CSV, XML, Parquet, JSON, Delta Lake, and others. Databricks can also ingest data from data storage providers like Google BigQuery, Amazon S3, Snowflake, and others.
Databricks can also be integrated with third-party tools and platforms like Tableau, Power BI, Fivetran, and others. These allow you to work with data via Databricks clusters. These platforms enable data ingestion, data preparation, data transformation, Machine Learning, and business intelligence. Databricks also comes with a feature known as Partner Connect that allows you to integrate these platforms faster with your Databricks clusters.
After loading data into Databricks, you will need to preprocess it. The Databricks Feature Store can help you view and re-use existing features, create new features, and choose features to train your Machine Learning model.
For the case of large datasets, you can use MLlib and Spark SQL for feature engineering. The Databricks Runtime ML also comes with third-party libraries such as Scikit-Learn that have useful helper methods.
C) Databricks AutoML
Databricks AutoML enables Databricks users to automatically apply Machine Learning to their datasets. This Databricks Machine Learning feature prepares data for training Machine Learning models and runs a number of trials to create and evaluate multiple Machine Learning models. AutoML displays the results and generates a Python notebook with the source code for every trial that you run. You can view, reproduce, and modify the source code. The model will also return the summary statistics of your dataset and the information is saved in a notebook for future review.
During the Hyperparameter Tuning Trials, AutoML automatically distributes the trials across the different worker nodes of the cluster.
Note that each model is created using open source components, making it easy for you to edit and integrate the models into your Machine Learning pipelines. AutoML can help you to solve Classification, Forecasting, and Regression problems. It creates models based on algorithms provided by the Scikit-learn, LightGBM, and XGBoost algorithms.
Below is the list of Databricks Machine Learning algorithms that you can use to train your models:
- Classification models
- Random forests
- Decision trees
- Logistic regression
- Regression models
- Decision trees
- Linear regression with stochastic gradient descent
- Random forests
Note that although AutoML distributes the hyperparameter tuning trials across the different worker nodes of a cluster, every model is trained on one node. If you are using Databricks Runtime 9.1 LTS ML and above, AutoML will sample your dataset if it can’t fit into the memory of one worker node. It is capable of estimating the amount of memory required to load and train your dataset. If there is a need for sampling, it can also determine the sampling fraction. The sample obtained from the dataset will be used to train the model.
To access AutoML in your Databricks account, do this:
- Step 1: Hover the mouse pointer over the left sidebar, and select the “Machine Learning” option from the top.
- Step 2: Click on the “Create” option and then select “AutoML” from the sidebar.
You will then be allowed to create AutoML Experiments on the Experiments page.
In this article, you have learned how to effortlessly use the Databricks Machine Learning features. Databricks Machine Learning can assist you in data preparation, train your models, and then finally help you in the model deployment process. You can either train your model manually or use the AutoML feature to automatically do it for you. It takes care of Model Training, trial recording, Creating, Tuning, and Evaluating multiple models.
After you successfully train and deploy your Machine learning models in Databricks, you can make strategic decisions based on the model predictions for your business growth. As your firm grows and attracts more customers, tremendous volumes of data start generating at an exponential rate. Efficiently handling this massive amount of data across numerous applications used in your business can be a challenging and resource-intensive task. You would require to devote a section of your Engineering Bandwidth to Integrate, Clean, Transform and Load your data into your Data lake like Databricks, Data Warehouse, or a destination of your choice for further Business analysis. This can be effortlessly automated by a Cloud-Based ETL Tool like Hevo Data.Visit our Website to Explore Hevo
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Share with us your experience of using Databricks Machine Learning. Let us know in the comments section below!