Data Engineering for Machine Learning

Data Infrastructure for Machine Learning

For When You Can't Afford an Outage

Data engineering for machine learning includes data streaming, storage, and search. These components lay the groundwork for successful ML applications. Our approach to data infrastructure provides high performing, fault tolerant, fully managed Streaming, Storage, and Search for data science, ML, and AI companies.

Data Engineering by Dattell ➔

Improve Performance, Decrease Costs

We’ve saved our clients over $200M on their data infrastructure.

Software Costs.
Moving to free Elasticsearch or migrating to Opensearch are both trusted alternatives to paid software that are completely free. This alone saves many clients $1M or more per year.

Hardware costs.
Another example is poorly written queries. A bad query can use 10,000% more hardware.

Business Losses.
Machine learning models require that data infrastructure be online and collecting 100% of incoming data at all times to avoid missed data. Expertise is needed to ensure a reliable data infrastructure.

Improve Performance, Decrease Costs

We’ve saved our clients over $200M on their data infrastructure.

Hardware costs.
Another example is poorly written queries. A bad query can use 10,000% more hardware.

Components of a ML Data Pipeline

Machine learning requires that all data is received, clean, and quickly retrievable. To ensure these needs are met a company’s data infrastructure should have the following:

24x7 monitoring & on-call
Fault tolerant system to handle spikes in data
Data delivery semantics, for instance exactly once or at least once delivery
Custom optimization of your data infrastructure
Cross-cluster replication to prevent data loss and decrease latency

Real-Time Data Streaming for Machine Learning

Data collection and ingestion involve gathering data from multiple sources. These sources can include databases, unstructured data from documents, or real-time data streams from a variety of devices or applications.

A well-designed ingestion system can handle diverse data formats, manage high volumes, and allow data engineers to implement pre-processing and filtering techniques to remove irrelevant data early in the pipeline.

For data collection we use either Apache Kafka or Apache Pulsar, depending on a client’s needs. Both of these software are widely used and extensively tested. They are both also free and open source. In other words, our clients do not pay any license fees to use these platforms.

Additionally, we guarantee 99.99% uptime for data streaming in production. Our clients trust in us to guarantee 100% of their data is collected to ensure their machine learning models work appropriately.

Data Storage and Search for Machine Learning

Data storage is where raw and processed data is kept for further use. We use either Elasticsearch or OpenSearch, depending on each client’s individual needs and preferences. Both Elasticsearch and OpenSearch are horizontally scalable to handle large workloads and spikes in data. For instance, some of our clients are ingesting terabytes of data each day.

We also employ hot, warm, and cold storage classifications to suit each use case.

Additionally, both of these software are search engines. For our clients we emphasize smart categorization of data and fast retrieval.

Real-Time Monitoring and Alerting for Artificial Intelligence

Continuous monitoring of data infrastructure is critical to identify emerging issues. This early detection allows emerging issues to be resolved before causing a performance issue.

Security and Privacy Management for Data Science

Data used for machine learning often contains sensitive information, requiring strict security and privacy measures. Data engineering teams implement data encryption, role-based access controls, and secure data storage solutions to protect against unauthorized access. Compliance with regulations such as HIPAA, PCI, GDPR or CCPA is critical, especially for companies handling customer data.

Expert Management of Data Engineering Pipelines for AI

Data engineering workflows require expertise to be set up and managed correctly. Misconfigurations, inadequate scaling, and inefficient workflows can lead to costly errors and downtime. For many businesses, working with an external expert company for data infrastructure management is a smart choice. Here’s why:

Cost Savings.
Poorly architected data pipelines can lead to inefficiencies and unexpected costs. Expert data engineering companies, like Dattell, can optimize the pipeline, improving performance and reducing infrastructure costs. For instance, a single bad query can result in 10,000% more hardware usage.

Time Efficiency.
An experienced team can set up and manage complex data pipelines faster than an internal team learning on the job. This frees up time for businesses to focus on their core activities and lets data scientists concentrate on building models rather than struggling with infrastructure.

Scalability and Reliability.
Scaling data pipelines to handle increasing data loads requires deep expertise. We ensure our clients’ systems are scalable, reliable, and capable of handling growth as data needs expand. See our SLA overview below.

Security and Compliance.
We are well-versed in best practices and compliance standards. This ensures that your data engineering pipelines meet regulatory requirements.

24x7 Data Engineering Support & Consulting

Visit our OpenSearch page for more details on our support services.

Data Engineering for Machine Learning