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When you're building a product on top of an LLM, there's a moment everyone hits eventually. You've got a working prototype, it feels pretty good, and now you need to decide which model to ship with. Here's what a systematic evaluation approach looks like, and what it can reveal.

Your smartest people are stuck waiting. Not because they're lazy, but because your systems waste their time. This is Part 1 of the AI Factory Series on how Lean principles apply to scaling AI teams.

How Continental Tires deployed their first predictive ML model into production, reduced testing cycles from two months to overnight, and built a multilingual R-and-Python pipeline that survives real-world industrial complexity.

Your AlphaFold pipeline is probably held together with bash scripts and hope. Here's why that's not your fault and how to fix it.

Valohai’s enterprise-grade MLOps platform is now available on Oracle Cloud Marketplace, bringing SageMaker-like MLOps on OCI with automated pipelines, governance, and scalability for regulated industries.

Valohai's new Productivity Dashboard visualizes ML operations ROI, offering insights into cost savings, innovation acceleration, operational excellence, and governance for stakeholders.

MLflow democratized ML experiment tracking, but features that mature teams need feel like afterthoughts. When growing teams discover that what worked for 5 people breaks at 50, it might be time to consider a platform approach.

NVIDIA MONAI powers cutting-edge medical imaging research. Here’s how Valohai makes it reproducible, scalable, and production-ready — even in highly regulated healthcare environments.

NVIDIA NeMo brings state-of-the-art speech AI to the enterprise. Here’s how Valohai makes it production-ready without leaving your ML team with hand-stitched scripts, notebooks, and wishful thinking.

AWS SageMaker promises end-to-end ML workflows, but the hidden complexity often turns your data scientists into part-time DevOps engineers. Here's what that really costs your team.

Most MLOps platforms quietly couple themselves to your codebase—until one day you're debugging their wrappers instead of your model. Here's why that happens, what it costs you, and how to avoid it entirely.

ML reproducibility challenges rob teams of productivity and inflate costs through duplicate experiments. This post explores how automated reproducibility can save compute costs, accelerate development, and eliminate the frustration of recreating successful models when they matter most.

Handling ML infrastructure with MLFlow, Airflow, and Kubernetes often creates a monster that devours team productivity. Learn how knowledge silos form, why debugging becomes a nightmare, and how to escape your ML stack prison without burning everything down or disrupting your team's workflow.

Handling massive datasets can be time-consuming and error-prone. We are introducing multiple additions to Valohai designed to streamline ML workflows involving a massive number of files, from dataset creation and preprocessing to model training and data lineage tracking.

Let's take a look back at the past year. In this first part of our annual review, we'll recap all the key additions and improvements to our end-to-end MLOps platform, ecosystem integrations, and more. Stick around and you'll find out what to expect in the next year and far beyond!

Create structured and efficient workflows that help your data science team work faster and smarter, i.e., maximize the impact on the business and increase the speed of experimentation and delivery without compromising quality.

Our latest feature is built to help you make the most out of your on-prem hardware: utilize idle GPUs, adjust GPU usage for every ML job, and forget about managing priority queues. It’s live and ready for you to give it a spin (no pun intended).

Introducing an out-of-the-box solution that gives all Valohai users automatic, immutable, and secure audit logs that ensure traceability for navigating compliance requirements, debugging issues, and improving accountability within teams.

AMD's MI300X GPU can outperform Nvidia's H100 in LLM inference benchmarks, offering larger memory and higher bandwidth. Read our benchmark in full, get the details, and discover how this impacts AI hardware performance and model capabilities.

We’ve built the Model Hub to help you streamline and automate model lifecycle management. Leverage Valohai for lineage tracking, performance comparison, workflow automation, access control, regulatory compliance, and more.

Don’t miss out on Valohai’s upcoming updates on AI governance and the AI EU Act, examples of machine learning pipelines in production, new features, and GPU benchmarks. Subscribe to our newsletter.

Valohai’s new experimental feature selects compute instances based on where the data has been cached already, helping you reduce data transfer overhead and increase model iteration speed.

Our new partnership enables you to seamlessly access OVHcloud’s scalable and secure environments from the Valohai MLOps platform without changing your preferred ML workflows.

Valohai’s latest feature helps you avoid unnecessary costs by reusing the results of matching pipeline steps from previous executions. This feature is already available to all Valohai users!

We’ve built significant enhancements into our platform to further empower data science teams in accelerating time-to-market and optimizing operational costs. These enhancements tackle model iteration speed, efficient resource utilization, and dataset management.

Our new feature monitors CPU, GPU, and memory usage and alerts you when your machines operate below 50% capacity. This allows you to optimize resource usage and reduce costs.

Valohai’s Model Registry is a centralized hub for managing model lifecycle from development to production. Think of it as a single source of truth for model versions and lineage.

We designed our new Kubernetes support so that Data Science teams can effortlessly manage and scale their workflows on top of Kubernetes and enhance their overall machine-learning operations.

We're excited to announce that Valohai now supports Slurm, an open-source workload manager used in HPC environments. Valohai users can now scale their ML workflows with Slurm-based clusters with unprecedented ease and efficiency.

LLMs and other generative models make ripples everywhere from established enterprises to innovative startups, and beyond. But what did successful adoption look like in 2023? And what can we expect in 2024?

We’ve built a template for fine-tuning Mistral 7B on Valohai. Mistral is an excellent combination of size and performance, and by fine-tuning it using a technique called LoRA, we can be very cost-efficient.

We’re thrilled to announce our new free trial for all aspiring ML pioneers! With the new free trial, we’ve made it easy to kickstart your journey with our handpicked templates.

ChatGPT continues to capture the public attention and many are looking to incorporate similar functionalities in their products. But is it a safe route for production-grade applications?

We've built a set of Hugging Face templates that make it super simple to use the latest and greatest in open-source ML. These templates are available through the Valohai Ecosystem.

The key takeaways from a presentation by Andres Hernandez, Principal Data Scientist at KONUX, about how their team streamlines operations utilizing the Valohai datasets feature.

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.

Hannes is working on making voice the new touch: ubiquitous and intuitive for everyone. Together with his team Hannes is pioneering not only voice interfaces but also voice moderation problem.

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.

As a Senior Research Engineer at Valeo Cyril Poulet is working on creating a robust understanding of what is happening outside the vehicle through intelligent sensors and cameras that can detect objects, lanes, and parking spaces.

Wherever machine learning pioneers break new ground on an unforeseen scale, the curse of dimensionality lurks behind the corner. David Eriksson holds a black belt in unwrapping black boxes and compressing a wide range of large-scale models into edge devices.

Daniel Levai from UprightProject is pioneering the measurement and comparison of the net impact of companies and products using an uncompromising scientific approach. His model truly stands out due to its ability to factor in the impact of the entire global value chain.

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.

Tapio Friberg is on a bold mission to continuously monitor the entire globe. He has made it possible to have a reliable around-the-clock observation of the Earth's surface through the constellation of small and affordable SAR satellites.

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!

There is absolutely nothing wrong with notebooks, and they are fantastic for many use-cases, but they are not the only option for writing programs. Too many get stuck in the vanilla notebook and do not realize what they are missing out on.

Valohai has been mentioned as a Representative Vendor by Gartner® in the “Market Guide for DSML Engineering Platforms”

We want to talk about what distributed learning is in brief and focus more on why having this feature is a valuable tool for your business.

What started as a fun side project for our developer Magda turned out to be a proud addition to the platform. Valohai can now estimate the carbon emissions of cloud instances. Yay!

Almost any programming language in the world is more powerful than the command line. Why would you even bother doing anything on it? Don't be fooled: the modern command line is rocking like never before!

Suppose you find your projects to be in the gray area between the extremes of delayed and real-time inference where you can go with either one, ask yourself if you can delay. And if you can, you should!

There's a new wave of automation being enabled by the combination of machine learning and smart devices. With the complexity of use cases and amount of devices increasing, we'll have to adopt MLOps practices designed for IoT and edge.

Machine learning comes with new types of risk. We need to minimize the risk by addressing how we develop these algorithms and also how we apply these algorithms in the real world. In this article, we'll look at three ways of mitigating the latter – i.e. output risk.

Mike Del Balso is a familiar name to most in the machine learning community. He's one of the pioneers in the MLOps space and has laid the foundations for operational machine learning at Uber, Google, and most recently, Tecton.

Syngenta is a leading provider of agricultural science and technology focused on seed and crop protection products aiming to improve global food security by enabling millions of farmers to make better use of available resources.

Algorithms have become faster, fancier, and more complex in the past couple of years. Still, they haven't gained as much complexity as the systems around algorithms. In this article, we'll discuss three examples of systems complexity.

Docker isolates the software from all other things on the same system. A program running inside a "spacesuit" generally has no idea it is wearing one and is unaffected by anything happening outside.

People, Processes and Platforms are the foundation for every company looking to be an early-mover in machine learning. Leaders should focus on developing in tandem because unsupported team members will be ineffective and platforms alone can't provide value.

Dependency management is the act of managing all the external pieces that your project relies on. It has the risk profile of a sewage system. When it works, you don't even know it's there, but when it fails, it becomes very painful and almost impossible to ignore.

We're excited to announce Antti Karjalainen to our advisory board. He's the founder of Robocorp, a leader in developer-first RPA. To Valohai, Antti brings his unique perspective on the developer tooling space and go-to-market strategy.

A recent report by Harvard Business Review revealed that the pandemic accelerated the adoption of AI and data-driven innovation. In this article, we set out to explore the top AI trends and predict what we'll see pop in 2022.

Product management is as massive a topic as machine learning so let's start with a fundamental question. When is it worthwhile to develop an AI product? A helpful tool most PMs have seen for this is the Sweet Spot for Innovation that IDEO popularized.

Sometimes it is hard to combine the world of experimenting and the more dev-oriented world of data science with robust pipelines and modular work. This example combines Weights and Biases experiments with Valohai's production pipelines.

Git is a tool most software developers have used daily for a decade, and with data scientists becoming an integral part of R&D teams, Git is every day for them as well. We've listed a few helpful tips on using Git for your ML work and avoiding the common pitfalls.

The data you have, is, if not the most, at least close to the most valuable asset you’ve got when creating AI systems. So in practice, what can you do to embrace more data-centric AI then? We have prepared some simple steps for you to keep in mind and implement.

Observability is the collection of statistics, performance data, and metrics from every part of your ML system. Metadata, if you will. We will dig into how we can easily get started with observability and detect data drift using whylogs while executing your pipeline on Valohai.

We’ve recently introduced two features that make building trusted and validated models easier: human validation steps and confusion matrices.

When it comes to the production phase, actually providing the model to end-users and integrating it to the (existing) tools, Data Scientist often pass the baton to Software engineers. That handover is often quite rocky. Here are a few tips to how the bridge the gap between data science and engineering.

This article shows an example of a pipeline that uses Hugging Face transformers (DistilBERT) to predict the shark species based on injury descriptions. With Valohai, you can easily tie together typical data science workflows into repeatable pipelines.

Let me preface this article by saying there isn’t a single accepted definition of a machine learning lifecycle. Most articles about the machine learning lifecycle tend to focus only on a small portion of the actual lifecycle: the Experimentation loop.

For the October product update, we chose to highlight a new feature, Remote Access Debugger, and some major improvements that we've shipped to the Metadata View.

No-code is only no-code for the end user, and that is also true for no-code AI. These platforms rely on the ingenuity of developers to abstract away the technical parts. MLOps is vital to deliver the product reliably and without risk.

DLOps, deep learning operations, is an evolution of MLOps, looking to answer the unique operational challenges that deep learning sets. A skeptic may look at it as unnecessarily muddying the waters with a new buzzword.

Support for Spark has been one of the most requested features as Spark has become almost ubiquitous for data scientists and engineers working with structured data. We’ve heard the calls and Valohai now supports Spark natively.

One of the unique aspects of Valohai is that despite being a proprietary platform it can run in fully private, even airgapped, environments. Why is this important? Machine learning often revolves around data that is sensitive and thus data security is a fundamental requirement.

The AIIA blueprint is an excellent starting resource for teams looking to implement their stack for machine learning development. The initiative draws inspiration from other popularized tech stacks.

Datum is a version-controlled file inside the Valohai platform. Every datum is immutable by design. We have introduced three new improvements for more flexibility over datums.

Putting together a suitable dataset for training a model can be one of the biggest challenges. Data augmentation is an approach where you start with an existing dataset and expand it to have more variety.

Let’s start by defining interpretability in the context of machine learning and AI. In simple terms, it means how easily a human can interpret how the model arrived at a decision.

Summer is here and hopefully, for most of us, it means time to decompress. But if you are like me and learning is relaxing, podcasts are a great way to enjoy the summer weather while learning.

This article shows an example of a pipeline that integrates Valohai and Superb AI to train a computer vision model using pre-trained weights and transfer learning. For the model, we are using YOLOv3, which is built for real-time object detection.

It's time for an update on what's been happening under the hood of the Valohai platform. We'd like to highlight three major features we've added in the past two months: Support for Kubernetes and Spot instances and the Valohai Python utility library.

Why a Machine Learning model is not a product if there is no MLOps. Our approach to implementing training as a reproducible process, and how this process intertwines with our CI/CD pipeline.

It's becoming more important to think about the competencies of a team rather than expecting every individual to be an expert at everything related to machine learning.

Computer vision is one of the most disruptive technologies of the recent decade. To develop computer vision systems requires massive, upfront investments. Or it used to, before Superb met Valohai.

How can MLOps make consultant-client relationships more productive? Starting with machine learning is a massive, strategic undertaking, and many are turning to consultancies and contractors to take the first steps with AI.

March 16th, we held a webinar to follow up on our MLOps eBook. Together with our co-authors, we wanted to tackle the goal we set for MLOps in the eBook: “The goal of MLOps is to reduce technical friction to get the model from an idea into production in the shortest possible time to market with as little risk as possible.”

In the past few months, we've rolled out three new features that highlight end-to-end automation on our platform: Deployment nodes in pipelines, Pipeline scheduler & Model monitoring.

If you are involved with production machine learning in any way, understanding MLOps is essential. For people with software development experience, the easiest way to understand MLOps is to draw a parallel between it and DevOps.

Valohai MLOps platform provided the infrastructure for the Black-Box Optimization Challenge for the NeurIPS 2020 conference. The competition was organized together with Twitter, Facebook, SigOpt, ChaLearn, and 4paradigm.

The bus factor is a common term in software engineering describing the risk of a key contributor disappearing unexpectedly from a project – because they get hit by a bus. In machine learning the bus factor is magnified significantly.

Should a machine learning model be retrained each time new observations are available (or otherwise very frequently)? The answer is “it depends”, but this article looks at two components to consider: the use case and the costs.

Buying an MLOps platform is tricky and for that reason we’ve introduced a model where teams can sign up for a two-week proof-of-concept project to test out our platform with their environment and projects.

As you start incorporating machine learning models into your end-user applications, the question comes up: “When is the model good enough to deploy?” There simply is no single right answer.

Modern tooling and shared work methods (CI/CD, version control, microservices) have enabled companies to scale their throughput in software development exponentially. A machine learning pipeline brings similar scale to machine learning.

To make it easier to consider what tools your organization could use to adopt MLOps, we’ve made a simple template that breaks down a machine learning workflow into components.

Machine learning and artificial intelligence allow businesses to gain new insights and improve their business processes. However, they expose companies to additional risks because humans do not explicitly program the algorithms. Let's look at some of these risks and how data scientists and compliance officers can help mitigate them.

Using the MLOps platform allows you to manage everything about machine learning in production, where each new update doesn’t feel like an entirely new project and easily dovetails to the last.

To make an analogy to a more traditional industry, machine learning is shipping goods while MLOps is containerization. And much like containerization of global shipping, MLOps is equal parts process and infrastructure.

For a long time, most machine learning initiatives have been stuck in a persistent state of proofs-of-concept. However, in the past year, we’ve seen a rapid acceleration of machine learning models getting real-world use. Consequently, machine learning engineers are increasingly sought after – nearly catching up to data scientists in posted jobs.

Valohai, the MLOps platform company, is collaborating with Twitter and Facebook to launch a competition for the annual The Neural Information Processing Systems (NeurIPS) conference to advance the optimization of machine learning models towards more accurate AI solutions. The goal is to find better optimization algorithms for machine learning.

Most software development teams have adopted continuous integration and delivery (CI/CD) to iterate faster. However, a machine learning model depends not only on the code but also the data and hyperparameters. Releasing a new machine learning model in production is more complex than traditional software development.

Grid search and random search are the most well-known in hyperparameter tuning. They are also both first-class citizens inside the Valohai platform. You define your search space, hit go, and Valohai will start all your machines. It does a search over the designated area of parameters you’ve defined. It is all automatic and doesn’t make you launch or shut down machines by hand. Also, you don't accidentally leave machines running costing you money. But we’ve been missing one central way for hyperparameter tuning, Bayesian optimization. Not anymore!

Finding the right subreddit to submit your post can be tricky, especially for people new to Reddit. There are thousands of active subreddits with overlapping content. If it is no easy task for a human, I didn’t expect it to be easier for a machine. Currently, redditors can ask for suitable subreddits in a special subreddit: r/findareddit.

A lot of companies and teams are going fully remote for the first time due to the Coronavirus. We at Valohai are big believers in remote work. Having practiced with a distributed team for a good 4 years we would like to share some of our thoughts on remote work in Machine Learning. A lot of major pain points we have seen revolve around tooling.

In this blog post we will explore how you can use DVC for your data version control and how you can automate your data version control with and without DVC inside the Valohai platform.

It’s a running joke among developers that the cloud is just a word for somebody else’s computer. But the fact remains, that by leveraging the cloud you can reap benefits that you couldn’t achieve with your on-premises server farm.

Machine learning (ML) platforms take many forms and usually solve only one or a few parts of the ML problem space. So how do you make sense of the different platforms that all call themselves ML platforms?

When doing machine learning in production, the choice of the model is just one of the many important criteria. Equally important are the definition of the problem, gathering high-quality data and the architecture of the machine learning pipeline.

This article is the story of us at Selko.io, productionizing our machine learning workflows. We'll describe Selko's route from starting the company to developing our first ML models. We'll also walk through how we built a fully working machine learning solution combining our UI, backend, and orchestration layer for machine learning tasks. And of course, how we went from a homegrown ML orchestration platform to Valohai. To give you some context, let's first dive into the history of the company.

One of the key challenges for a Data Science team is the search for an accurately labelled dataset for solving the given problem. While it is easy to build a basic model that is reasonably accurate for a demo to the business, going beyond it towards a production worthy solution needs gold standard ground truth data.

Apache Airflow is a popular platform to create, schedule and monitor workflows in Python. It has more than 15k stars on Github and it’s used by data engineers at companies like Twitter, Airbnb and Spotify.

After looking at a lot of Java/JVM based NLP libraries listed on Awesome AI/ML/DL I decided to pick the Apache OpenNLP library. One of the reasons comes from the fact another developer (who had a look at it previously) recommended it. Besides, it’s an Apache project, they have been great supporters of F/OSS Java projects for the last two decades or so. It also goes without saying that Apache OpenNLP is backed by the Apache 2.0 license.

Only the companies that invest into machine learning today will exist 10 years from now. The ones that look to the sidelines will be eaten by their competition.

Continuous Integration (CI) in software development is the process of testing that a change in one place doesn’t break something else. Continuous Delivery (CD), on the other hand, is an extension to CI where every change in the code is also deployed. Both are and have been core parts in the advancements of Extreme Programming, i.e. rapid small-batch development. This, on its hand, has been the main contributor to advancements in rapid software development.

Valohai is the enterprise-grade machine learning platform for data scientists that build custom models by hand. In addition to writing code with classic IDEs like PyCharm or VSCode, we also have native support for data scientists preferring to use Jupyter notebooks.

We are all aware of Machine Learning tools and cloud services that work via the browser and give us an interface we can use to perform our day-to-day data analysis, model training, and evaluation, and other tasks to various degrees of efficiencies.

They say data is the new gold. But without a data catalog, your data is just scattered around like random nuggets of gold in a desert full of rocks, pebbles and sand. Data catalogs help you keep track of the data you have but also, in the case of machine learning models, what data has affected which model. Data brings meaning to machine learning because unlike software, machine learning models are 90% data and 10% code.

One of the more exciting things we have under development (or, should we say, in the pipeline) right now is our Pipeline system. Since our mission is to enable CI/CD style development for AI and machine learning, there's a logical next step up from just (well, "just" might be the understatement of the year here) running your code in a repeatable manner with Valohai.

One of the hottest areas of application for deep learning is undoubtedly self-driving cars. We’ll go through the problem space, discuss its intricacies and build a self-driving solution utilizing the Unity game engine, training a neural network on top of the Valohai platform. Regardless of the technologies used, you’ll get an understanding of the basics as well as the code to tweak for yourself.

We all understand the importance of reproducibility of machine learning experiments. And we all understand that the basis for reproducibility is tracking every experiment, either manually in a spreadsheet or automatically through a platform such as Valohai. What you can’t track what you’ve done it’s impossible to remember what you did last week, not to mention last year. This complexity is further multiplied with every new team member that joins your company.

Some time ago I came across this life-cycle management tool (or cloud service) called Valohai and I was quite impressed by its user-interface and simplicity of design and layout. Previous to that I had written a simple pipeline using GNU Parallel, JavaScript, Python and Bash - and another one purely using GNU Parallel, and Bash.

Software patents raised a lot of hairs twenty years ago, mainly because while governments are slow to react to change, software evolves rapidly, and patents thus live on for too long in comparison to hardware. Let’s in this blog post take a look at how AI patents are similar and different from software patents and what challenges can be seen in AI patenting.

Production-grade machine-learning algorithms never come out perfect on the first try. They require the same approach to iteration and testing as any other software project. But validating machine-learning algorithms is particularly hard—harder than writing simple unit or integration tests. And iterating on machine-learning algorithms gets harder as the team contributing to it grows.

All over the world, patents are known as the best way to protect inventions. They provide inventors with a period of up to 20 years to use an exclusive, monopoly-like position in the commercial exploitation of their creations. It is the key for getting returns on the investments they made during the research and development of their new technological solutions.

I see the quote “AI is the new electricity” thrown around in about every other blog post. I think there is truth in it, but I also think most people don’t go to the bottom of what it really means for their business. Let’s first define what we mean by AI: in this context, I’m referring to new advances in machine learning and deep learning.

Valohai is a deep learning platform that helps you execute on-demand experiments in the cloud with full version control. Jupyter Notebook is a popular IDE for the data scientist. It is especially suited for early data exploration and prototyping.

Pointlessly staring at live logs and waiting for a miracle to happen is a huge time sink for data scientists everywhere. Instead, one should strive for an asynchronous workflow. In this article, we define asynchronous workflows, figure out some of the obstacles and finally guide you to a next article to look at a real-life example in action in Jupyter Notebooks.

Valohai executions can be triggered directly from the CLI and let you roll up your sleeves and fine-tune your options a bit more hands-on than our web-based UI. In [part one](/blog/from-zero-to-hero-with-valohai-cli), I showed you how to install and get started with Valohai’s command-line interface (CLI). Now, it’s time to take a deeper dive and power up with features that’ll take your daily productivity to new heights.

Ville Tuulos, machine learning infrastructure architect, was the first to publicly dissect Netflix’s Machine Learning infrastructure at QCon in November 2018 in San Francisco. If you haven’t seen the talk yet, read the summary of his talk here! All the pictures used here, are from Ville's presentation.

In our series of machine learning infrastructure blog posts, we recently featured Uber’s Michelangelo. Today we’re happy to be interviewing Ville Tuulos from Netflix. Ville is a machine learning infrastructure architect at Netflix’s Los Gatos, CA office.

As new Valohai users get acquainted with the platform, many fall in love our web-based UI - and for good reason. Its responsive, intuitive and gets the job done with just a few clicks. But don’t be fooled into thinking that’s the end of the interface conversation. We know it takes different \[key\]strokes for different folks, so Valohai also includes a command-line interface (CLI) and the REST API.

One of the core design paradigms of Valohai is technology agnosticism. Building on top of the file system and in our case Docker means that we support running very different kinds of applications, scripts, languages and frameworks on top of Valohai. This means most systems are Valohai-ready because of these common abstractions. The same is true for TensorBoard as well.

Running a local notebook is great for early data exploration and model tinkering, there’s no doubt about it. But eventually you’ll outgrow it and want to scale up and train the model in the cloud with easy parallel executions, full version control and robust deployment. (Letting you reproduce your experiments and share them with team members at any time.)

SwiftStack and Valohai, in joint partnership, announce the world’s first peta-scale ML solution that covers everything from computation to data management in a multi-cloud environment. The solution provides a global namespace removing silos and enabling universal access to all your data in all your machine learning use-cases. It has built-in support for Azure, Google Cloud, AWS and SwiftStack.

We may live in the era of “Big Data,” and yet the access to it is somewhat restricted; especially, when we talk about high-quality data. This blogpost will address the question of acquiring data for your Machine Learning projects from the perspective of EU and US copyright laws.

In this third part, we will move our Q-learning approach from a Q-table to a deep neural net.

When we founded Valohai two years ago, we were lucky to make friends with team leads for Uber’s Michelangelo machine learning platform. Michelangelo has been an inspiration in building Valohai for the other 99.999...% of companies that aren’t Uber but still need to speed up their machine learning through automation.

By now you’ve surely heard about Kubeflow, the machine learning platform based out of Google. Kubeflow basically connects TensorFlow’s ML model building with Kubernetes’ scalable infrastructure (thus the name Kube and Flow) so that you can concentrate on building your predictive model logic, without having to worry about the underlying infrastructure. At least in theory.

In this second part takes these examples, turns them into Python code and trains them in the cloud, using the Valohai deep learning management platform.

There’s only one way to grow your deep learning team effectively: by adding new people to it! (We were just as shocked as you are by this revelation!)

This is the first part of a tutorial series about reinforcement learning. We will start with some theory and then move on to more practical things in the next part. During this series, you will not only learn how to train your model, but also what is the best workflow for training it in the cloud with full version control using the Valohai deep learning management platform.

This tutorial will demonstrate how to take a single cell in a local Jupyter Notebook and run it in the cloud, using the Valohai platform and its command-line client (CLI).

Since the rise of the deep learning revolution, springboarded by the Krizhevsky et al. 2012 ImageNet victory, people have thought that data, processing power and data scientists were the three key ingredients to building AI solutions. The companies with the largest datasets, the most GPUs to train neural networks on, and the smartest data scientists were going to dominate forever.

Valohai now supports random search for hyperparameter optimization (which we call the Tasks feature), which has been proven in the aptly named paper Random search for hyper-parameter optimization to be an efficient way to find “neighborhoods” of likely-to-be-optimal hyperparameter values, which can then be iterated further to find the really good values.

Watch a recording of the webinar on version control in machine learning that was held on 22th of November 2018. During the webinar we discussed about the topics below and answered multiple questions addressed by the attendees.

PocketFlow is an open-source framework from Tencent to automatically compress and optimize deep learning models. Especially edge devices such as mobile phones or IoT devices can be very limited on computing resources so sacrificing a bit of model performance for a much smaller memory footprint and lower computational requirements is a smart tradeoff.

Microsoft's Cognitive Toolkit or CNTK is an open source framework for building Deep Learning models. This relatively new framework has been gaining traction so we decided to make sure Valohai supports it well. One of the benefits over competing frameworks has been CNTK’s ground up support for multi-node, multi-GPU training, something that for instance TensorFlow has been struggling to tackle well. If you are doing work on really large datasets, you should maybe give it a try.

Synthetic data is artificially created information rather than recorded from real-world events. A simple example would be generating a user profile for John Doe rather than using an actual user profile. This way you can theoretically generate vast amounts of training data for deep learning models and with infinite possibilities.

You might have heard that every individual subject to automated decision making by machine learning models has a right to an explanation of the result. I bet you feel drops of sweat forming on your forehead when you receive an inquiry from a manager saying that he needs details about how a certain decision was made. If thinking about this scenario gives you chills, you are in the right place. Read further and learn how to tackle the transparency issue.

When meeting with teams that are working with machine learning today, there is one point above everything else that I try to teach. It is the importance of storing and versioning of machine learning experiments and especially how many things there actually are that need to be stored.

Recreating experiments inside Valohai could be a whole lot easier and we’ve heard your cries!

All of us have seen those fear mongering headlines about how artificial intelligence is going to steal our jobs and how we should be very careful with biased AI algorithms. Bias means that the algorithm favors certain groups of people or otherwise guides decisions towards an unfair outcome. Bias can mean giving a raise only to white male employees, increasing criminal risk factors of certain ethnic groups and filling your news feed only with topics and point of views that you are currently consuming – instead of giving a broad, balanced view of the world and educating you.

Valohai and Microsoft cross lightsabers in the battle for artificial intelligence, through Microsoft’s global ScaleUp Program.

Just lately we’ve been playing around with IBM PowerAI in order to ensure our customers can leverage it in large-scale on-premise training. PowerAI in itself is IBM’s solution for deep learning consisting of software and hardware to help you quickly train deep learning models. Today we’re happy to announce that Valohai fully supports PowerAI and our customers can start using it!

Valohai is turning 2 years old in three weeks. The paperwork was done on October 16th, 2016. It’s been a thrilling ride so I’ll take this chance to write a few words about why we really started this company.

Whitesnake cover bands of the 2020s. Although both might be sporting the same hobo beards, Data Scientists are getting their work done with just sticks and stones as their tools while us Software Engineers have every tool in the universe.

Developing a machine learning model for a new project starts with certain common groundwork and exploration, to understand your data and figure out the approaches to try. A popular choice for this groundwork is Jupyter, an environment where you write Python code interactively.

Reproducibility and replicability are cornerstones of the scientific method. Every so often there’s a sensationalized news article about a new scientific study with astounding results (for instance, we’re looking forward to seeing what’s hot at ICML 2018.

If machine learning is a team sport, like I so frequently hear, machine learning platforms must be the playing fields. And to up your machine learning game, you must have the proper environments to do it.

Machine learning infrastructure is one of the biggest things to concentrate on when building production-level machine learning models. Find all you need to know about what machine learning infrastructure is and why it is so important.

Today’s machine learning teams consist of people with different skill sets. There are a bunch of different roles that are needed, but today I am going to talk about the two key roles that I get asked about the most: machine learning researcher / data scientist vs. machine learning engineer.

Smart recommendation in apps and websites is not an additional feature that differentiates top industries from others. Most users take for granted that they will be suggested products that they like. Collaborative filtering has been widely used to predict the interests of a user by collecting preference and tastes information from many users. It is often combined with content-based filtering, especially for tackling the cold-start problem.

In the age of technology, conventional methods are being automated, and computers are taking over. Similarly, for energy distribution, smart grids are replacing traditional energy distribution grids which allow efficient distribution and demand-side management.

Valohai, a machine learning (ML) platform-as-a-service company, has raised $1.8M in funding to help international companies accelerate machine learning development and scale their model deployment. The round was led by Nordic seed stage investment company Superhero Capital, with participation from Reaktor Ventures and Business Finland, the Finnish Funding Agency for Innovation.