Faros AI zbere 16 milijonov dolarjev, da osvetli produktivnost razvijalcev, lansira brezplačno odprtokodno platformo

Vitaly Gordon je ustanovil Salesforce Einstein v kleti s 5 ljudmi leta 2016. Ni trajalo predolgo, da je prerasel v nedvoumen uspeh za Salesforce: izboljšanje notranjih operacij podjetja, ki ga uporablja več kot 10 strank, ustvarjanje več kot 10 milijard napovedi vsak dan, tako dobro, kot vrhunske raziskave, na katerem dela na stotine ljudi.

Zakaj torej Gordon ne uživa sadov svojega dela pri Salesforce?

Ker, kot je rekel, niso izvajali tega, kar pridigajo. Gordon je spoznal, da inženirske ekipe v organizacijah sploh ne temeljijo na podatkih, kot bi morale biti. Pustil je svojo vlogo podpredsednika, Data Science and Engineering pri Salesforce Einstein in se skupaj z nekaterimi svojimi nekdanjimi sodelavci lotil prizadevanja, da bi programsko inženirstvo temeljilo na podatkih.

Faros AI je podjetje, ki ga je Gordon soustanovil leta 2019, da bi inženirskim ekipam zagotovil globok vpogled v njihovo delovanje, tako da lahko izdelke pošiljajo hitreje. Platformo Faros Engineering Operations Platform že uporabljajo Box, Coursera in GoFundMe.

Faros AI je danes objavil, da je zbral 16 milijonov dolarjev začetnega financiranja, ki ga vodijo SignalFire, Salesforce Ventures in Global Founders Capital s sodelovanjem izkušenih tehnoloških svetil, vključno z Maynardom Webbom, Fredericom Kerrestom, Adamom Grossom in drugimi.

What’s more, the company is also announcing the general availability of its free open-source Community Edition, Faros CE. We caught up with Gordon to discuss his journey with Faros AI, the philosophy of what they call EngOps, and the making of the Faros AI platform.

Analitika kot svetilnik ekip programskega inženiringa

Faros je grščina za svetilnik. Kot je opozoril Gordon, so analogije, ki jih navdihuje morje, močne v infrastrukturnem prostoru. Začelo se je z Dockerjem, nato pa je prišel Kubernetes, kar v grščini pomeni pomorski kapitan. Torej če Kubernetes je krmar, ki usmerja ladjo, kaj kaže pot? To bi bil svetilnik in Faros AI želi biti svetilnik.

Gordon se nanaša na to, kar počne Faros EngOps. If you’re familiar with DevOps, you may think that EngOps is similar — but it’s not. In reality, what Faros AI does can be summarized as analytics for software engineering teams. The reason Faros is using the term EngOps, Gordon said, is a nod to other disciplines.

Če pogledamo vloge, kot so prodajne operacije, tržne operacije ali zaposlovalne operacije, ugotovimo, da jih opravljajo zelo analitični ljudje. Njihova naloga je pridobiti podatke iz več virov, analizirati cevovode, poiskati ozka grla, nato pa poročati ustreznim vodstvenim delavcem in skupaj z njimi izboljšati tisto, kar je treba izboljšati.

Faros AI je zgrajen na ideji oznanjevanja te vrste vloge programskega inženiringa. Gordon verjame, da bi moralo vsako podjetje imeti ljudi, ki analizirajo podatke in svetujejo vodjem inženirjev pri dodeljevanju virov in sprejemanju odločitev.

You would think that with software engineering being entirely digital, with established practices and systems used, using analytics for this would have occurred to someone, and it would have been implemented already. Conceptually, it’s pretty straightforward, and Faros AI describes it using the Connect — Analyze — Customize triptych.

Prvič, vsi sistemi, pomembni za proces razvoja programske opreme, morajo biti povezani, tako da je mogoče zaužiti njihove podatke. Faros uporabnikom omogoča povezovanje sistemov, kot so repozitoriji kod, CI / CD, programsko opremo za upravljanje vozovnic in vodenje projektov v en centraliziran sistem evidence.

That is a prerequisite to being able to do analytics. It’s also not as simple as it sounds. Beyond getting the connectors in place, the data has to be integrated and aligned, and Gordon said it takes “some kind of intelligence” to stitch all those different data sources together. The goal is to trace changes from idea to production and beyond, incidents from discovery to recovery to resolution, and reconcile identities across the different systems.

Then comes the analysis, which is the core of the process. In Gordon’s experience, the metrics that are often used to measure developer productivity, such as lines of code or ticketing story points, may be easy to measure, but they are not really representative. If anything, Gordon said, there may be a reverse correlation between those metrics and the actual value generated.

Gordon in njegovi soustanovitelji so iskali visoko in nizko, da bi prišli do tega, za kar trdi, da lahko postane de facto nabor meritev za programsko inženirstvo. Nanje so se začeli močno zanašati DORA – Google Cloud’s DevOps Research and Assessment.

DORA studied over 1000 companies and measured over 100 metrics, using them to classify teams in 4 buckets — Elite, High, Medium and Low. They did that, Gordon said, based on metrics that focus on process and not people, measuring outcomes rather than outputs. This is the philosophy that Faros AI embraces as well.

Nenazadnje prilagajanje omogoča uporabnikom Faros AI, da natančneje prilagodijo meritve svojim potrebam in okolju. Ker se organizacije razlikujejo po načinu dela in okoljih, ki jih uporabljajo, je to nujna določba za zagotovitev, da platforma dobro deluje za vsak scenarij in da zbrane meritve odražajo realnost na terenu.

Merjenje in maksimiranje vrednosti

All that sounds fine and well, but how does it translate to tangible benefits in practice? To address this question, Gordon started by saying that just being able to see everything in one place is oftentimes enough to generate an “aha moment”. But it goes beyond that; he went on to add. One crucial aspect Faros AI has been able to help customers with is resource allocation:

“One of the things that we keep hearing from our customers, and it comes a lot from high-level management, or even sometimes the board, is: We hire more engineers, but we don’t seem to get more things done. Why is that? Especially in an environment where it’s so hard to hire more engineers, why don’t we see results?

One of the things we showed them is that if your bottleneck is not on engineers writing code, but in quality assurance, and you don’t have enough people there, then hiring more engineers to write more features will actually make things slower, not faster”, Gordon said.

Ko so organizacije to spoznale, so se odzvale tako, da so spremenile svoje načrte zaposlovanja, da bi odpravile ta ozka grla, kar je pomenilo veliko razliko. Prerazporeditev obstoječe delovne sile za reševanje težav v cevovodu programskega inženiringa, namesto da bi zaposlili več ljudi, lahko povzroči zaposlitev 20 % več inženirjev, kot pravi Gordon.

The value comes not just from delivering software faster but also from improving software quality and minimizing downtime, Gordon went on to add. According to Google’s research, savings can be anywhere between $6 million and $250 million per year, depending on team size.

Faros AI is aimed at engineering team leads, CTOs and similar roles. While Gordon made a case for the value it can deliver to them; we wondered how the product is received by engineering team members, whose work is spotlighted. Experience with Faros AI customers shows that employee satisfaction goes up, Gordon said. That is because it reduces “internal bureaucracy”, resulting in a faster turnaround and having engineers see the impact of their work in the real world.

If talking about things such as software quality and value generated whets your appetite, you will have to manage your expectations. Trying to attribute the work of engineering teams to high-level business metrics is the holy grail for EngOps, Gordon said, but we’re not there yet.

The closest we can get at this point, he went on to add, is measuring how long it takes to get something to production. Given how engineering environments and systems sprawl, that’s not trivial. In Gordon’s experience, the Connect – Analyze – Customize cycle is something that many organizations do, under names such as produktivnost razvijalcev, inženirska učinkovitost ali inženirska opolnomočenost.

Most of that work is completely undifferentiated, and it’s about infrastructure building. The thinking is that just like it makes sense for most organizations to use an off-the-shelf ERP or CRM system and customize it to their needs, EngOps should be no different.

For Gordon, Faros AI’s mission is to bring EngOps to as many organizations as possible. The release of Faros CE, the free, open-source Community Edition of the Faros AI platform, is an important step serving that goal. There are no real differences in capabilities between Faros CE and Faros AI Enterprise, except when it comes to features such as security and compliance, Gordon said.

Faros CE je BI, API in avtomatizacijski sloj za vse inženirske operativne podatke, vključno z nadzorom vira, upravljanjem opravil, upravljanjem incidentov in podatki CI/CD. Sestavlja najboljšo odprtokodno programsko opremo: Airbyte za vnos podatkov, Hasura za plast API, Metabase za BI in n8n za avtomatizacijo. Faros CE temelji na vsebnikih in lahko deluje v katerem koli okolju, vključno z javnim oblakom, brez zunanjih odvisnosti.

Faros AI Enterprise, ki je na voljo kot SaaS z možnostmi samostojnega gostovanja, bo še naprej gonilna sila monetizacije za Faros AI. Vendar pa bo Faros CE služil tudi cilju, da strankam omogoči stvari, kot je dodajanje več konektorjev svojim sistemom po izbiri. Faros AI je deloval obratno, kot običajno počnejo podjetja, ki uporabljajo odprtokodne in poslovne različice, začenši z različico za podjetja in nato izdajo odprtokodno različico.

To se odraža tudi v načinu, kako se je podjetje odločilo za zbiranje sredstev, je dejal Gordon. Začetni krog v višini 16 milijonov dolarjev je prišel po tem, ko je podjetje že nekaj časa delovalo s popolnoma delujočo platformo in strankami, ki plačujejo. To, je dodal Gordon, pomeni, da ustanovitelji čim bolj zmanjšajo redčenje svojih delnic, podporniki pa zmanjšajo svoje tveganje. Financiranje bo porabljeno za naložbe v izdelek in za rast ekipe Faros AI.