As data scientists deal with solving complex business problems through building models and deploying algorithms, the right kind of tools become essential to effectively manage different aspects of a project pipeline. Taking your data science project to the cloud comes with advantages like the ability to scale, access to all the latest tools, and less maintenance from the user side. Some of the most common cloud-based platforms for data science projects include Amazon Web Services, Google Cloud Platform, IBM Watson and Microsoft Azure.
IBM provides the tools for machine learning and automation to support the entire data science lifecycle, right from preparing and exploring the data to deploying and monitoring the models.
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IBM Watson Studio
It allows data scientists to build, run and manage AI models anywhere on IBM Cloud Pak for Data. It brings open-source frameworks like PyTorch, TensorFlow and scikit-learn along and its entire ecosystem of tools for code-based and visual data science. It works with JupyterLab and CLIs and is compatible with languages such as Python, R and Scala.
IBM Cloud Pak for Data
This helps collect, explore and analyse the data across any cloud with a fully integrated data and AI platform. IBM says that IBM Cloud Pak delivers a data fabric to connect and access siloed data on-premises (or across multiple clouds) without moving it. It also accelerates insights with an integrated modern cloud data warehouse.
IBM SPSS Modeler
It is a visual data science and machine learning solution that helps enterprises by accelerating time for operational tasks for data scientists. It is mainly used for data preparation and discovery, predictive analytics, model management and deployment. It also comes with IBM Cloud Pak for Data which lets one run the SPSS Modeler on the public cloud.
One of the best names when it comes to cloud-based platforms, Google Cloud is a top choice for data scientists.
Data ingestion and data preprocessing
Here, one can build data ingestion and preprocessing pipelines with Dataflow, a managed Apache Beam service. For a scalable messaging system to help ingest data, one can consider Cloud Pub/Sub, a global and horizontally scalable messaging infrastructure. To automate data movement to BigQuery, one can use BigQuery Data Transfer Service. For transferring data to Cloud Storage, Storage Transfer Service can be an option.
Data exploration and insights
Data exploration includes slicing and dicing data through data preprocessing. Google Cloud provides many ways to explore, preprocess, and uncover insights in the data. For a notebook-based end-to-end data science environment, Vertex AI Workbench is a good option that allows accessing, analysing, and visualising the entire data. It also helps undergo machine learning mechanisms with TensorFlow, PyTorch, and Spark, with built-in MLOps capabilities.
Google says, at this stage of model development, Jupyter-based fully managed, scalable, and enterprise-ready environment, Vertex AI Workbench can be of great help. Vertex AI Workbench combines analytics and machine learning as it supports frameworks such as Apache Spark, XGBoost, TensorFlow, and PyTorch. It allows to train custom models and deploy them using containers.
For low-code model development, data analysts and data scientists can use SQL with BigQuery ML to train and deploy models directly using BigQuery’s built-in serverless, autoscaling capabilities.
One can build ML models in their preferred development language and deploy the models on-cloud, at the edge with Azure AI or on-premises. Microsoft helps protect the data with differential privacy and confidential computing and control the machine learning lifecycle with audit trials and datasheets.
Azure Machine Learning
Through Azure machine learning, data scientists and developers can speed up the process with MLOps open-source interoperability and integrated tools. Microsoft says that deployment happens with a single click, and one can run ML workloads anywhere with built-in governance, security and compliance.
Microsoft also adds that Azure allows using repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). One can continuously monitor model performance metrics, detect data drift and work on retraining to improve model performance.
One can also scale reinforcement learning to compute clusters and support multiple-agent scenarios and access open-source reinforcement learning algorithms says the tech giant.
By using the SageMaker Data Wrangler’s data selection tool, one can select data from multiple data sources like Amazon Athena, Amazon Redshift, AWS Lake Formation, Amazon S3, and the Amazon SageMaker Feature Store. The user can write queries for data sources and import data directly into SageMaker from various file formats.
One can also connect to Apache Spark data processing environments that run on Amazon EMR from SageMaker Studio notebooks. Then, they can explore and visualise data and run Spark jobs using the language of their choice.
By using Amazon SageMaker Clarify, one can improve model quality through bias detection during data preparation and after training. It also provides model explainability reports to stakeholders.
The Amazon SageMaker Model Monitor automatically detects model and concept drifts. It provides alerts to figure out the source of the problem that can be worked upon to improve model quality over time. Models trained in Amazon SageMaker show key metrics that can be collected and viewed in SageMaker Studio.