Toloka recognized in 5 stages of the ML Value Chain Landscape by TheSequence

What is the ML Value Chain Landscape?

TheSequence recently released the ML Value Chain Landscape, a comprehensive map of relevant solutions divided into 6 stages of the ML development process: data collection, data processing, data annotation, ML model training & evaluation, ML model deployment, and model monitoring. The Sequence provides substack publications helping 144,000 enthusiasts to keep up with the fast-moving ML world. What makes the Landscape unique? We see three things that make this publication stand out:

• A novel map for the ML value chain makes the landscape easy to navigate and apply to real life.

• The Landscape was shaped by TheSequence community of ML engineers and data scientists. All readers were invited to answer questions and contribute.

• The map identifies each company or vendor by how many stages of ML development it covers, giving you the big picture.

Putting Toloka on the ML map

We are thrilled to share that Toloka was recognized by TheSequence community as a representative vendor in 5 out of 6 stages of the ML value chain: data collection, data annotation, ML model training & evaluation, ML model deployment, and model monitoring. The Toloka environment has evolved to support fast and scalable AI/ML development with a data labeling platform, pre-trained adaptive ML models, and an ML management platform.

TheSequence has given us permission to share the landscape and our insights with you.

Insights

Here's what we discovered after analyzing the content from TheSequence:

#1 There are about 40 companies for each stage of the ML lifecycle. This doesn't mean that all of them provide comprehensive solutions for every step involved in a particular stage — only that they offer something related to the stage.

#2 There is not a single company that has solutions and products for every stage of the ML lifecycle. However, there are a few that come close. Abacus.AI, Appen, Vertex AI, Scale, and Toloka have the most extensive portfolios, covering 5 out of 6 stages.

#3 Each stage has its own hidden issues. Model monitoring is the least covered stage because most solutions are not well optimized and it requires manual effort. Data processing has so many steps that it can hardly be considered a single stage. The key pain points for ML development are mostly related to integrating different solutions, transitioning between stages, and user friendliness.

Check out the Landscape for details!