What does Snowflake Acquiring Myst.AI do?

What does Snowflake Acquiring Myst.AI do?

Snowflake Acquires Myst

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The stealthy rise of Time series forecasting and A.I. of Everything.

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Hey Everyone,

So Snowflake has acquired Myst.

Snowflake Inc. is acquiring Myst AI, a startup that offers an artificial intelligence development platform for companies in the energy sector. Watching Snowflake and Databricks make acquisitions is super interesting to me.

Snowflake is incredible, even with a rout of Tech stocks in 2022, its market cap is still $40 Billion. Not many companies in the world can do this:

2023 Q3:

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When you think of BigTech companies becoming and driving ESG, this is a timely acquisition. Energy companies use AI models to predict electricity price fluctuations, changes in customer demand and other trends. San Francisco-based Myst offers a platform that makes it easier to develop AI models capable of making business forecasts. The startup’s platform also automates related tasks, such as the process of deploying neural networks in production.

Myst.AI had raised $8 million ?in funding according to Crunchbase.

Myst AI ?is a developer of the AI-based data analysis platform intended for electricity demand and supply forecasting.

Okay Montana? What! Over the last year, Snowflake has worked aggressively to expand its data cloud.?The Montana-based company has been focusing on building machine learning extensibility into the platform, and with this deal, it has taken a step in this direction.?


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As the Energy industry involves this is actually really key BigData, since as AI models generate forecasts by analyzing historical data about a phenomenon and estimating how it may change in the future. A neural network built to forecast rainfall, for example, might analyze past precipitation measurements. The AI models used in the energy sector often use similar methods.

Myst offers ?an AI platform that provides data science teams with a workflow to build, deploy and maintain highly accurate forecasting models on time series data in minutes.?VentureBeat? and not TechCrunch have covered them. Props to Siliconangle as well.

AI models are used to generate business forecasts not only in the energy sector but also other industries. Following the acquisition, Snowflake could potentially extend Myst’s platform to new use cases in additional markets.?

This feels like money well spent for Snowflake. According to the startup, its platform offers more than half a dozen prepackaged neural networks. Myst also provides?connectors for collecting time-series data?such as weather measurements. Using that time-series data, customers can train the neural networks provided by Myst to perform tasks such as predicting changes in energy demand.?

The startup’s platform also helps developers test the accuracy of new AI models before deploying them. A software team using its platform can have a new neural network estimate how a past business event unfolded. The neural network’s estimate can then be compared with historical data about the event to measure its accuracy.

If A.I. is going to help create a greener world, it appears Snowflake is going to be there and involved. The Energy sector could also represent a rather significant segment of their top clients and customers.

  • Snowflake is built on top of the Amazon Web Services, Microsoft Azure, and Google cloud infrastructure. There's no hardware or software to select, install, configure, or manage, so it's ideal for organizations that don't want to dedicate resources for setup, maintenance, and support of in-house servers.
  • Managing and storing that data has become a critical function for modern business operations. Choosing a data platform that can handle massive volumes of big data, high speeds, and reliability — not to mention the ease of use, is top of mind. Most enterprises are already using a cloud data platform, but many are evaluating whether a data migration might be needed in order to stay competitive. One of the most popular data platforms is?Snowflake .

How Snowflake and Databricks evolve is something I’m going to give special attention. Myst is the second AI startup that Snowflake has acquired in the past year. Previously, the company bought Streamlit Inc. last March in a deal?reportedly ?(TechCrunch) valued at $800 million. Streamlit provides a popular software tool that developers use to build interfaces for AI and data science applications.?

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Importance of Time Series Forecasting


According to Snowflake itself: Snowflake customers leverage the Data Cloud to bring all their data together and capitalize on the near-infinite resources of the cloud. But how can this data be used to look ahead? How can we use yesterday’s evidence to plan for tomorrow? The answer—time series forecasting.?

Time series data, also called?time-stamped information, is a sequence of data points indexed over a specific period of time. These data points consist of successive measurements made from the same source — stock price movement over the years, for instance. With this deal, which will see Myst’s team join Snowflake, the latter is expected to advance its data cloud with time-series forecasting capabilities, ultimately enabling teams to use their historical data to look ahead.?

Snowflake and Databricks are both growing at faster rates than many new Cloud Software Big Data companies.

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This makes them have a very serious trajectory for how A.I. is better integrated in the Cloud. Watching who they acquire also gives you valuable insight into how the world is changing in terms of A.I. adoption in the Cloud.

“Time series forecasting is one of the most applied data science techniques in business. It is used extensively in supply chain management, inventory planning, and finance. Accurate forecasts can establish measurements to guide management, facilitate planning and goal setting, and help mitigate risk,” Greg Czajkowski, SVP of engineering at Snowflake, said in a blog post.

As of early 2023, as far as I can tell, Snowflake currently supports eight workloads: data warehouse, data lake, data engineering, data apps, data sharing, data science, unistore and cybersecurity.

  1. Data warehouse
  2. Data lake
  3. Data engineering
  4. Data apps
  5. Data sharing
  6. Data science
  7. Unistore
  8. Cybersecurity

The Future of Time Series Forecasting is Bright


Time series forecasting is one of the most applied data science techniques in business. It is used extensively in supply chain management, inventory planning, and finance. Accurate forecasts can establish measurements to guide management, facilitate planning and goal setting, and help mitigate risk. It has practical applications across a wide-range of industries including:?

  • In healthcare, to forecast public health outcomes such as COVID-19, assess trends and interventions for people with chronic illnesses, and forecast healthcare expenditures.?
  • In manufacturing, to predict product demand at a geographic level to help meet shipping requirements and reduce inventory waste.?
  • In energy, to forecast potential spikes in electricity costs based on periods of high load volumes and scarce operating conditions.?

Developed in 2012,?Snowflake?is a fully managed?SaaS?(software as a service) that provides a single platform for?data management at scale. Snowflake raised over $2 billion and went public IPO in September 16th, 2020. It raised $3.4 billion just with the IPO. It hasn’t looked back. I give Databricks a little bit less than 50% chance of going public in 2023 and a 70% chance of going public in the next two years.

Snowflake is impressive, their growth rate is also absurd. To see their latest earnings info go?here . Here is a rare case where the hype is actually real.

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