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Collecting, cleaning and labeling data is one of the most time-consuming problems in data science and this is especially true in NLP. Recently, we've seen data scientists utilize large language models such as OpenAI's GPT-4 to help produce datasets to train smaller NLP models that solve a more specific task.

With the popularization of LLM's developers and product folks are flocking to the space and testing out novel concepts. How will LLM products evolve over time?

In 2020, Forbes estimated the market of MLOps solutions is expected to reach $4 billion by 2025. The recent Venture Beats article claims it will grow to over $6 billion by 2028. Let's look at what is the driving force for the demand of MLOps.

LLMOps focuses on the operational capabilities and infrastructure required to fine-tune existing foundational models and deploying these refined models as part of a product.

The Valohai Ecosystem is a library of templates that enable users to kick off their projects with ease and reduce the amount of boilerplate code that needs to be written.

The pioneers' journey will be full of sidestepping, u-turns, and zigzags. No organization can exactly know where they'll end up, but we will help you to get there. Valohai developer core will keep all paths open, just in case you need to change course.

With Valohai, rapid experimentation, massive grid searches, complex multi-cloud pipelines, distributed learning clusters, and model deployment are all a single click (UI), a single command (CLI), or a single request (API) away and handled by the battle-hardened orchestration system.

Valohai integrates into your code repository, container repository, data storage, and compute resources - in the cloud and on-premise - orchestrating and recording their complex interplay. This way, the bookkeeping is fully ingrained into the machine learning workflow and something the pioneers don’t even need to think about conscientiously.

As of September 29, 2022, Valohai is officially SOC 2 Type II compliant. SOC 2 compliance "demonstrates your organization's ability to effectively safeguard the privacy and security of customer data".

We are renewing our commitment to helping ML Pioneers. Our focus has always been on supporting people working on the next wave of ML and we’ve been working hard to turn that focus into words and visuals. We want Valohai to be as bold as the ML Pioneers who rely on us.

Web scraping and data gathering are vast topics. There is no single correct way to programmatically collect data from sources designed for human consumption. The right approach for web scraping depends on the context, and in this article, we focus on an early-stage ML project needing time series data.

Michael Vakulenko of JFrog and Juha Kiili of Valohai showcase the real-life example of creating a machine learning system that continuously improves itself through IoT.

What if I told you there is a simple, free, lightweight tool for weaponizing any CLI-based ML project. All the commands are nicely wrapped and accessible via shortcut aliases and only a TAB keypress away. This tool is easily installable and super robust for all operating systems! It is called Make.

We look at industries with the highest need for artificial intelligence solutions in 2022, why they need it, and gives example use-cases.

Here are some tips and tricks for Jupyter notebook with step-by-step guides: from running shell commands to changing the notebook theme - easily!